diff --git "a/q-iql-models/antmaze-large-diverse-v0/1/debug.log" "b/q-iql-models/antmaze-large-diverse-v0/1/debug.log" new file mode 100644--- /dev/null +++ "b/q-iql-models/antmaze-large-diverse-v0/1/debug.log" @@ -0,0 +1,100000 @@ +2022-05-15 18:03:35.038415 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -1000 finished +------------------------------ ---------------- +epoch -1000 +replay_buffer/size 999047 +trainer/num train calls 1000 +trainer/QF1 Loss 0.992996 +trainer/QF2 Loss 0.977933 +trainer/Policy Loss 1032.97 +trainer/Q1 Predictions Mean -1.97178e-05 +trainer/Q1 Predictions Std 0.00197258 +trainer/Q1 Predictions Max 0.00676722 +trainer/Q1 Predictions Min -0.00532919 +trainer/Q2 Predictions Mean -0.00769172 +trainer/Q2 Predictions Std 0.00396542 +trainer/Q2 Predictions Max 0.00135509 +trainer/Q2 Predictions Min -0.0173887 +trainer/Q Targets Mean -0.992618 +trainer/Q Targets Std 0.0881126 +trainer/Q Targets Max 0 +trainer/Q Targets Min -1.00789 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00157421 +trainer/policy/mean Std 0.00315786 +trainer/policy/mean Max 0.0124576 +trainer/policy/mean Min -0.00732035 +trainer/policy/std Mean 0.0497871 +trainer/policy/std Std 3.72529e-09 +trainer/policy/std Max 0.0497871 +trainer/policy/std Min 0.0497871 +trainer/Advantage Weights Mean 0.964147 +trainer/Advantage Weights Std 0.0273052 +trainer/Advantage Weights Max 1.06321 +trainer/Advantage Weights Min 0.889167 +trainer/Advantage Score Mean -0.00369119 +trainer/Advantage Score Std 0.00282904 +trainer/Advantage Score Max 0.00612885 +trainer/Advantage Score Min -0.0117471 +trainer/V1 Predictions Mean -0.000406459 +trainer/V1 Predictions Std 0.00238109 +trainer/V1 Predictions Max 0.00584654 +trainer/V1 Predictions Min -0.00624425 +trainer/VF Loss 2.48468e-06 +expl/num steps total 1000 +expl/num paths total 1 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.000463616 +expl/Actions Std 0.0497671 +expl/Actions Max 0.166236 +expl/Actions Min -0.173251 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 1000 +eval/num paths total 1 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 3.59032e-05 +eval/Actions Std 0.000163897 +eval/Actions Max 0.00147992 +eval/Actions Min -0.00118403 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94438e-06 +time/evaluation sampling (s) 8.41619 +time/exploration sampling (s) 4.13675 +time/logging (s) 0.0105012 +time/saving (s) 0.0296331 +time/training (s) 15.6202 +time/epoch (s) 28.2133 +time/total (s) 69.3642 +Epoch -1000 +------------------------------ ---------------- +2022-05-15 18:03:58.580339 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -999 finished +------------------------------ ---------------- +epoch -999 +replay_buffer/size 999047 +trainer/num train calls 2000 +trainer/QF1 Loss 0.272961 +trainer/QF2 Loss 0.284574 +trainer/Policy Loss 124.371 +trainer/Q1 Predictions Mean -7.62639 +trainer/Q1 Predictions Std 0.79082 +trainer/Q1 Predictions Max -5.10665 +trainer/Q1 Predictions Min -9.66313 +trainer/Q2 Predictions Mean -7.635 +trainer/Q2 Predictions Std 0.803315 +trainer/Q2 Predictions Max -4.84956 +trainer/Q2 Predictions Min -9.63011 +trainer/Q Targets Mean -7.51781 +trainer/Q Targets Std 0.844487 +trainer/Q Targets Max 0 +trainer/Q Targets Min -9.34373 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0217909 +trainer/policy/mean Std 0.539247 +trainer/policy/mean Max 0.977916 +trainer/policy/mean Min -0.977631 +trainer/policy/std Mean 0.0670152 +trainer/policy/std Std 0.00117693 +trainer/policy/std Max 0.0682754 +trainer/policy/std Min 0.0647906 +trainer/Advantage Weights Mean 0.479681 +trainer/Advantage Weights Std 1.02869 +trainer/Advantage Weights Max 10.151 +trainer/Advantage Weights Min 0.000260046 +trainer/Advantage Score Mean -0.223505 +trainer/Advantage Score Std 0.193393 +trainer/Advantage Score Max 0.231758 +trainer/Advantage Score Min -0.825465 +trainer/V1 Predictions Mean -6.61781 +trainer/V1 Predictions Std 0.730713 +trainer/V1 Predictions Max -3.89503 +trainer/V1 Predictions Min -8.83847 +trainer/VF Loss 0.00961119 +expl/num steps total 2000 +expl/num paths total 2 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00399121 +expl/Actions Std 0.304922 +expl/Actions Max 0.899977 +expl/Actions Min -1.02014 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 2000 +eval/num paths total 2 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.034021 +eval/Actions Std 0.306928 +eval/Actions Max 0.89096 +eval/Actions Min -0.861672 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.95114e-06 +time/evaluation sampling (s) 3.75681 +time/exploration sampling (s) 4.16468 +time/logging (s) 0.00897357 +time/saving (s) 0.012718 +time/training (s) 15.5887 +time/epoch (s) 23.5319 +time/total (s) 92.9032 +Epoch -999 +------------------------------ ---------------- +2022-05-15 18:04:21.428054 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -998 finished +------------------------------ ---------------- +epoch -998 +replay_buffer/size 999047 +trainer/num train calls 3000 +trainer/QF1 Loss 0.53533 +trainer/QF2 Loss 0.520521 +trainer/Policy Loss 133.484 +trainer/Q1 Predictions Mean -13.8032 +trainer/Q1 Predictions Std 1.76875 +trainer/Q1 Predictions Max -7.84481 +trainer/Q1 Predictions Min -17.9564 +trainer/Q2 Predictions Mean -13.827 +trainer/Q2 Predictions Std 1.74759 +trainer/Q2 Predictions Max -7.83631 +trainer/Q2 Predictions Min -17.8066 +trainer/Q Targets Mean -13.8352 +trainer/Q Targets Std 1.98044 +trainer/Q Targets Max 0 +trainer/Q Targets Min -18.2208 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0193799 +trainer/policy/mean Std 0.561974 +trainer/policy/mean Max 0.960062 +trainer/policy/mean Min -0.976846 +trainer/policy/std Mean 0.0954598 +trainer/policy/std Std 0.00405439 +trainer/policy/std Max 0.0991301 +trainer/policy/std Min 0.0884005 +trainer/Advantage Weights Mean 2.00015 +trainer/Advantage Weights Std 10.086 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.228e-08 +trainer/Advantage Score Mean -0.321511 +trainer/Advantage Score Std 0.367098 +trainer/Advantage Score Max 0.554962 +trainer/Advantage Score Min -1.82153 +trainer/V1 Predictions Mean -13.0082 +trainer/V1 Predictions Std 1.74244 +trainer/V1 Predictions Max -7.91368 +trainer/V1 Predictions Min -16.6169 +trainer/VF Loss 0.0290116 +expl/num steps total 3000 +expl/num paths total 3 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0277259 +expl/Actions Std 0.29142 +expl/Actions Max 0.745709 +expl/Actions Min -0.880016 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 3000 +eval/num paths total 3 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00574273 +eval/Actions Std 0.274128 +eval/Actions Max 0.859863 +eval/Actions Min -0.849973 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.00099e-06 +time/evaluation sampling (s) 3.47231 +time/exploration sampling (s) 3.94292 +time/logging (s) 0.00699897 +time/saving (s) 0.0102859 +time/training (s) 15.4072 +time/epoch (s) 22.8397 +time/total (s) 115.748 +Epoch -998 +------------------------------ ---------------- +2022-05-15 18:04:44.183527 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -997 finished +------------------------------ ---------------- +epoch -997 +replay_buffer/size 999047 +trainer/num train calls 4000 +trainer/QF1 Loss 0.395088 +trainer/QF2 Loss 0.429781 +trainer/Policy Loss 276.984 +trainer/Q1 Predictions Mean -18.6288 +trainer/Q1 Predictions Std 3.53769 +trainer/Q1 Predictions Max -7.87208 +trainer/Q1 Predictions Min -25.0417 +trainer/Q2 Predictions Mean -18.5986 +trainer/Q2 Predictions Std 3.51602 +trainer/Q2 Predictions Max -7.83762 +trainer/Q2 Predictions Min -25.1374 +trainer/Q Targets Mean -18.9817 +trainer/Q Targets Std 3.37496 +trainer/Q Targets Max -8.92104 +trainer/Q Targets Min -25.4828 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00787378 +trainer/policy/mean Std 0.574778 +trainer/policy/mean Max 0.967662 +trainer/policy/mean Min -0.96305 +trainer/policy/std Mean 0.12812 +trainer/policy/std Std 0.00566652 +trainer/policy/std Max 0.133375 +trainer/policy/std Min 0.117333 +trainer/Advantage Weights Mean 5.44957 +trainer/Advantage Weights Std 18.011 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.11205e-07 +trainer/Advantage Score Mean -0.268495 +trainer/Advantage Score Std 0.416346 +trainer/Advantage Score Max 0.609111 +trainer/Advantage Score Min -1.49828 +trainer/V1 Predictions Mean -18.147 +trainer/V1 Predictions Std 3.4 +trainer/V1 Predictions Max -7.80003 +trainer/V1 Predictions Min -24.6378 +trainer/VF Loss 0.0388556 +expl/num steps total 4000 +expl/num paths total 4 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0173805 +expl/Actions Std 0.311953 +expl/Actions Max 0.84486 +expl/Actions Min -1.04494 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 4000 +eval/num paths total 4 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0236756 +eval/Actions Std 0.281842 +eval/Actions Max 0.705112 +eval/Actions Min -0.787258 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.98722e-06 +time/evaluation sampling (s) 3.19268 +time/exploration sampling (s) 3.95101 +time/logging (s) 0.0122467 +time/saving (s) 0.0137524 +time/training (s) 15.586 +time/epoch (s) 22.7557 +time/total (s) 138.508 +Epoch -997 +------------------------------ ---------------- +2022-05-15 18:05:06.720074 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -996 finished +------------------------------ ---------------- +epoch -996 +replay_buffer/size 999047 +trainer/num train calls 5000 +trainer/QF1 Loss 1.28667 +trainer/QF2 Loss 1.30294 +trainer/Policy Loss 141.882 +trainer/Q1 Predictions Mean -22.8372 +trainer/Q1 Predictions Std 5.20605 +trainer/Q1 Predictions Max -7.8394 +trainer/Q1 Predictions Min -34.1135 +trainer/Q2 Predictions Mean -22.9207 +trainer/Q2 Predictions Std 5.19218 +trainer/Q2 Predictions Max -7.84066 +trainer/Q2 Predictions Min -33.9821 +trainer/Q Targets Mean -22.9508 +trainer/Q Targets Std 5.15438 +trainer/Q Targets Max 0 +trainer/Q Targets Min -34.0636 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0146448 +trainer/policy/mean Std 0.550827 +trainer/policy/mean Max 0.946674 +trainer/policy/mean Min -0.982973 +trainer/policy/std Mean 0.160477 +trainer/policy/std Std 0.00662998 +trainer/policy/std Max 0.166804 +trainer/policy/std Min 0.148003 +trainer/Advantage Weights Mean 4.89942 +trainer/Advantage Weights Std 20.3291 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.46195e-10 +trainer/Advantage Score Mean -0.461501 +trainer/Advantage Score Std 0.503757 +trainer/Advantage Score Max 0.855321 +trainer/Advantage Score Min -2.1784 +trainer/V1 Predictions Mean -22.2146 +trainer/V1 Predictions Std 5.03258 +trainer/V1 Predictions Max -7.76026 +trainer/V1 Predictions Min -33.799 +trainer/VF Loss 0.0643809 +expl/num steps total 5000 +expl/num paths total 5 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0217153 +expl/Actions Std 0.313075 +expl/Actions Max 0.961858 +expl/Actions Min -0.983247 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 5000 +eval/num paths total 5 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0174484 +eval/Actions Std 0.27508 +eval/Actions Max 0.668584 +eval/Actions Min -0.595782 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.06841e-05 +time/evaluation sampling (s) 3.07627 +time/exploration sampling (s) 3.91708 +time/logging (s) 0.0119509 +time/saving (s) 0.0145402 +time/training (s) 15.5087 +time/epoch (s) 22.5286 +time/total (s) 161.043 +Epoch -996 +------------------------------ ---------------- +2022-05-15 18:05:29.516181 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -995 finished +------------------------------ ---------------- +epoch -995 +replay_buffer/size 999047 +trainer/num train calls 6000 +trainer/QF1 Loss 0.446781 +trainer/QF2 Loss 0.484704 +trainer/Policy Loss 157.185 +trainer/Q1 Predictions Mean -27.0394 +trainer/Q1 Predictions Std 6.40524 +trainer/Q1 Predictions Max -7.49226 +trainer/Q1 Predictions Min -39.8847 +trainer/Q2 Predictions Mean -27.0527 +trainer/Q2 Predictions Std 6.43299 +trainer/Q2 Predictions Max -7.53963 +trainer/Q2 Predictions Min -40.4539 +trainer/Q Targets Mean -27.1408 +trainer/Q Targets Std 6.26664 +trainer/Q Targets Max -8.0054 +trainer/Q Targets Min -40.3358 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0131221 +trainer/policy/mean Std 0.564313 +trainer/policy/mean Max 0.95603 +trainer/policy/mean Min -0.972144 +trainer/policy/std Mean 0.193308 +trainer/policy/std Std 0.00701298 +trainer/policy/std Max 0.200215 +trainer/policy/std Min 0.180598 +trainer/Advantage Weights Mean 5.0604 +trainer/Advantage Weights Std 19.068 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.93808e-12 +trainer/Advantage Score Mean -0.434504 +trainer/Advantage Score Std 0.548129 +trainer/Advantage Score Max 0.934259 +trainer/Advantage Score Min -2.65533 +trainer/V1 Predictions Mean -26.4148 +trainer/V1 Predictions Std 6.42006 +trainer/V1 Predictions Max -7.26444 +trainer/V1 Predictions Min -40.0477 +trainer/VF Loss 0.0669709 +expl/num steps total 6000 +expl/num paths total 6 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0303478 +expl/Actions Std 0.328998 +expl/Actions Max 1.04959 +expl/Actions Min -1.00394 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 6000 +eval/num paths total 6 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0138903 +eval/Actions Std 0.275522 +eval/Actions Max 0.590523 +eval/Actions Min -0.588972 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.13204e-06 +time/evaluation sampling (s) 3.18153 +time/exploration sampling (s) 4.04457 +time/logging (s) 0.00697611 +time/saving (s) 0.0124505 +time/training (s) 15.5377 +time/epoch (s) 22.7832 +time/total (s) 183.834 +Epoch -995 +------------------------------ ---------------- +2022-05-15 18:05:52.415263 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -994 finished +------------------------------ ---------------- +epoch -994 +replay_buffer/size 999047 +trainer/num train calls 7000 +trainer/QF1 Loss 0.5112 +trainer/QF2 Loss 0.556379 +trainer/Policy Loss 80.183 +trainer/Q1 Predictions Mean -30.435 +trainer/Q1 Predictions Std 7.96305 +trainer/Q1 Predictions Max -3.85516 +trainer/Q1 Predictions Min -42.5858 +trainer/Q2 Predictions Mean -30.4587 +trainer/Q2 Predictions Std 7.97778 +trainer/Q2 Predictions Max -3.9528 +trainer/Q2 Predictions Min -42.728 +trainer/Q Targets Mean -30.3972 +trainer/Q Targets Std 7.66997 +trainer/Q Targets Max -3.84968 +trainer/Q Targets Min -41.6444 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000390773 +trainer/policy/mean Std 0.55228 +trainer/policy/mean Max 0.957275 +trainer/policy/mean Min -0.968081 +trainer/policy/std Mean 0.226585 +trainer/policy/std Std 0.00723911 +trainer/policy/std Max 0.233995 +trainer/policy/std Min 0.214104 +trainer/Advantage Weights Mean 4.59291 +trainer/Advantage Weights Std 17.75 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.71427e-10 +trainer/Advantage Score Mean -0.378401 +trainer/Advantage Score Std 0.465421 +trainer/Advantage Score Max 1.09043 +trainer/Advantage Score Min -2.14753 +trainer/V1 Predictions Mean -29.5916 +trainer/V1 Predictions Std 7.7935 +trainer/V1 Predictions Max -3.46925 +trainer/V1 Predictions Min -41.0783 +trainer/VF Loss 0.0510098 +expl/num steps total 7000 +expl/num paths total 7 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0145098 +expl/Actions Std 0.383172 +expl/Actions Max 1.21702 +expl/Actions Min -1.34593 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 7000 +eval/num paths total 7 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00368026 +eval/Actions Std 0.269047 +eval/Actions Max 0.624942 +eval/Actions Min -0.636984 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83495e-06 +time/evaluation sampling (s) 3.52857 +time/exploration sampling (s) 4.35894 +time/logging (s) 0.0114417 +time/saving (s) 0.0160647 +time/training (s) 14.9832 +time/epoch (s) 22.8982 +time/total (s) 206.737 +Epoch -994 +------------------------------ ---------------- +2022-05-15 18:06:15.890678 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -993 finished +------------------------------ ---------------- +epoch -993 +replay_buffer/size 999047 +trainer/num train calls 8000 +trainer/QF1 Loss 1.30355 +trainer/QF2 Loss 1.37026 +trainer/Policy Loss 30.2469 +trainer/Q1 Predictions Mean -34.0865 +trainer/Q1 Predictions Std 8.45155 +trainer/Q1 Predictions Max -6.71995 +trainer/Q1 Predictions Min -45.2989 +trainer/Q2 Predictions Mean -34.1524 +trainer/Q2 Predictions Std 8.41324 +trainer/Q2 Predictions Max -6.62846 +trainer/Q2 Predictions Min -45.31 +trainer/Q Targets Mean -33.9419 +trainer/Q Targets Std 8.65893 +trainer/Q Targets Max 0 +trainer/Q Targets Min -45.7063 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0123227 +trainer/policy/mean Std 0.551775 +trainer/policy/mean Max 0.958741 +trainer/policy/mean Min -0.97874 +trainer/policy/std Mean 0.25975 +trainer/policy/std Std 0.0080638 +trainer/policy/std Max 0.267959 +trainer/policy/std Min 0.246359 +trainer/Advantage Weights Mean 2.12695 +trainer/Advantage Weights Std 11.9606 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.32697e-11 +trainer/Advantage Score Mean -0.505206 +trainer/Advantage Score Std 0.45681 +trainer/Advantage Score Max 1.17107 +trainer/Advantage Score Min -2.36557 +trainer/V1 Predictions Mean -33.349 +trainer/V1 Predictions Std 8.43223 +trainer/V1 Predictions Max -6.34851 +trainer/V1 Predictions Min -45.3146 +trainer/VF Loss 0.0547728 +expl/num steps total 8000 +expl/num paths total 8 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0255867 +expl/Actions Std 0.382457 +expl/Actions Max 1.3859 +expl/Actions Min -1.15659 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 8000 +eval/num paths total 8 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0967519 +eval/Actions Std 0.271345 +eval/Actions Max 0.545191 +eval/Actions Min -0.578175 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.73879e-06 +time/evaluation sampling (s) 3.52076 +time/exploration sampling (s) 4.53325 +time/logging (s) 0.0110441 +time/saving (s) 0.0157706 +time/training (s) 15.3863 +time/epoch (s) 23.4672 +time/total (s) 230.211 +Epoch -993 +------------------------------ ---------------- +2022-05-15 18:06:38.450976 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -992 finished +------------------------------ ---------------- +epoch -992 +replay_buffer/size 999047 +trainer/num train calls 9000 +trainer/QF1 Loss 1.14956 +trainer/QF2 Loss 1.14445 +trainer/Policy Loss 45.2863 +trainer/Q1 Predictions Mean -37.218 +trainer/Q1 Predictions Std 9.24657 +trainer/Q1 Predictions Max -1.1357 +trainer/Q1 Predictions Min -51.5108 +trainer/Q2 Predictions Mean -37.2325 +trainer/Q2 Predictions Std 9.24753 +trainer/Q2 Predictions Max -1.12903 +trainer/Q2 Predictions Min -51.2642 +trainer/Q Targets Mean -36.9516 +trainer/Q Targets Std 9.49419 +trainer/Q Targets Max 0 +trainer/Q Targets Min -51.3627 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0135092 +trainer/policy/mean Std 0.559069 +trainer/policy/mean Max 0.951789 +trainer/policy/mean Min -0.978916 +trainer/policy/std Mean 0.293115 +trainer/policy/std Std 0.00859062 +trainer/policy/std Max 0.302512 +trainer/policy/std Min 0.279962 +trainer/Advantage Weights Mean 4.77901 +trainer/Advantage Weights Std 18.1172 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.36011e-13 +trainer/Advantage Score Mean -0.40781 +trainer/Advantage Score Std 0.487785 +trainer/Advantage Score Max 1.30124 +trainer/Advantage Score Min -2.78101 +trainer/V1 Predictions Mean -36.413 +trainer/V1 Predictions Std 9.18273 +trainer/V1 Predictions Max -1.08741 +trainer/V1 Predictions Min -50.8704 +trainer/VF Loss 0.0606154 +expl/num steps total 9000 +expl/num paths total 9 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00229773 +expl/Actions Std 0.421056 +expl/Actions Max 1.43691 +expl/Actions Min -1.53559 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 9000 +eval/num paths total 9 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0448861 +eval/Actions Std 0.236755 +eval/Actions Max 0.751198 +eval/Actions Min -0.696594 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.46778e-06 +time/evaluation sampling (s) 3.30733 +time/exploration sampling (s) 4.05577 +time/logging (s) 0.00953663 +time/saving (s) 0.0142925 +time/training (s) 15.1646 +time/epoch (s) 22.5515 +time/total (s) 252.769 +Epoch -992 +------------------------------ ---------------- +2022-05-15 18:07:00.275987 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -991 finished +------------------------------ ---------------- +epoch -991 +replay_buffer/size 999047 +trainer/num train calls 10000 +trainer/QF1 Loss 1.11 +trainer/QF2 Loss 1.20196 +trainer/Policy Loss 71.6004 +trainer/Q1 Predictions Mean -39.5119 +trainer/Q1 Predictions Std 10.4688 +trainer/Q1 Predictions Max -6.36983 +trainer/Q1 Predictions Min -53.6232 +trainer/Q2 Predictions Mean -39.5291 +trainer/Q2 Predictions Std 10.4264 +trainer/Q2 Predictions Max -6.60648 +trainer/Q2 Predictions Min -53.3179 +trainer/Q Targets Mean -39.6652 +trainer/Q Targets Std 10.6191 +trainer/Q Targets Max 0 +trainer/Q Targets Min -53.8885 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0137304 +trainer/policy/mean Std 0.572474 +trainer/policy/mean Max 0.966381 +trainer/policy/mean Min -0.978554 +trainer/policy/std Mean 0.327573 +trainer/policy/std Std 0.00936531 +trainer/policy/std Max 0.338022 +trainer/policy/std Min 0.314379 +trainer/Advantage Weights Mean 8.20445 +trainer/Advantage Weights Std 23.9122 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.50039e-11 +trainer/Advantage Score Mean -0.24683 +trainer/Advantage Score Std 0.499715 +trainer/Advantage Score Max 1.28281 +trainer/Advantage Score Min -2.38243 +trainer/V1 Predictions Mean -39.2073 +trainer/V1 Predictions Std 10.4663 +trainer/V1 Predictions Max -6.25585 +trainer/V1 Predictions Min -53.5669 +trainer/VF Loss 0.0655034 +expl/num steps total 10000 +expl/num paths total 10 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0123271 +expl/Actions Std 0.442558 +expl/Actions Max 1.6674 +expl/Actions Min -1.4531 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 10000 +eval/num paths total 10 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0701219 +eval/Actions Std 0.274018 +eval/Actions Max 0.691367 +eval/Actions Min -0.636125 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.24706e-06 +time/evaluation sampling (s) 3.2813 +time/exploration sampling (s) 4.13816 +time/logging (s) 0.00987382 +time/saving (s) 0.0144928 +time/training (s) 14.3733 +time/epoch (s) 21.8171 +time/total (s) 274.593 +Epoch -991 +------------------------------ ---------------- +2022-05-15 18:07:23.616490 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -990 finished +------------------------------ ---------------- +epoch -990 +replay_buffer/size 999047 +trainer/num train calls 11000 +trainer/QF1 Loss 0.66588 +trainer/QF2 Loss 0.718439 +trainer/Policy Loss 28.6578 +trainer/Q1 Predictions Mean -42.517 +trainer/Q1 Predictions Std 11.8728 +trainer/Q1 Predictions Max -1.14736 +trainer/Q1 Predictions Min -59.6464 +trainer/Q2 Predictions Mean -42.5913 +trainer/Q2 Predictions Std 11.8594 +trainer/Q2 Predictions Max -1.18661 +trainer/Q2 Predictions Min -59.9282 +trainer/Q Targets Mean -42.4579 +trainer/Q Targets Std 11.9001 +trainer/Q Targets Max 0 +trainer/Q Targets Min -58.8302 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00563809 +trainer/policy/mean Std 0.573784 +trainer/policy/mean Max 0.965416 +trainer/policy/mean Min -0.978102 +trainer/policy/std Mean 0.360058 +trainer/policy/std Std 0.0109987 +trainer/policy/std Max 0.372658 +trainer/policy/std Min 0.345118 +trainer/Advantage Weights Mean 3.03974 +trainer/Advantage Weights Std 14.345 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.29038e-09 +trainer/Advantage Score Mean -0.439062 +trainer/Advantage Score Std 0.447346 +trainer/Advantage Score Max 1.27195 +trainer/Advantage Score Min -1.86082 +trainer/V1 Predictions Mean -41.9372 +trainer/V1 Predictions Std 11.8981 +trainer/V1 Predictions Max -0.892375 +trainer/V1 Predictions Min -58.5074 +trainer/VF Loss 0.0514477 +expl/num steps total 11000 +expl/num paths total 11 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0280861 +expl/Actions Std 0.502139 +expl/Actions Max 1.96942 +expl/Actions Min -1.89606 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 11000 +eval/num paths total 11 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0707428 +eval/Actions Std 0.273138 +eval/Actions Max 0.725374 +eval/Actions Min -0.626186 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.74299e-06 +time/evaluation sampling (s) 3.63213 +time/exploration sampling (s) 4.23279 +time/logging (s) 0.0114566 +time/saving (s) 0.0150404 +time/training (s) 15.4437 +time/epoch (s) 23.3351 +time/total (s) 297.934 +Epoch -990 +------------------------------ ---------------- +2022-05-15 18:07:46.777120 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -989 finished +------------------------------ ---------------- +epoch -989 +replay_buffer/size 999047 +trainer/num train calls 12000 +trainer/QF1 Loss 0.895828 +trainer/QF2 Loss 0.752984 +trainer/Policy Loss 55.0794 +trainer/Q1 Predictions Mean -44.9346 +trainer/Q1 Predictions Std 12.1063 +trainer/Q1 Predictions Max -2.98031 +trainer/Q1 Predictions Min -62.1778 +trainer/Q2 Predictions Mean -45.0629 +trainer/Q2 Predictions Std 12.0901 +trainer/Q2 Predictions Max -2.79143 +trainer/Q2 Predictions Min -62.126 +trainer/Q Targets Mean -45.4512 +trainer/Q Targets Std 12.1472 +trainer/Q Targets Max 0 +trainer/Q Targets Min -62.1865 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00430544 +trainer/policy/mean Std 0.576756 +trainer/policy/mean Max 0.970038 +trainer/policy/mean Min -0.973006 +trainer/policy/std Mean 0.391937 +trainer/policy/std Std 0.0129086 +trainer/policy/std Max 0.406173 +trainer/policy/std Min 0.374441 +trainer/Advantage Weights Mean 8.79215 +trainer/Advantage Weights Std 23.4904 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.96564e-07 +trainer/Advantage Score Mean -0.201027 +trainer/Advantage Score Std 0.430942 +trainer/Advantage Score Max 1.0673 +trainer/Advantage Score Min -1.54423 +trainer/V1 Predictions Mean -44.9469 +trainer/V1 Predictions Std 12.1007 +trainer/V1 Predictions Max -3.31626 +trainer/V1 Predictions Min -61.6746 +trainer/VF Loss 0.0520554 +expl/num steps total 12000 +expl/num paths total 12 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0165088 +expl/Actions Std 0.531475 +expl/Actions Max 2.42031 +expl/Actions Min -1.8866 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 12000 +eval/num paths total 12 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0497373 +eval/Actions Std 0.283239 +eval/Actions Max 0.85778 +eval/Actions Min -0.928009 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.10596e-06 +time/evaluation sampling (s) 3.57428 +time/exploration sampling (s) 4.33286 +time/logging (s) 0.0104171 +time/saving (s) 0.0146682 +time/training (s) 15.2197 +time/epoch (s) 23.152 +time/total (s) 321.093 +Epoch -989 +------------------------------ ---------------- +2022-05-15 18:08:09.714094 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -988 finished +------------------------------ ---------------- +epoch -988 +replay_buffer/size 999047 +trainer/num train calls 13000 +trainer/QF1 Loss 1.96713 +trainer/QF2 Loss 2.06522 +trainer/Policy Loss 13.5603 +trainer/Q1 Predictions Mean -48.5678 +trainer/Q1 Predictions Std 11.0657 +trainer/Q1 Predictions Max -6.4665 +trainer/Q1 Predictions Min -71.0382 +trainer/Q2 Predictions Mean -48.6067 +trainer/Q2 Predictions Std 11.0284 +trainer/Q2 Predictions Max -6.19376 +trainer/Q2 Predictions Min -71.0435 +trainer/Q Targets Mean -48.1376 +trainer/Q Targets Std 11.0371 +trainer/Q Targets Max -6.97257 +trainer/Q Targets Min -70.6385 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0312776 +trainer/policy/mean Std 0.572724 +trainer/policy/mean Max 0.979471 +trainer/policy/mean Min -0.983253 +trainer/policy/std Mean 0.422941 +trainer/policy/std Std 0.0155389 +trainer/policy/std Max 0.440319 +trainer/policy/std Min 0.403369 +trainer/Advantage Weights Mean 2.25546 +trainer/Advantage Weights Std 12.7126 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.78429e-13 +trainer/Advantage Score Mean -0.535278 +trainer/Advantage Score Std 0.479451 +trainer/Advantage Score Max 0.719843 +trainer/Advantage Score Min -2.86027 +trainer/V1 Predictions Mean -47.6364 +trainer/V1 Predictions Std 11.1613 +trainer/V1 Predictions Max -5.16653 +trainer/V1 Predictions Min -69.8892 +trainer/VF Loss 0.0581445 +expl/num steps total 13000 +expl/num paths total 13 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.053888 +expl/Actions Std 0.550208 +expl/Actions Max 1.8121 +expl/Actions Min -2.04216 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 13000 +eval/num paths total 13 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.105709 +eval/Actions Std 0.285037 +eval/Actions Max 0.717001 +eval/Actions Min -0.708774 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.21982e-06 +time/evaluation sampling (s) 3.44611 +time/exploration sampling (s) 4.26498 +time/logging (s) 0.0079907 +time/saving (s) 0.0105731 +time/training (s) 15.1975 +time/epoch (s) 22.9271 +time/total (s) 344.026 +Epoch -988 +------------------------------ ---------------- +2022-05-15 18:08:32.903509 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -987 finished +------------------------------ ---------------- +epoch -987 +replay_buffer/size 999047 +trainer/num train calls 14000 +trainer/QF1 Loss 1.14825 +trainer/QF2 Loss 1.18523 +trainer/Policy Loss 25.9563 +trainer/Q1 Predictions Mean -48.406 +trainer/Q1 Predictions Std 14.1423 +trainer/Q1 Predictions Max -4.9167 +trainer/Q1 Predictions Min -68.89 +trainer/Q2 Predictions Mean -48.3382 +trainer/Q2 Predictions Std 14.1305 +trainer/Q2 Predictions Max -4.54441 +trainer/Q2 Predictions Min -69.022 +trainer/Q Targets Mean -48.2174 +trainer/Q Targets Std 14.3378 +trainer/Q Targets Max 0 +trainer/Q Targets Min -68.1706 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00944145 +trainer/policy/mean Std 0.593398 +trainer/policy/mean Max 0.971762 +trainer/policy/mean Min -0.981706 +trainer/policy/std Mean 0.449336 +trainer/policy/std Std 0.0175978 +trainer/policy/std Max 0.467782 +trainer/policy/std Min 0.425572 +trainer/Advantage Weights Mean 4.63058 +trainer/Advantage Weights Std 19.0012 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.43401e-13 +trainer/Advantage Score Mean -0.529911 +trainer/Advantage Score Std 0.515879 +trainer/Advantage Score Max 0.992657 +trainer/Advantage Score Min -2.76893 +trainer/V1 Predictions Mean -47.8023 +trainer/V1 Predictions Std 14.2263 +trainer/V1 Predictions Max -5.20295 +trainer/V1 Predictions Min -68.5936 +trainer/VF Loss 0.0705123 +expl/num steps total 14000 +expl/num paths total 14 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.002559 +expl/Actions Std 0.597905 +expl/Actions Max 2.16106 +expl/Actions Min -2.16248 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 14000 +eval/num paths total 14 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0630761 +eval/Actions Std 0.297242 +eval/Actions Max 0.739967 +eval/Actions Min -0.759186 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 7.92183e-06 +time/evaluation sampling (s) 3.52664 +time/exploration sampling (s) 4.11077 +time/logging (s) 0.0120843 +time/saving (s) 0.018596 +time/training (s) 15.5197 +time/epoch (s) 23.1878 +time/total (s) 367.219 +Epoch -987 +------------------------------ ---------------- +2022-05-15 18:08:55.192565 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -986 finished +------------------------------ ---------------- +epoch -986 +replay_buffer/size 999047 +trainer/num train calls 15000 +trainer/QF1 Loss 1.13948 +trainer/QF2 Loss 1.07841 +trainer/Policy Loss 13.8166 +trainer/Q1 Predictions Mean -50.8316 +trainer/Q1 Predictions Std 14.327 +trainer/Q1 Predictions Max -4.178 +trainer/Q1 Predictions Min -67.7031 +trainer/Q2 Predictions Mean -50.8085 +trainer/Q2 Predictions Std 14.3226 +trainer/Q2 Predictions Max -4.18286 +trainer/Q2 Predictions Min -67.8716 +trainer/Q Targets Mean -50.4881 +trainer/Q Targets Std 14.5787 +trainer/Q Targets Max 0 +trainer/Q Targets Min -67.4667 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00398668 +trainer/policy/mean Std 0.589753 +trainer/policy/mean Max 0.979345 +trainer/policy/mean Min -0.989594 +trainer/policy/std Mean 0.471032 +trainer/policy/std Std 0.0209515 +trainer/policy/std Max 0.493252 +trainer/policy/std Min 0.443899 +trainer/Advantage Weights Mean 2.45191 +trainer/Advantage Weights Std 13.2317 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.53927e-14 +trainer/Advantage Score Mean -0.631554 +trainer/Advantage Score Std 0.534587 +trainer/Advantage Score Max 0.700058 +trainer/Advantage Score Min -3.09723 +trainer/V1 Predictions Mean -50.0458 +trainer/V1 Predictions Std 14.5117 +trainer/V1 Predictions Max -3.32904 +trainer/V1 Predictions Min -67.14 +trainer/VF Loss 0.0753073 +expl/num steps total 15000 +expl/num paths total 15 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00136835 +expl/Actions Std 0.617806 +expl/Actions Max 1.89512 +expl/Actions Min -2.11714 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 15000 +eval/num paths total 15 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.106879 +eval/Actions Std 0.4156 +eval/Actions Max 0.742081 +eval/Actions Min -0.680699 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.73809e-06 +time/evaluation sampling (s) 3.1736 +time/exploration sampling (s) 4.04374 +time/logging (s) 0.00665521 +time/saving (s) 0.00980673 +time/training (s) 15.0418 +time/epoch (s) 22.2756 +time/total (s) 389.501 +Epoch -986 +------------------------------ ---------------- +2022-05-15 18:09:17.282231 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -985 finished +------------------------------ ---------------- +epoch -985 +replay_buffer/size 999047 +trainer/num train calls 16000 +trainer/QF1 Loss 0.630324 +trainer/QF2 Loss 0.707812 +trainer/Policy Loss 38.7578 +trainer/Q1 Predictions Mean -52.1081 +trainer/Q1 Predictions Std 14.9774 +trainer/Q1 Predictions Max -4.72258 +trainer/Q1 Predictions Min -74.9146 +trainer/Q2 Predictions Mean -52.2894 +trainer/Q2 Predictions Std 14.9629 +trainer/Q2 Predictions Max -4.29508 +trainer/Q2 Predictions Min -75.6266 +trainer/Q Targets Mean -52.3346 +trainer/Q Targets Std 14.9554 +trainer/Q Targets Max -4.66475 +trainer/Q Targets Min -74.7264 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0220346 +trainer/policy/mean Std 0.599122 +trainer/policy/mean Max 0.986299 +trainer/policy/mean Min -0.988729 +trainer/policy/std Mean 0.487272 +trainer/policy/std Std 0.0245953 +trainer/policy/std Max 0.513833 +trainer/policy/std Min 0.455705 +trainer/Advantage Weights Mean 6.13535 +trainer/Advantage Weights Std 20.4637 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.21599e-11 +trainer/Advantage Score Mean -0.338171 +trainer/Advantage Score Std 0.488154 +trainer/Advantage Score Max 0.972909 +trainer/Advantage Score Min -2.45327 +trainer/V1 Predictions Mean -51.8218 +trainer/V1 Predictions Std 15.1571 +trainer/V1 Predictions Max -4.21245 +trainer/V1 Predictions Min -74.5636 +trainer/VF Loss 0.0553398 +expl/num steps total 16000 +expl/num paths total 16 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00156073 +expl/Actions Std 0.646162 +expl/Actions Max 2.22428 +expl/Actions Min -2.29463 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 16000 +eval/num paths total 16 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.15361 +eval/Actions Std 0.377448 +eval/Actions Max 0.784662 +eval/Actions Min -0.696196 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.45102e-06 +time/evaluation sampling (s) 3.09654 +time/exploration sampling (s) 3.94955 +time/logging (s) 0.010781 +time/saving (s) 0.015238 +time/training (s) 15.0156 +time/epoch (s) 22.0877 +time/total (s) 411.594 +Epoch -985 +------------------------------ ---------------- +2022-05-15 18:09:39.809240 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -984 finished +------------------------------ ---------------- +epoch -984 +replay_buffer/size 999047 +trainer/num train calls 17000 +trainer/QF1 Loss 1.65681 +trainer/QF2 Loss 1.42612 +trainer/Policy Loss 53.729 +trainer/Q1 Predictions Mean -53.0819 +trainer/Q1 Predictions Std 14.8175 +trainer/Q1 Predictions Max -3.33244 +trainer/Q1 Predictions Min -74.9069 +trainer/Q2 Predictions Mean -53.2859 +trainer/Q2 Predictions Std 14.7737 +trainer/Q2 Predictions Max -3.00412 +trainer/Q2 Predictions Min -75.8211 +trainer/Q Targets Mean -53.6373 +trainer/Q Targets Std 14.9402 +trainer/Q Targets Max 0 +trainer/Q Targets Min -74.4476 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00877161 +trainer/policy/mean Std 0.606097 +trainer/policy/mean Max 0.971972 +trainer/policy/mean Min -0.992611 +trainer/policy/std Mean 0.498715 +trainer/policy/std Std 0.0285181 +trainer/policy/std Max 0.529448 +trainer/policy/std Min 0.458749 +trainer/Advantage Weights Mean 10.1431 +trainer/Advantage Weights Std 26.5693 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.86607e-13 +trainer/Advantage Score Mean -0.295933 +trainer/Advantage Score Std 0.579007 +trainer/Advantage Score Max 1.26754 +trainer/Advantage Score Min -2.76445 +trainer/V1 Predictions Mean -53.2401 +trainer/V1 Predictions Std 14.7805 +trainer/V1 Predictions Max -4.35994 +trainer/V1 Predictions Min -74.5791 +trainer/VF Loss 0.0861962 +expl/num steps total 17000 +expl/num paths total 17 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0206022 +expl/Actions Std 0.670196 +expl/Actions Max 2.53788 +expl/Actions Min -2.56846 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 17000 +eval/num paths total 17 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.127224 +eval/Actions Std 0.439341 +eval/Actions Max 0.706508 +eval/Actions Min -0.750422 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 7.79424e-06 +time/evaluation sampling (s) 3.11362 +time/exploration sampling (s) 4.17835 +time/logging (s) 0.0124281 +time/saving (s) 0.0125762 +time/training (s) 15.2046 +time/epoch (s) 22.5216 +time/total (s) 434.122 +Epoch -984 +------------------------------ ---------------- +2022-05-15 18:10:02.146097 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -983 finished +------------------------------ ---------------- +epoch -983 +replay_buffer/size 999047 +trainer/num train calls 18000 +trainer/QF1 Loss 0.874902 +trainer/QF2 Loss 0.89768 +trainer/Policy Loss 22.5213 +trainer/Q1 Predictions Mean -53.0673 +trainer/Q1 Predictions Std 18.1113 +trainer/Q1 Predictions Max -3.38528 +trainer/Q1 Predictions Min -76.9945 +trainer/Q2 Predictions Mean -53.0857 +trainer/Q2 Predictions Std 18.1501 +trainer/Q2 Predictions Max -3.3864 +trainer/Q2 Predictions Min -77.1764 +trainer/Q Targets Mean -52.9858 +trainer/Q Targets Std 17.9273 +trainer/Q Targets Max 0 +trainer/Q Targets Min -77.0875 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00352856 +trainer/policy/mean Std 0.607607 +trainer/policy/mean Max 0.987366 +trainer/policy/mean Min -0.979072 +trainer/policy/std Mean 0.505989 +trainer/policy/std Std 0.0301625 +trainer/policy/std Max 0.538884 +trainer/policy/std Min 0.465622 +trainer/Advantage Weights Mean 4.00089 +trainer/Advantage Weights Std 16.9656 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.28348e-14 +trainer/Advantage Score Mean -0.4765 +trainer/Advantage Score Std 0.554503 +trainer/Advantage Score Max 1.58562 +trainer/Advantage Score Min -3.19866 +trainer/V1 Predictions Mean -52.5138 +trainer/V1 Predictions Std 17.986 +trainer/V1 Predictions Max -2.88775 +trainer/V1 Predictions Min -76.4929 +trainer/VF Loss 0.0757191 +expl/num steps total 18000 +expl/num paths total 18 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0102428 +expl/Actions Std 0.708891 +expl/Actions Max 2.32132 +expl/Actions Min -2.5097 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 18000 +eval/num paths total 18 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.121989 +eval/Actions Std 0.394166 +eval/Actions Max 0.768149 +eval/Actions Min -0.786976 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.98675e-06 +time/evaluation sampling (s) 3.09026 +time/exploration sampling (s) 4.03891 +time/logging (s) 0.00960758 +time/saving (s) 0.0133861 +time/training (s) 15.1731 +time/epoch (s) 22.3253 +time/total (s) 456.455 +Epoch -983 +------------------------------ ---------------- +2022-05-15 18:10:24.786769 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -982 finished +------------------------------ ---------------- +epoch -982 +replay_buffer/size 999047 +trainer/num train calls 19000 +trainer/QF1 Loss 0.582232 +trainer/QF2 Loss 0.579984 +trainer/Policy Loss 32.3239 +trainer/Q1 Predictions Mean -55.9215 +trainer/Q1 Predictions Std 16.3864 +trainer/Q1 Predictions Max -2.8434 +trainer/Q1 Predictions Min -73.3764 +trainer/Q2 Predictions Mean -55.9183 +trainer/Q2 Predictions Std 16.4035 +trainer/Q2 Predictions Max -2.65476 +trainer/Q2 Predictions Min -73.8556 +trainer/Q Targets Mean -55.8099 +trainer/Q Targets Std 16.4471 +trainer/Q Targets Max 0 +trainer/Q Targets Min -73.3909 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0125601 +trainer/policy/mean Std 0.62533 +trainer/policy/mean Max 0.988001 +trainer/policy/mean Min -0.992726 +trainer/policy/std Mean 0.508811 +trainer/policy/std Std 0.030968 +trainer/policy/std Max 0.544344 +trainer/policy/std Min 0.467316 +trainer/Advantage Weights Mean 4.67106 +trainer/Advantage Weights Std 17.3374 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.50537e-11 +trainer/Advantage Score Mean -0.339244 +trainer/Advantage Score Std 0.489607 +trainer/Advantage Score Max 1.59661 +trainer/Advantage Score Min -2.49194 +trainer/V1 Predictions Mean -55.3912 +trainer/V1 Predictions Std 16.4793 +trainer/V1 Predictions Max -2.6831 +trainer/V1 Predictions Min -73.0721 +trainer/VF Loss 0.0571095 +expl/num steps total 19000 +expl/num paths total 19 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.013425 +expl/Actions Std 0.683558 +expl/Actions Max 2.5692 +expl/Actions Min -2.90834 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 19000 +eval/num paths total 19 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.169014 +eval/Actions Std 0.44163 +eval/Actions Max 0.796192 +eval/Actions Min -0.826277 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.7339e-06 +time/evaluation sampling (s) 3.09573 +time/exploration sampling (s) 4.16514 +time/logging (s) 0.00669401 +time/saving (s) 0.0153461 +time/training (s) 15.347 +time/epoch (s) 22.6299 +time/total (s) 479.091 +Epoch -982 +------------------------------ ---------------- +2022-05-15 18:10:47.402816 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -981 finished +------------------------------ ---------------- +epoch -981 +replay_buffer/size 999047 +trainer/num train calls 20000 +trainer/QF1 Loss 1.07317 +trainer/QF2 Loss 0.996965 +trainer/Policy Loss 4.44276 +trainer/Q1 Predictions Mean -57.7327 +trainer/Q1 Predictions Std 16.1175 +trainer/Q1 Predictions Max -4.7154 +trainer/Q1 Predictions Min -77.7232 +trainer/Q2 Predictions Mean -57.7019 +trainer/Q2 Predictions Std 16.1244 +trainer/Q2 Predictions Max -4.38273 +trainer/Q2 Predictions Min -77.7383 +trainer/Q Targets Mean -57.159 +trainer/Q Targets Std 16.1955 +trainer/Q Targets Max 0 +trainer/Q Targets Min -76.3267 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0128795 +trainer/policy/mean Std 0.626543 +trainer/policy/mean Max 0.991976 +trainer/policy/mean Min -0.993846 +trainer/policy/std Mean 0.507313 +trainer/policy/std Std 0.0333638 +trainer/policy/std Max 0.543194 +trainer/policy/std Min 0.462124 +trainer/Advantage Weights Mean 0.70897 +trainer/Advantage Weights Std 4.55248 +trainer/Advantage Weights Max 63.1015 +trainer/Advantage Weights Min 4.69825e-12 +trainer/Advantage Score Mean -0.586698 +trainer/Advantage Score Std 0.481497 +trainer/Advantage Score Max 0.414475 +trainer/Advantage Score Min -2.60838 +trainer/V1 Predictions Mean -56.7211 +trainer/V1 Predictions Std 16.2971 +trainer/V1 Predictions Max -3.72247 +trainer/V1 Predictions Min -76.2987 +trainer/VF Loss 0.0595681 +expl/num steps total 20000 +expl/num paths total 20 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0138616 +expl/Actions Std 0.678919 +expl/Actions Max 2.72103 +expl/Actions Min -2.38919 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 20000 +eval/num paths total 20 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.100096 +eval/Actions Std 0.285407 +eval/Actions Max 0.940995 +eval/Actions Min -0.947291 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.71108e-06 +time/evaluation sampling (s) 3.10338 +time/exploration sampling (s) 4.13854 +time/logging (s) 0.00688977 +time/saving (s) 0.0110454 +time/training (s) 15.3512 +time/epoch (s) 22.611 +time/total (s) 501.706 +Epoch -981 +------------------------------ ---------------- +2022-05-15 18:11:10.357409 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -980 finished +------------------------------ ---------------- +epoch -980 +replay_buffer/size 999047 +trainer/num train calls 21000 +trainer/QF1 Loss 5.05555 +trainer/QF2 Loss 5.24475 +trainer/Policy Loss 103.555 +trainer/Q1 Predictions Mean -55.1636 +trainer/Q1 Predictions Std 18.1489 +trainer/Q1 Predictions Max -2.58328 +trainer/Q1 Predictions Min -76.0581 +trainer/Q2 Predictions Mean -55.2113 +trainer/Q2 Predictions Std 18.1798 +trainer/Q2 Predictions Max -2.19391 +trainer/Q2 Predictions Min -76.4205 +trainer/Q Targets Mean -55.6178 +trainer/Q Targets Std 18.1739 +trainer/Q Targets Max 0 +trainer/Q Targets Min -75.3226 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.01011 +trainer/policy/mean Std 0.636564 +trainer/policy/mean Max 0.995191 +trainer/policy/mean Min -0.990456 +trainer/policy/std Mean 0.5089 +trainer/policy/std Std 0.0324184 +trainer/policy/std Max 0.54427 +trainer/policy/std Min 0.465139 +trainer/Advantage Weights Mean 19.4625 +trainer/Advantage Weights Std 36.4862 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.02827e-10 +trainer/Advantage Score Mean -0.156049 +trainer/Advantage Score Std 0.682178 +trainer/Advantage Score Max 1.83335 +trainer/Advantage Score Min -2.2998 +trainer/V1 Predictions Mean -55.3008 +trainer/V1 Predictions Std 18.1581 +trainer/V1 Predictions Max -2.91802 +trainer/V1 Predictions Min -75.1804 +trainer/VF Loss 0.178169 +expl/num steps total 21000 +expl/num paths total 21 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0230537 +expl/Actions Std 0.68621 +expl/Actions Max 2.43551 +expl/Actions Min -2.63658 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 21000 +eval/num paths total 21 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0905463 +eval/Actions Std 0.340004 +eval/Actions Max 0.950835 +eval/Actions Min -0.96552 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.77931e-06 +time/evaluation sampling (s) 3.27948 +time/exploration sampling (s) 4.1256 +time/logging (s) 0.00924707 +time/saving (s) 0.0139446 +time/training (s) 15.5227 +time/epoch (s) 22.9509 +time/total (s) 524.662 +Epoch -980 +------------------------------ ---------------- +2022-05-15 18:11:33.427349 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -979 finished +------------------------------ ---------------- +epoch -979 +replay_buffer/size 999047 +trainer/num train calls 22000 +trainer/QF1 Loss 0.535816 +trainer/QF2 Loss 0.520559 +trainer/Policy Loss 32.3009 +trainer/Q1 Predictions Mean -56.2368 +trainer/Q1 Predictions Std 18.7591 +trainer/Q1 Predictions Max -2.8648 +trainer/Q1 Predictions Min -79.2387 +trainer/Q2 Predictions Mean -56.2164 +trainer/Q2 Predictions Std 18.7932 +trainer/Q2 Predictions Max -3.01189 +trainer/Q2 Predictions Min -79.1197 +trainer/Q Targets Mean -56.0293 +trainer/Q Targets Std 18.7869 +trainer/Q Targets Max 0 +trainer/Q Targets Min -78.9371 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00356451 +trainer/policy/mean Std 0.625814 +trainer/policy/mean Max 0.989885 +trainer/policy/mean Min -0.994977 +trainer/policy/std Mean 0.506269 +trainer/policy/std Std 0.0331604 +trainer/policy/std Max 0.541872 +trainer/policy/std Min 0.458888 +trainer/Advantage Weights Mean 4.25433 +trainer/Advantage Weights Std 18.2251 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.46108e-11 +trainer/Advantage Score Mean -0.414815 +trainer/Advantage Score Std 0.517688 +trainer/Advantage Score Max 2.13743 +trainer/Advantage Score Min -2.3193 +trainer/V1 Predictions Mean -55.664 +trainer/V1 Predictions Std 18.7897 +trainer/V1 Predictions Max -3.0554 +trainer/V1 Predictions Min -78.9335 +trainer/VF Loss 0.0757116 +expl/num steps total 22000 +expl/num paths total 22 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0151587 +expl/Actions Std 0.661518 +expl/Actions Max 2.13601 +expl/Actions Min -2.32357 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 22000 +eval/num paths total 22 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.140653 +eval/Actions Std 0.312034 +eval/Actions Max 0.955365 +eval/Actions Min -0.973365 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.35043e-06 +time/evaluation sampling (s) 3.59967 +time/exploration sampling (s) 4.04844 +time/logging (s) 0.011421 +time/saving (s) 0.0193649 +time/training (s) 15.3857 +time/epoch (s) 23.0646 +time/total (s) 547.733 +Epoch -979 +------------------------------ ---------------- +2022-05-15 18:11:55.559681 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -978 finished +------------------------------ ---------------- +epoch -978 +replay_buffer/size 999047 +trainer/num train calls 23000 +trainer/QF1 Loss 1.72811 +trainer/QF2 Loss 1.76781 +trainer/Policy Loss 1.81349 +trainer/Q1 Predictions Mean -59.688 +trainer/Q1 Predictions Std 17.066 +trainer/Q1 Predictions Max -4.50913 +trainer/Q1 Predictions Min -77.0749 +trainer/Q2 Predictions Mean -59.7129 +trainer/Q2 Predictions Std 17.039 +trainer/Q2 Predictions Max -4.64888 +trainer/Q2 Predictions Min -77.1544 +trainer/Q Targets Mean -58.7229 +trainer/Q Targets Std 17.4546 +trainer/Q Targets Max -4.41704 +trainer/Q Targets Min -76.6095 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00373482 +trainer/policy/mean Std 0.643535 +trainer/policy/mean Max 0.989193 +trainer/policy/mean Min -0.988991 +trainer/policy/std Mean 0.503111 +trainer/policy/std Std 0.0336787 +trainer/policy/std Max 0.542791 +trainer/policy/std Min 0.4577 +trainer/Advantage Weights Mean 0.187681 +trainer/Advantage Weights Std 2.45498 +trainer/Advantage Weights Max 39.0773 +trainer/Advantage Weights Min 1.73411e-19 +trainer/Advantage Score Mean -1.20604 +trainer/Advantage Score Std 0.730144 +trainer/Advantage Score Max 0.366554 +trainer/Advantage Score Min -4.31986 +trainer/V1 Predictions Mean -58.3106 +trainer/V1 Predictions Std 17.643 +trainer/V1 Predictions Max -2.38617 +trainer/V1 Predictions Min -76.3748 +trainer/VF Loss 0.199281 +expl/num steps total 23000 +expl/num paths total 23 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0281108 +expl/Actions Std 0.704467 +expl/Actions Max 2.28518 +expl/Actions Min -2.42562 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 23000 +eval/num paths total 23 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.101958 +eval/Actions Std 0.431278 +eval/Actions Max 0.956815 +eval/Actions Min -0.98034 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.61823e-06 +time/evaluation sampling (s) 3.25785 +time/exploration sampling (s) 3.89874 +time/logging (s) 0.0109425 +time/saving (s) 0.0150132 +time/training (s) 14.9413 +time/epoch (s) 22.1239 +time/total (s) 569.864 +Epoch -978 +------------------------------ ---------------- +2022-05-15 18:12:18.232226 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -977 finished +------------------------------ ---------------- +epoch -977 +replay_buffer/size 999047 +trainer/num train calls 24000 +trainer/QF1 Loss 0.803178 +trainer/QF2 Loss 0.792497 +trainer/Policy Loss 4.3133 +trainer/Q1 Predictions Mean -59.5783 +trainer/Q1 Predictions Std 17.3986 +trainer/Q1 Predictions Max -3.45241 +trainer/Q1 Predictions Min -77.4581 +trainer/Q2 Predictions Mean -59.5888 +trainer/Q2 Predictions Std 17.3412 +trainer/Q2 Predictions Max -3.72612 +trainer/Q2 Predictions Min -77.4354 +trainer/Q Targets Mean -59.3201 +trainer/Q Targets Std 17.6475 +trainer/Q Targets Max 0 +trainer/Q Targets Min -77.3967 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0115915 +trainer/policy/mean Std 0.614364 +trainer/policy/mean Max 0.988911 +trainer/policy/mean Min -0.976761 +trainer/policy/std Mean 0.500283 +trainer/policy/std Std 0.0331441 +trainer/policy/std Max 0.536454 +trainer/policy/std Min 0.454959 +trainer/Advantage Weights Mean 0.752163 +trainer/Advantage Weights Std 6.9818 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.6593e-15 +trainer/Advantage Score Mean -0.8497 +trainer/Advantage Score Std 0.548088 +trainer/Advantage Score Max 0.702827 +trainer/Advantage Score Min -3.23801 +trainer/V1 Predictions Mean -58.9834 +trainer/V1 Predictions Std 17.6706 +trainer/V1 Predictions Max -2.95946 +trainer/V1 Predictions Min -77.1678 +trainer/VF Loss 0.10481 +expl/num steps total 24000 +expl/num paths total 24 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.032528 +expl/Actions Std 0.689908 +expl/Actions Max 2.25526 +expl/Actions Min -2.41895 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 24000 +eval/num paths total 24 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.221259 +eval/Actions Std 0.46723 +eval/Actions Max 0.761843 +eval/Actions Min -0.777651 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.22005e-06 +time/evaluation sampling (s) 3.10447 +time/exploration sampling (s) 4.27307 +time/logging (s) 0.0111433 +time/saving (s) 0.0181142 +time/training (s) 15.2572 +time/epoch (s) 22.664 +time/total (s) 592.535 +Epoch -977 +------------------------------ ---------------- +2022-05-15 18:12:41.038117 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -976 finished +------------------------------ ---------------- +epoch -976 +replay_buffer/size 999047 +trainer/num train calls 25000 +trainer/QF1 Loss 0.695043 +trainer/QF2 Loss 0.66365 +trainer/Policy Loss 38.9824 +trainer/Q1 Predictions Mean -59.2572 +trainer/Q1 Predictions Std 18.6806 +trainer/Q1 Predictions Max -1.86033 +trainer/Q1 Predictions Min -78.3535 +trainer/Q2 Predictions Mean -59.3435 +trainer/Q2 Predictions Std 18.6318 +trainer/Q2 Predictions Max -2.06856 +trainer/Q2 Predictions Min -78.4961 +trainer/Q Targets Mean -59.5656 +trainer/Q Targets Std 18.5609 +trainer/Q Targets Max 0 +trainer/Q Targets Min -78.8276 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0096944 +trainer/policy/mean Std 0.626359 +trainer/policy/mean Max 0.987443 +trainer/policy/mean Min -0.995498 +trainer/policy/std Mean 0.49867 +trainer/policy/std Std 0.03242 +trainer/policy/std Max 0.53529 +trainer/policy/std Min 0.454431 +trainer/Advantage Weights Mean 6.72166 +trainer/Advantage Weights Std 22.0511 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.08271e-11 +trainer/Advantage Score Mean -0.323538 +trainer/Advantage Score Std 0.501897 +trainer/Advantage Score Max 1.33347 +trainer/Advantage Score Min -2.45948 +trainer/V1 Predictions Mean -59.1947 +trainer/V1 Predictions Std 18.5797 +trainer/V1 Predictions Max -3.35842 +trainer/V1 Predictions Min -78.7444 +trainer/VF Loss 0.0633435 +expl/num steps total 25000 +expl/num paths total 25 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.010589 +expl/Actions Std 0.693457 +expl/Actions Max 2.32528 +expl/Actions Min -2.37062 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 25000 +eval/num paths total 25 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.167151 +eval/Actions Std 0.46464 +eval/Actions Max 0.918421 +eval/Actions Min -0.934519 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.76696e-06 +time/evaluation sampling (s) 3.14079 +time/exploration sampling (s) 3.92467 +time/logging (s) 0.00864337 +time/saving (s) 0.0148691 +time/training (s) 15.7064 +time/epoch (s) 22.7954 +time/total (s) 615.337 +Epoch -976 +------------------------------ ---------------- +2022-05-15 18:13:03.556570 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -975 finished +------------------------------ ---------------- +epoch -975 +replay_buffer/size 999047 +trainer/num train calls 26000 +trainer/QF1 Loss 1.02845 +trainer/QF2 Loss 1.04123 +trainer/Policy Loss 19.946 +trainer/Q1 Predictions Mean -62.9472 +trainer/Q1 Predictions Std 16.672 +trainer/Q1 Predictions Max -2.86415 +trainer/Q1 Predictions Min -80.7117 +trainer/Q2 Predictions Mean -62.9613 +trainer/Q2 Predictions Std 16.6413 +trainer/Q2 Predictions Max -3.30704 +trainer/Q2 Predictions Min -80.5507 +trainer/Q Targets Mean -62.6867 +trainer/Q Targets Std 16.7476 +trainer/Q Targets Max 0 +trainer/Q Targets Min -79.8236 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0124866 +trainer/policy/mean Std 0.624768 +trainer/policy/mean Max 0.988764 +trainer/policy/mean Min -0.989577 +trainer/policy/std Mean 0.497249 +trainer/policy/std Std 0.0315983 +trainer/policy/std Max 0.534012 +trainer/policy/std Min 0.453989 +trainer/Advantage Weights Mean 3.78742 +trainer/Advantage Weights Std 17.6217 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.18183e-15 +trainer/Advantage Score Mean -0.49177 +trainer/Advantage Score Std 0.478986 +trainer/Advantage Score Max 0.741071 +trainer/Advantage Score Min -3.37586 +trainer/V1 Predictions Mean -62.3642 +trainer/V1 Predictions Std 16.7766 +trainer/V1 Predictions Max -2.0631 +trainer/V1 Predictions Min -80.044 +trainer/VF Loss 0.0572847 +expl/num steps total 26000 +expl/num paths total 26 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0175885 +expl/Actions Std 0.725543 +expl/Actions Max 2.67695 +expl/Actions Min -2.30462 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 26000 +eval/num paths total 26 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0247503 +eval/Actions Std 0.646654 +eval/Actions Max 0.992632 +eval/Actions Min -0.993542 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.43286e-06 +time/evaluation sampling (s) 3.06856 +time/exploration sampling (s) 3.91336 +time/logging (s) 0.00679594 +time/saving (s) 0.0104159 +time/training (s) 15.5124 +time/epoch (s) 22.5115 +time/total (s) 637.853 +Epoch -975 +------------------------------ ---------------- +2022-05-15 18:13:26.151745 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -974 finished +------------------------------ ---------------- +epoch -974 +replay_buffer/size 999047 +trainer/num train calls 27000 +trainer/QF1 Loss 0.772793 +trainer/QF2 Loss 0.886455 +trainer/Policy Loss 52.104 +trainer/Q1 Predictions Mean -61.9839 +trainer/Q1 Predictions Std 17.8701 +trainer/Q1 Predictions Max -0.726233 +trainer/Q1 Predictions Min -82.4216 +trainer/Q2 Predictions Mean -61.9158 +trainer/Q2 Predictions Std 17.8758 +trainer/Q2 Predictions Max -0.780231 +trainer/Q2 Predictions Min -82.3225 +trainer/Q Targets Mean -62.2083 +trainer/Q Targets Std 17.8817 +trainer/Q Targets Max 0 +trainer/Q Targets Min -82.3453 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00711156 +trainer/policy/mean Std 0.629754 +trainer/policy/mean Max 0.992031 +trainer/policy/mean Min -0.98861 +trainer/policy/std Mean 0.493833 +trainer/policy/std Std 0.0311894 +trainer/policy/std Max 0.530671 +trainer/policy/std Min 0.451618 +trainer/Advantage Weights Mean 8.69929 +trainer/Advantage Weights Std 23.5929 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.04781e-11 +trainer/Advantage Score Mean -0.251392 +trainer/Advantage Score Std 0.509775 +trainer/Advantage Score Max 0.858295 +trainer/Advantage Score Min -2.46117 +trainer/V1 Predictions Mean -61.9023 +trainer/V1 Predictions Std 17.8839 +trainer/V1 Predictions Max -0.454493 +trainer/V1 Predictions Min -81.9225 +trainer/VF Loss 0.0578003 +expl/num steps total 27000 +expl/num paths total 27 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0101131 +expl/Actions Std 0.747455 +expl/Actions Max 2.28711 +expl/Actions Min -2.37636 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 27000 +eval/num paths total 27 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.159109 +eval/Actions Std 0.319992 +eval/Actions Max 0.799105 +eval/Actions Min -0.869555 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.32972e-06 +time/evaluation sampling (s) 3.35596 +time/exploration sampling (s) 4.00332 +time/logging (s) 0.00855682 +time/saving (s) 0.011485 +time/training (s) 15.2124 +time/epoch (s) 22.5918 +time/total (s) 660.449 +Epoch -974 +------------------------------ ---------------- +2022-05-15 18:13:49.018404 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -973 finished +------------------------------ ---------------- +epoch -973 +replay_buffer/size 999047 +trainer/num train calls 28000 +trainer/QF1 Loss 0.584877 +trainer/QF2 Loss 0.680581 +trainer/Policy Loss 39.3962 +trainer/Q1 Predictions Mean -63.8368 +trainer/Q1 Predictions Std 17.797 +trainer/Q1 Predictions Max -3.90049 +trainer/Q1 Predictions Min -83.0272 +trainer/Q2 Predictions Mean -63.7804 +trainer/Q2 Predictions Std 17.824 +trainer/Q2 Predictions Max -3.59518 +trainer/Q2 Predictions Min -83.7796 +trainer/Q Targets Mean -63.8784 +trainer/Q Targets Std 17.6382 +trainer/Q Targets Max -4.17025 +trainer/Q Targets Min -82.7147 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.016296 +trainer/policy/mean Std 0.641966 +trainer/policy/mean Max 0.99203 +trainer/policy/mean Min -0.994604 +trainer/policy/std Mean 0.49339 +trainer/policy/std Std 0.0301459 +trainer/policy/std Max 0.52607 +trainer/policy/std Min 0.452669 +trainer/Advantage Weights Mean 6.30535 +trainer/Advantage Weights Std 21.4166 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.72694e-11 +trainer/Advantage Score Mean -0.28197 +trainer/Advantage Score Std 0.445117 +trainer/Advantage Score Max 1.25193 +trainer/Advantage Score Min -2.43253 +trainer/V1 Predictions Mean -63.5379 +trainer/V1 Predictions Std 17.7801 +trainer/V1 Predictions Max -3.79777 +trainer/V1 Predictions Min -82.8827 +trainer/VF Loss 0.0478038 +expl/num steps total 28000 +expl/num paths total 28 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00259535 +expl/Actions Std 0.739691 +expl/Actions Max 2.43702 +expl/Actions Min -2.3118 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 28000 +eval/num paths total 28 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0137137 +eval/Actions Std 0.579886 +eval/Actions Max 0.993162 +eval/Actions Min -0.992725 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.05405e-06 +time/evaluation sampling (s) 3.36356 +time/exploration sampling (s) 3.63214 +time/logging (s) 0.0111318 +time/saving (s) 0.0157456 +time/training (s) 15.8407 +time/epoch (s) 22.8633 +time/total (s) 683.317 +Epoch -973 +------------------------------ ---------------- +2022-05-15 18:14:11.984972 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -972 finished +------------------------------ ---------------- +epoch -972 +replay_buffer/size 999047 +trainer/num train calls 29000 +trainer/QF1 Loss 1.53648 +trainer/QF2 Loss 1.64287 +trainer/Policy Loss 14.8886 +trainer/Q1 Predictions Mean -64.0026 +trainer/Q1 Predictions Std 17.2876 +trainer/Q1 Predictions Max -3.49815 +trainer/Q1 Predictions Min -82.0324 +trainer/Q2 Predictions Mean -63.9016 +trainer/Q2 Predictions Std 17.3193 +trainer/Q2 Predictions Max -3.29123 +trainer/Q2 Predictions Min -82.2283 +trainer/Q Targets Mean -63.8071 +trainer/Q Targets Std 17.3794 +trainer/Q Targets Max 0 +trainer/Q Targets Min -82.75 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0109022 +trainer/policy/mean Std 0.632391 +trainer/policy/mean Max 0.994308 +trainer/policy/mean Min -0.991574 +trainer/policy/std Mean 0.489865 +trainer/policy/std Std 0.0309068 +trainer/policy/std Max 0.523226 +trainer/policy/std Min 0.446745 +trainer/Advantage Weights Mean 2.36906 +trainer/Advantage Weights Std 13.04 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.75149e-17 +trainer/Advantage Score Mean -0.606687 +trainer/Advantage Score Std 0.526639 +trainer/Advantage Score Max 0.946472 +trainer/Advantage Score Min -3.70961 +trainer/V1 Predictions Mean -63.4392 +trainer/V1 Predictions Std 17.3028 +trainer/V1 Predictions Max -2.4652 +trainer/V1 Predictions Min -81.8019 +trainer/VF Loss 0.0727639 +expl/num steps total 29000 +expl/num paths total 29 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0175269 +expl/Actions Std 0.690471 +expl/Actions Max 2.34241 +expl/Actions Min -2.24582 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 29000 +eval/num paths total 29 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.1253 +eval/Actions Std 0.347671 +eval/Actions Max 0.980924 +eval/Actions Min -0.974156 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.42918e-06 +time/evaluation sampling (s) 3.26583 +time/exploration sampling (s) 3.66168 +time/logging (s) 0.0164419 +time/saving (s) 0.0183304 +time/training (s) 16.0013 +time/epoch (s) 22.9636 +time/total (s) 706.288 +Epoch -972 +------------------------------ ---------------- +2022-05-15 18:14:35.152221 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -971 finished +------------------------------ ---------------- +epoch -971 +replay_buffer/size 999047 +trainer/num train calls 30000 +trainer/QF1 Loss 2.28579 +trainer/QF2 Loss 2.16657 +trainer/Policy Loss 101.579 +trainer/Q1 Predictions Mean -63.9979 +trainer/Q1 Predictions Std 16.766 +trainer/Q1 Predictions Max -3.12682 +trainer/Q1 Predictions Min -83.3194 +trainer/Q2 Predictions Mean -64.0847 +trainer/Q2 Predictions Std 16.7789 +trainer/Q2 Predictions Max -2.54529 +trainer/Q2 Predictions Min -83.38 +trainer/Q Targets Mean -65.0066 +trainer/Q Targets Std 16.9344 +trainer/Q Targets Max 0 +trainer/Q Targets Min -83.9819 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0204375 +trainer/policy/mean Std 0.651176 +trainer/policy/mean Max 0.993068 +trainer/policy/mean Min -0.988379 +trainer/policy/std Mean 0.490311 +trainer/policy/std Std 0.0306115 +trainer/policy/std Max 0.52379 +trainer/policy/std Min 0.44643 +trainer/Advantage Weights Mean 17.381 +trainer/Advantage Weights Std 33.5795 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.73484e-14 +trainer/Advantage Score Mean -0.0989042 +trainer/Advantage Score Std 0.507677 +trainer/Advantage Score Max 1.08459 +trainer/Advantage Score Min -3.06812 +trainer/V1 Predictions Mean -64.6703 +trainer/V1 Predictions Std 17.0251 +trainer/V1 Predictions Max -3.7522 +trainer/V1 Predictions Min -84.0793 +trainer/VF Loss 0.0816084 +expl/num steps total 30000 +expl/num paths total 30 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00118426 +expl/Actions Std 0.698335 +expl/Actions Max 2.35078 +expl/Actions Min -2.28852 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 30000 +eval/num paths total 30 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.178378 +eval/Actions Std 0.372509 +eval/Actions Max 0.772135 +eval/Actions Min -0.763341 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.13297e-06 +time/evaluation sampling (s) 3.12638 +time/exploration sampling (s) 4.04342 +time/logging (s) 0.0090069 +time/saving (s) 0.0131149 +time/training (s) 15.9591 +time/epoch (s) 23.1511 +time/total (s) 729.446 +Epoch -971 +------------------------------ ---------------- +2022-05-15 18:14:57.619922 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -970 finished +------------------------------ ---------------- +epoch -970 +replay_buffer/size 999047 +trainer/num train calls 31000 +trainer/QF1 Loss 1.1926 +trainer/QF2 Loss 1.2785 +trainer/Policy Loss 45.0349 +trainer/Q1 Predictions Mean -63.3879 +trainer/Q1 Predictions Std 19.7738 +trainer/Q1 Predictions Max -3.86549 +trainer/Q1 Predictions Min -84.5115 +trainer/Q2 Predictions Mean -63.3641 +trainer/Q2 Predictions Std 19.7518 +trainer/Q2 Predictions Max -3.91742 +trainer/Q2 Predictions Min -84.1586 +trainer/Q Targets Mean -63.4684 +trainer/Q Targets Std 19.6818 +trainer/Q Targets Max -4.66277 +trainer/Q Targets Min -83.9368 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0324728 +trainer/policy/mean Std 0.650087 +trainer/policy/mean Max 0.990761 +trainer/policy/mean Min -0.991739 +trainer/policy/std Mean 0.487626 +trainer/policy/std Std 0.0301458 +trainer/policy/std Max 0.522888 +trainer/policy/std Min 0.443222 +trainer/Advantage Weights Mean 6.61444 +trainer/Advantage Weights Std 22.0572 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.96825e-13 +trainer/Advantage Score Mean -0.407714 +trainer/Advantage Score Std 0.537065 +trainer/Advantage Score Max 0.922958 +trainer/Advantage Score Min -2.76342 +trainer/V1 Predictions Mean -63.1642 +trainer/V1 Predictions Std 19.755 +trainer/V1 Predictions Max -4.22378 +trainer/V1 Predictions Min -84.3119 +trainer/VF Loss 0.0667187 +expl/num steps total 31000 +expl/num paths total 31 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0215632 +expl/Actions Std 0.714605 +expl/Actions Max 2.59376 +expl/Actions Min -2.56226 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 31000 +eval/num paths total 31 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.160677 +eval/Actions Std 0.594042 +eval/Actions Max 0.987076 +eval/Actions Min -0.987194 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99187e-06 +time/evaluation sampling (s) 2.96526 +time/exploration sampling (s) 4.12878 +time/logging (s) 0.00710839 +time/saving (s) 0.0117446 +time/training (s) 15.3473 +time/epoch (s) 22.4601 +time/total (s) 751.91 +Epoch -970 +------------------------------ ---------------- +2022-05-15 18:15:19.995475 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -969 finished +------------------------------ ---------------- +epoch -969 +replay_buffer/size 999047 +trainer/num train calls 32000 +trainer/QF1 Loss 0.793303 +trainer/QF2 Loss 0.812393 +trainer/Policy Loss 44.9222 +trainer/Q1 Predictions Mean -65.954 +trainer/Q1 Predictions Std 15.0398 +trainer/Q1 Predictions Max -2.51901 +trainer/Q1 Predictions Min -84.5662 +trainer/Q2 Predictions Mean -65.9437 +trainer/Q2 Predictions Std 15.0284 +trainer/Q2 Predictions Max -2.96211 +trainer/Q2 Predictions Min -84.3828 +trainer/Q Targets Mean -66.0787 +trainer/Q Targets Std 15.2866 +trainer/Q Targets Max -3.07854 +trainer/Q Targets Min -84.7862 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00167713 +trainer/policy/mean Std 0.641669 +trainer/policy/mean Max 0.990163 +trainer/policy/mean Min -0.9882 +trainer/policy/std Mean 0.487794 +trainer/policy/std Std 0.0284492 +trainer/policy/std Max 0.520916 +trainer/policy/std Min 0.44598 +trainer/Advantage Weights Mean 7.06015 +trainer/Advantage Weights Std 21.9895 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.12651e-11 +trainer/Advantage Score Mean -0.32282 +trainer/Advantage Score Std 0.523706 +trainer/Advantage Score Max 1.10172 +trainer/Advantage Score Min -2.52093 +trainer/V1 Predictions Mean -65.7494 +trainer/V1 Predictions Std 15.3933 +trainer/V1 Predictions Max -1.32713 +trainer/V1 Predictions Min -84.6854 +trainer/VF Loss 0.0668309 +expl/num steps total 32000 +expl/num paths total 32 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00580753 +expl/Actions Std 0.767192 +expl/Actions Max 2.84772 +expl/Actions Min -2.58216 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 32000 +eval/num paths total 32 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0369894 +eval/Actions Std 0.670004 +eval/Actions Max 0.99397 +eval/Actions Min -0.994962 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.05381e-06 +time/evaluation sampling (s) 3.33145 +time/exploration sampling (s) 3.68351 +time/logging (s) 0.00796107 +time/saving (s) 0.011489 +time/training (s) 15.3366 +time/epoch (s) 22.371 +time/total (s) 774.286 +Epoch -969 +------------------------------ ---------------- +2022-05-15 18:15:41.955783 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -968 finished +------------------------------ ---------------- +epoch -968 +replay_buffer/size 999047 +trainer/num train calls 33000 +trainer/QF1 Loss 11.2045 +trainer/QF2 Loss 11.393 +trainer/Policy Loss 24.2896 +trainer/Q1 Predictions Mean -62.7776 +trainer/Q1 Predictions Std 18.8969 +trainer/Q1 Predictions Max -3.57522 +trainer/Q1 Predictions Min -81.5761 +trainer/Q2 Predictions Mean -62.7922 +trainer/Q2 Predictions Std 18.9092 +trainer/Q2 Predictions Max -3.44292 +trainer/Q2 Predictions Min -81.5878 +trainer/Q Targets Mean -63.0686 +trainer/Q Targets Std 18.7288 +trainer/Q Targets Max 0 +trainer/Q Targets Min -82.1978 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00398462 +trainer/policy/mean Std 0.644963 +trainer/policy/mean Max 0.991493 +trainer/policy/mean Min -0.992667 +trainer/policy/std Mean 0.487416 +trainer/policy/std Std 0.0283244 +trainer/policy/std Max 0.523319 +trainer/policy/std Min 0.445314 +trainer/Advantage Weights Mean 4.79119 +trainer/Advantage Weights Std 18.3805 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.56348e-18 +trainer/Advantage Score Mean -0.302638 +trainer/Advantage Score Std 0.505634 +trainer/Advantage Score Max 1.2558 +trainer/Advantage Score Min -3.97303 +trainer/V1 Predictions Mean -62.8569 +trainer/V1 Predictions Std 18.8324 +trainer/V1 Predictions Max -2.85331 +trainer/V1 Predictions Min -81.4913 +trainer/VF Loss 0.0552519 +expl/num steps total 33000 +expl/num paths total 33 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0106778 +expl/Actions Std 0.747297 +expl/Actions Max 2.32592 +expl/Actions Min -2.33534 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 33000 +eval/num paths total 33 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00119732 +eval/Actions Std 0.641737 +eval/Actions Max 0.989533 +eval/Actions Min -0.980481 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.89315e-06 +time/evaluation sampling (s) 3.2076 +time/exploration sampling (s) 3.57036 +time/logging (s) 0.0110771 +time/saving (s) 0.0151293 +time/training (s) 15.1535 +time/epoch (s) 21.9577 +time/total (s) 796.248 +Epoch -968 +------------------------------ ---------------- +2022-05-15 18:16:04.366843 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -967 finished +------------------------------ ---------------- +epoch -967 +replay_buffer/size 999047 +trainer/num train calls 34000 +trainer/QF1 Loss 3.26522 +trainer/QF2 Loss 3.16065 +trainer/Policy Loss 47.0673 +trainer/Q1 Predictions Mean -65.8201 +trainer/Q1 Predictions Std 18.0588 +trainer/Q1 Predictions Max -6.33563 +trainer/Q1 Predictions Min -84.5243 +trainer/Q2 Predictions Mean -65.8071 +trainer/Q2 Predictions Std 18.039 +trainer/Q2 Predictions Max -6.61967 +trainer/Q2 Predictions Min -84.1248 +trainer/Q Targets Mean -65.6399 +trainer/Q Targets Std 17.993 +trainer/Q Targets Max 0 +trainer/Q Targets Min -84.6365 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0157489 +trainer/policy/mean Std 0.636431 +trainer/policy/mean Max 0.991584 +trainer/policy/mean Min -0.986075 +trainer/policy/std Mean 0.48452 +trainer/policy/std Std 0.0289959 +trainer/policy/std Max 0.518307 +trainer/policy/std Min 0.440096 +trainer/Advantage Weights Mean 8.95161 +trainer/Advantage Weights Std 25.165 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.25997e-13 +trainer/Advantage Score Mean -0.241802 +trainer/Advantage Score Std 0.551188 +trainer/Advantage Score Max 1.45028 +trainer/Advantage Score Min -2.97025 +trainer/V1 Predictions Mean -65.4074 +trainer/V1 Predictions Std 18.1177 +trainer/V1 Predictions Max -6.77258 +trainer/V1 Predictions Min -84.6188 +trainer/VF Loss 0.0781625 +expl/num steps total 34000 +expl/num paths total 34 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0253251 +expl/Actions Std 0.758805 +expl/Actions Max 2.64411 +expl/Actions Min -2.73136 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 34000 +eval/num paths total 34 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0421171 +eval/Actions Std 0.644249 +eval/Actions Max 0.989924 +eval/Actions Min -0.998296 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.96719e-06 +time/evaluation sampling (s) 3.33434 +time/exploration sampling (s) 3.76528 +time/logging (s) 0.0072718 +time/saving (s) 0.010698 +time/training (s) 15.2843 +time/epoch (s) 22.4019 +time/total (s) 818.655 +Epoch -967 +------------------------------ ---------------- +2022-05-15 18:16:27.217633 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -966 finished +------------------------------ ---------------- +epoch -966 +replay_buffer/size 999047 +trainer/num train calls 35000 +trainer/QF1 Loss 0.989103 +trainer/QF2 Loss 1.05902 +trainer/Policy Loss 59.7947 +trainer/Q1 Predictions Mean -65.6192 +trainer/Q1 Predictions Std 17.7226 +trainer/Q1 Predictions Max -4.01194 +trainer/Q1 Predictions Min -82.0284 +trainer/Q2 Predictions Mean -65.6199 +trainer/Q2 Predictions Std 17.6771 +trainer/Q2 Predictions Max -3.98692 +trainer/Q2 Predictions Min -82.3941 +trainer/Q Targets Mean -65.96 +trainer/Q Targets Std 17.762 +trainer/Q Targets Max 0 +trainer/Q Targets Min -82.6265 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00457302 +trainer/policy/mean Std 0.655679 +trainer/policy/mean Max 0.997296 +trainer/policy/mean Min -0.992211 +trainer/policy/std Mean 0.482511 +trainer/policy/std Std 0.0298467 +trainer/policy/std Max 0.517999 +trainer/policy/std Min 0.438601 +trainer/Advantage Weights Mean 10.8671 +trainer/Advantage Weights Std 27.1277 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.92583e-09 +trainer/Advantage Score Mean -0.158749 +trainer/Advantage Score Std 0.428382 +trainer/Advantage Score Max 0.817533 +trainer/Advantage Score Min -1.85343 +trainer/V1 Predictions Mean -65.7268 +trainer/V1 Predictions Std 17.6325 +trainer/V1 Predictions Max -4.16909 +trainer/V1 Predictions Min -82.4506 +trainer/VF Loss 0.0527344 +expl/num steps total 35000 +expl/num paths total 35 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0167524 +expl/Actions Std 0.745 +expl/Actions Max 2.34996 +expl/Actions Min -2.24971 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 35000 +eval/num paths total 35 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.152673 +eval/Actions Std 0.674255 +eval/Actions Max 0.981633 +eval/Actions Min -0.99199 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.65287e-06 +time/evaluation sampling (s) 3.49995 +time/exploration sampling (s) 4.17583 +time/logging (s) 0.00659748 +time/saving (s) 0.0103353 +time/training (s) 15.1514 +time/epoch (s) 22.8441 +time/total (s) 841.504 +Epoch -966 +------------------------------ ---------------- +2022-05-15 18:16:50.318278 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -965 finished +------------------------------ ---------------- +epoch -965 +replay_buffer/size 999047 +trainer/num train calls 36000 +trainer/QF1 Loss 0.846674 +trainer/QF2 Loss 0.854883 +trainer/Policy Loss 26.146 +trainer/Q1 Predictions Mean -67.5011 +trainer/Q1 Predictions Std 17.1384 +trainer/Q1 Predictions Max -3.06046 +trainer/Q1 Predictions Min -85.6321 +trainer/Q2 Predictions Mean -67.4321 +trainer/Q2 Predictions Std 17.2056 +trainer/Q2 Predictions Max -2.81294 +trainer/Q2 Predictions Min -85.6575 +trainer/Q Targets Mean -67.6339 +trainer/Q Targets Std 17.345 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2836 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0074142 +trainer/policy/mean Std 0.66165 +trainer/policy/mean Max 0.995797 +trainer/policy/mean Min -0.990233 +trainer/policy/std Mean 0.482518 +trainer/policy/std Std 0.0291777 +trainer/policy/std Max 0.5178 +trainer/policy/std Min 0.439565 +trainer/Advantage Weights Mean 5.60175 +trainer/Advantage Weights Std 19.4158 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.47422e-10 +trainer/Advantage Score Mean -0.300191 +trainer/Advantage Score Std 0.46951 +trainer/Advantage Score Max 1.8616 +trainer/Advantage Score Min -2.15275 +trainer/V1 Predictions Mean -67.3518 +trainer/V1 Predictions Std 17.2892 +trainer/V1 Predictions Max -2.31456 +trainer/V1 Predictions Min -86.0639 +trainer/VF Loss 0.0582179 +expl/num steps total 36000 +expl/num paths total 36 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00356644 +expl/Actions Std 0.731048 +expl/Actions Max 2.13428 +expl/Actions Min -2.48548 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 35701 +eval/num paths total 36 +eval/path length Mean 701 +eval/path length Std 0 +eval/path length Max 701 +eval/path length Min 701 +eval/Rewards Mean 0.00142653 +eval/Rewards Std 0.0377425 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0113164 +eval/Actions Std 0.662613 +eval/Actions Max 0.995239 +eval/Actions Min -0.991846 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.67709e-06 +time/evaluation sampling (s) 3.45811 +time/exploration sampling (s) 3.80148 +time/logging (s) 0.0100385 +time/saving (s) 0.0149498 +time/training (s) 15.814 +time/epoch (s) 23.0986 +time/total (s) 864.607 +Epoch -965 +------------------------------ ---------------- +2022-05-15 18:17:12.712130 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -964 finished +------------------------------ ---------------- +epoch -964 +replay_buffer/size 999047 +trainer/num train calls 37000 +trainer/QF1 Loss 1.59575 +trainer/QF2 Loss 1.50786 +trainer/Policy Loss 112.385 +trainer/Q1 Predictions Mean -66.7148 +trainer/Q1 Predictions Std 17.166 +trainer/Q1 Predictions Max -2.19483 +trainer/Q1 Predictions Min -85.1377 +trainer/Q2 Predictions Mean -66.7678 +trainer/Q2 Predictions Std 17.1557 +trainer/Q2 Predictions Max -1.56932 +trainer/Q2 Predictions Min -84.4858 +trainer/Q Targets Mean -67.5284 +trainer/Q Targets Std 17.0878 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.3529 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0212102 +trainer/policy/mean Std 0.643947 +trainer/policy/mean Max 0.992872 +trainer/policy/mean Min -0.994159 +trainer/policy/std Mean 0.48142 +trainer/policy/std Std 0.0282925 +trainer/policy/std Max 0.516108 +trainer/policy/std Min 0.442393 +trainer/Advantage Weights Mean 21.1714 +trainer/Advantage Weights Std 35.9932 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.26987e-08 +trainer/Advantage Score Mean 0.0296937 +trainer/Advantage Score Std 0.459524 +trainer/Advantage Score Max 1.52149 +trainer/Advantage Score Min -1.81818 +trainer/V1 Predictions Mean -67.3017 +trainer/V1 Predictions Std 16.9342 +trainer/V1 Predictions Max -3.6272 +trainer/V1 Predictions Min -85.3264 +trainer/VF Loss 0.108633 +expl/num steps total 37000 +expl/num paths total 37 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0294073 +expl/Actions Std 0.754518 +expl/Actions Max 2.50373 +expl/Actions Min -2.51571 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 36701 +eval/num paths total 37 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.111257 +eval/Actions Std 0.575926 +eval/Actions Max 0.978886 +eval/Actions Min -0.990678 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.90619e-06 +time/evaluation sampling (s) 3.06005 +time/exploration sampling (s) 3.75144 +time/logging (s) 0.00768698 +time/saving (s) 0.0126281 +time/training (s) 15.5528 +time/epoch (s) 22.3846 +time/total (s) 886.997 +Epoch -964 +------------------------------ ---------------- +2022-05-15 18:17:34.963028 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -963 finished +------------------------------ ---------------- +epoch -963 +replay_buffer/size 999047 +trainer/num train calls 38000 +trainer/QF1 Loss 1.30293 +trainer/QF2 Loss 1.26458 +trainer/Policy Loss 45.7767 +trainer/Q1 Predictions Mean -67.6539 +trainer/Q1 Predictions Std 16.7979 +trainer/Q1 Predictions Max -1.661 +trainer/Q1 Predictions Min -84.5587 +trainer/Q2 Predictions Mean -67.6854 +trainer/Q2 Predictions Std 16.7701 +trainer/Q2 Predictions Max -1.79122 +trainer/Q2 Predictions Min -84.6092 +trainer/Q Targets Mean -68.2495 +trainer/Q Targets Std 16.8781 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.0928 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0128725 +trainer/policy/mean Std 0.66143 +trainer/policy/mean Max 0.996155 +trainer/policy/mean Min -0.993689 +trainer/policy/std Mean 0.477992 +trainer/policy/std Std 0.0271515 +trainer/policy/std Max 0.514207 +trainer/policy/std Min 0.437264 +trainer/Advantage Weights Mean 8.57104 +trainer/Advantage Weights Std 22.9409 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.94423e-09 +trainer/Advantage Score Mean -0.163904 +trainer/Advantage Score Std 0.465551 +trainer/Advantage Score Max 1.48994 +trainer/Advantage Score Min -1.85323 +trainer/V1 Predictions Mean -68.0114 +trainer/V1 Predictions Std 16.6994 +trainer/V1 Predictions Max -1.62215 +trainer/V1 Predictions Min -84.9066 +trainer/VF Loss 0.0689077 +expl/num steps total 38000 +expl/num paths total 38 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.089896 +expl/Actions Std 0.844802 +expl/Actions Max 2.52908 +expl/Actions Min -2.39413 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 37701 +eval/num paths total 38 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00423142 +eval/Actions Std 0.636381 +eval/Actions Max 0.996838 +eval/Actions Min -0.996726 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.96581e-06 +time/evaluation sampling (s) 3.20475 +time/exploration sampling (s) 3.77143 +time/logging (s) 0.00965586 +time/saving (s) 0.0146852 +time/training (s) 15.2462 +time/epoch (s) 22.2468 +time/total (s) 909.249 +Epoch -963 +------------------------------ ---------------- +2022-05-15 18:17:57.275067 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -962 finished +------------------------------ ---------------- +epoch -962 +replay_buffer/size 999047 +trainer/num train calls 39000 +trainer/QF1 Loss 3.15266 +trainer/QF2 Loss 3.09156 +trainer/Policy Loss 34.8384 +trainer/Q1 Predictions Mean -65.9791 +trainer/Q1 Predictions Std 20.0647 +trainer/Q1 Predictions Max -3.21759 +trainer/Q1 Predictions Min -86.2151 +trainer/Q2 Predictions Mean -65.9197 +trainer/Q2 Predictions Std 20.0353 +trainer/Q2 Predictions Max -3.10838 +trainer/Q2 Predictions Min -86.0299 +trainer/Q Targets Mean -65.9805 +trainer/Q Targets Std 20.1661 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0213 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00219946 +trainer/policy/mean Std 0.671646 +trainer/policy/mean Max 0.991237 +trainer/policy/mean Min -0.987501 +trainer/policy/std Mean 0.47899 +trainer/policy/std Std 0.0276699 +trainer/policy/std Max 0.512995 +trainer/policy/std Min 0.438143 +trainer/Advantage Weights Mean 8.83937 +trainer/Advantage Weights Std 25.3779 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.68012e-11 +trainer/Advantage Score Mean -0.201405 +trainer/Advantage Score Std 0.471319 +trainer/Advantage Score Max 1.41881 +trainer/Advantage Score Min -2.48096 +trainer/V1 Predictions Mean -65.8198 +trainer/V1 Predictions Std 19.9395 +trainer/V1 Predictions Max -4.05125 +trainer/V1 Predictions Min -86.0487 +trainer/VF Loss 0.0719814 +expl/num steps total 39000 +expl/num paths total 39 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0123488 +expl/Actions Std 0.760948 +expl/Actions Max 2.34862 +expl/Actions Min -2.3058 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 38701 +eval/num paths total 39 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0414673 +eval/Actions Std 0.593084 +eval/Actions Max 0.997367 +eval/Actions Min -0.994088 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99606e-06 +time/evaluation sampling (s) 3.17486 +time/exploration sampling (s) 3.7831 +time/logging (s) 0.00964519 +time/saving (s) 0.0165494 +time/training (s) 15.3197 +time/epoch (s) 22.3039 +time/total (s) 931.56 +Epoch -962 +------------------------------ ---------------- +2022-05-15 18:18:19.506445 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -961 finished +------------------------------ ---------------- +epoch -961 +replay_buffer/size 999047 +trainer/num train calls 40000 +trainer/QF1 Loss 1.51992 +trainer/QF2 Loss 1.46397 +trainer/Policy Loss 37.7077 +trainer/Q1 Predictions Mean -65.0523 +trainer/Q1 Predictions Std 21.4708 +trainer/Q1 Predictions Max -1.87057 +trainer/Q1 Predictions Min -85.7845 +trainer/Q2 Predictions Mean -65.0764 +trainer/Q2 Predictions Std 21.4278 +trainer/Q2 Predictions Max -1.66837 +trainer/Q2 Predictions Min -85.8406 +trainer/Q Targets Mean -65.6727 +trainer/Q Targets Std 21.388 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4814 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00445725 +trainer/policy/mean Std 0.654693 +trainer/policy/mean Max 0.992685 +trainer/policy/mean Min -0.98936 +trainer/policy/std Mean 0.475669 +trainer/policy/std Std 0.0273152 +trainer/policy/std Max 0.507718 +trainer/policy/std Min 0.436203 +trainer/Advantage Weights Mean 6.37972 +trainer/Advantage Weights Std 21.7149 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.08954e-18 +trainer/Advantage Score Mean -0.313921 +trainer/Advantage Score Std 0.5209 +trainer/Advantage Score Max 1.94849 +trainer/Advantage Score Min -4.13608 +trainer/V1 Predictions Mean -65.3269 +trainer/V1 Predictions Std 21.3843 +trainer/V1 Predictions Max -3.08557 +trainer/V1 Predictions Min -86.0893 +trainer/VF Loss 0.0666889 +expl/num steps total 40000 +expl/num paths total 40 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.127245 +expl/Actions Std 0.843796 +expl/Actions Max 2.815 +expl/Actions Min -2.56664 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 39701 +eval/num paths total 40 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0204272 +eval/Actions Std 0.656235 +eval/Actions Max 0.998831 +eval/Actions Min -0.994395 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.98303e-06 +time/evaluation sampling (s) 2.93604 +time/exploration sampling (s) 4.04698 +time/logging (s) 0.0113634 +time/saving (s) 0.015636 +time/training (s) 15.2153 +time/epoch (s) 22.2253 +time/total (s) 953.791 +Epoch -961 +------------------------------ ---------------- +2022-05-15 18:18:41.800506 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -960 finished +------------------------------ ---------------- +epoch -960 +replay_buffer/size 999047 +trainer/num train calls 41000 +trainer/QF1 Loss 1.08943 +trainer/QF2 Loss 1.0767 +trainer/Policy Loss 14.134 +trainer/Q1 Predictions Mean -65.606 +trainer/Q1 Predictions Std 19.3569 +trainer/Q1 Predictions Max -2.87386 +trainer/Q1 Predictions Min -84.3866 +trainer/Q2 Predictions Mean -65.7336 +trainer/Q2 Predictions Std 19.3389 +trainer/Q2 Predictions Max -3.34142 +trainer/Q2 Predictions Min -84.4374 +trainer/Q Targets Mean -65.8118 +trainer/Q Targets Std 19.6699 +trainer/Q Targets Max 0 +trainer/Q Targets Min -84.7293 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00222593 +trainer/policy/mean Std 0.652999 +trainer/policy/mean Max 0.993806 +trainer/policy/mean Min -0.991951 +trainer/policy/std Mean 0.47614 +trainer/policy/std Std 0.0277766 +trainer/policy/std Max 0.510482 +trainer/policy/std Min 0.435764 +trainer/Advantage Weights Mean 2.47915 +trainer/Advantage Weights Std 11.3539 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.80806e-15 +trainer/Advantage Score Mean -0.42605 +trainer/Advantage Score Std 0.474932 +trainer/Advantage Score Max 0.78751 +trainer/Advantage Score Min -3.32017 +trainer/V1 Predictions Mean -65.5471 +trainer/V1 Predictions Std 19.5181 +trainer/V1 Predictions Max -3.16579 +trainer/V1 Predictions Min -84.3683 +trainer/VF Loss 0.0479512 +expl/num steps total 41000 +expl/num paths total 41 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0136811 +expl/Actions Std 0.755834 +expl/Actions Max 2.47203 +expl/Actions Min -2.64584 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 40701 +eval/num paths total 41 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.221898 +eval/Actions Std 0.7058 +eval/Actions Max 0.997366 +eval/Actions Min -0.995149 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.38117e-06 +time/evaluation sampling (s) 3.16087 +time/exploration sampling (s) 3.6693 +time/logging (s) 0.0110095 +time/saving (s) 0.0154708 +time/training (s) 15.4309 +time/epoch (s) 22.2875 +time/total (s) 976.084 +Epoch -960 +------------------------------ ---------------- +2022-05-15 18:19:04.276205 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -959 finished +------------------------------ ---------------- +epoch -959 +replay_buffer/size 999047 +trainer/num train calls 42000 +trainer/QF1 Loss 0.892929 +trainer/QF2 Loss 0.959325 +trainer/Policy Loss 22.3616 +trainer/Q1 Predictions Mean -68.1271 +trainer/Q1 Predictions Std 17.7107 +trainer/Q1 Predictions Max -0.94335 +trainer/Q1 Predictions Min -86.8157 +trainer/Q2 Predictions Mean -68.09 +trainer/Q2 Predictions Std 17.7145 +trainer/Q2 Predictions Max -1.12205 +trainer/Q2 Predictions Min -85.9663 +trainer/Q Targets Mean -67.9568 +trainer/Q Targets Std 17.5848 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1772 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00399937 +trainer/policy/mean Std 0.666264 +trainer/policy/mean Max 0.993347 +trainer/policy/mean Min -0.99694 +trainer/policy/std Mean 0.476384 +trainer/policy/std Std 0.027405 +trainer/policy/std Max 0.507684 +trainer/policy/std Min 0.436392 +trainer/Advantage Weights Mean 4.00825 +trainer/Advantage Weights Std 17.981 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.04755e-11 +trainer/Advantage Score Mean -0.413937 +trainer/Advantage Score Std 0.456858 +trainer/Advantage Score Max 1.01119 +trainer/Advantage Score Min -2.5282 +trainer/V1 Predictions Mean -67.6851 +trainer/V1 Predictions Std 17.6378 +trainer/V1 Predictions Max -1.33575 +trainer/V1 Predictions Min -86.18 +trainer/VF Loss 0.0542908 +expl/num steps total 42000 +expl/num paths total 42 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00675559 +expl/Actions Std 0.748415 +expl/Actions Max 2.65481 +expl/Actions Min -2.19096 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 41701 +eval/num paths total 42 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00441223 +eval/Actions Std 0.651567 +eval/Actions Max 0.998961 +eval/Actions Min -0.998613 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.89222e-06 +time/evaluation sampling (s) 2.98502 +time/exploration sampling (s) 3.63049 +time/logging (s) 0.00824352 +time/saving (s) 0.0166497 +time/training (s) 15.826 +time/epoch (s) 22.4664 +time/total (s) 998.556 +Epoch -959 +------------------------------ ---------------- +2022-05-15 18:19:26.859689 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -958 finished +------------------------------ ---------------- +epoch -958 +replay_buffer/size 999047 +trainer/num train calls 43000 +trainer/QF1 Loss 0.806098 +trainer/QF2 Loss 0.82073 +trainer/Policy Loss 7.66055 +trainer/Q1 Predictions Mean -67.0565 +trainer/Q1 Predictions Std 19.6042 +trainer/Q1 Predictions Max -3.27072 +trainer/Q1 Predictions Min -85.2369 +trainer/Q2 Predictions Mean -67.0028 +trainer/Q2 Predictions Std 19.7135 +trainer/Q2 Predictions Max -2.81312 +trainer/Q2 Predictions Min -85.2447 +trainer/Q Targets Mean -66.863 +trainer/Q Targets Std 19.5617 +trainer/Q Targets Max 0 +trainer/Q Targets Min -84.6967 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0117673 +trainer/policy/mean Std 0.681333 +trainer/policy/mean Max 0.997263 +trainer/policy/mean Min -0.996534 +trainer/policy/std Mean 0.47531 +trainer/policy/std Std 0.0268221 +trainer/policy/std Max 0.509326 +trainer/policy/std Min 0.439035 +trainer/Advantage Weights Mean 1.33853 +trainer/Advantage Weights Std 9.18946 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.46173e-13 +trainer/Advantage Score Mean -0.562455 +trainer/Advantage Score Std 0.522652 +trainer/Advantage Score Max 0.977547 +trainer/Advantage Score Min -2.9554 +trainer/V1 Predictions Mean -66.5753 +trainer/V1 Predictions Std 19.7066 +trainer/V1 Predictions Max -1.59268 +trainer/V1 Predictions Min -84.5593 +trainer/VF Loss 0.0654259 +expl/num steps total 43000 +expl/num paths total 43 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0115468 +expl/Actions Std 0.771035 +expl/Actions Max 2.32505 +expl/Actions Min -2.34393 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 42701 +eval/num paths total 43 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0799356 +eval/Actions Std 0.615649 +eval/Actions Max 0.987697 +eval/Actions Min -0.996931 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66498e-06 +time/evaluation sampling (s) 3.16188 +time/exploration sampling (s) 3.67132 +time/logging (s) 0.00738441 +time/saving (s) 0.0107056 +time/training (s) 15.7249 +time/epoch (s) 22.5762 +time/total (s) 1021.14 +Epoch -958 +------------------------------ ---------------- +2022-05-15 18:19:49.320793 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -957 finished +------------------------------ ---------------- +epoch -957 +replay_buffer/size 999047 +trainer/num train calls 44000 +trainer/QF1 Loss 1.51114 +trainer/QF2 Loss 1.47928 +trainer/Policy Loss 51.6967 +trainer/Q1 Predictions Mean -67.0937 +trainer/Q1 Predictions Std 19.1452 +trainer/Q1 Predictions Max -2.48491 +trainer/Q1 Predictions Min -87.5697 +trainer/Q2 Predictions Mean -67.0743 +trainer/Q2 Predictions Std 19.1247 +trainer/Q2 Predictions Max -2.37875 +trainer/Q2 Predictions Min -87.2858 +trainer/Q Targets Mean -67.2012 +trainer/Q Targets Std 19.5505 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.8185 +trainer/rewards Mean -0.972656 +trainer/rewards Std 0.163083 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0273438 +trainer/terminals Std 0.163083 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0342768 +trainer/policy/mean Std 0.666259 +trainer/policy/mean Max 0.993132 +trainer/policy/mean Min -0.99608 +trainer/policy/std Mean 0.474279 +trainer/policy/std Std 0.0263413 +trainer/policy/std Max 0.508203 +trainer/policy/std Min 0.433961 +trainer/Advantage Weights Mean 8.29068 +trainer/Advantage Weights Std 23.9698 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.41047e-09 +trainer/Advantage Score Mean -0.258047 +trainer/Advantage Score Std 0.489278 +trainer/Advantage Score Max 0.871244 +trainer/Advantage Score Min -1.85938 +trainer/V1 Predictions Mean -67.0386 +trainer/V1 Predictions Std 19.1723 +trainer/V1 Predictions Max -3.03855 +trainer/V1 Predictions Min -87.8317 +trainer/VF Loss 0.0568043 +expl/num steps total 44000 +expl/num paths total 44 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0167724 +expl/Actions Std 0.82755 +expl/Actions Max 2.59206 +expl/Actions Min -2.37617 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 43701 +eval/num paths total 44 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0258598 +eval/Actions Std 0.621278 +eval/Actions Max 0.994725 +eval/Actions Min -0.995112 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.92622e-06 +time/evaluation sampling (s) 2.82424 +time/exploration sampling (s) 3.74497 +time/logging (s) 0.00895916 +time/saving (s) 0.0125915 +time/training (s) 15.8662 +time/epoch (s) 22.457 +time/total (s) 1043.6 +Epoch -957 +------------------------------ ---------------- +2022-05-15 18:20:12.370373 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -956 finished +------------------------------ ---------------- +epoch -956 +replay_buffer/size 999047 +trainer/num train calls 45000 +trainer/QF1 Loss 1.9202 +trainer/QF2 Loss 2.00739 +trainer/Policy Loss 25.217 +trainer/Q1 Predictions Mean -66.3841 +trainer/Q1 Predictions Std 18.6552 +trainer/Q1 Predictions Max -3.59584 +trainer/Q1 Predictions Min -85.0399 +trainer/Q2 Predictions Mean -66.3765 +trainer/Q2 Predictions Std 18.6851 +trainer/Q2 Predictions Max -3.49432 +trainer/Q2 Predictions Min -85.3785 +trainer/Q Targets Mean -66.6847 +trainer/Q Targets Std 18.6602 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.2771 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0198789 +trainer/policy/mean Std 0.660545 +trainer/policy/mean Max 0.99421 +trainer/policy/mean Min -0.995036 +trainer/policy/std Mean 0.473563 +trainer/policy/std Std 0.0277627 +trainer/policy/std Max 0.508233 +trainer/policy/std Min 0.43208 +trainer/Advantage Weights Mean 4.1533 +trainer/Advantage Weights Std 17.8939 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.84474e-12 +trainer/Advantage Score Mean -0.487783 +trainer/Advantage Score Std 0.475203 +trainer/Advantage Score Max 0.744698 +trainer/Advantage Score Min -2.58655 +trainer/V1 Predictions Mean -66.2847 +trainer/V1 Predictions Std 18.7086 +trainer/V1 Predictions Max -2.0599 +trainer/V1 Predictions Min -85.4137 +trainer/VF Loss 0.0574996 +expl/num steps total 45000 +expl/num paths total 45 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00257324 +expl/Actions Std 0.756708 +expl/Actions Max 2.21556 +expl/Actions Min -2.58258 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 44701 +eval/num paths total 45 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0164776 +eval/Actions Std 0.636135 +eval/Actions Max 0.994295 +eval/Actions Min -0.994263 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.5942e-06 +time/evaluation sampling (s) 3.04185 +time/exploration sampling (s) 3.75046 +time/logging (s) 0.00689583 +time/saving (s) 0.0117393 +time/training (s) 16.2315 +time/epoch (s) 23.0425 +time/total (s) 1066.65 +Epoch -956 +------------------------------ ---------------- +2022-05-15 18:20:35.223746 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -955 finished +------------------------------ ---------------- +epoch -955 +replay_buffer/size 999047 +trainer/num train calls 46000 +trainer/QF1 Loss 1.2138 +trainer/QF2 Loss 1.47371 +trainer/Policy Loss 28.6953 +trainer/Q1 Predictions Mean -67.0677 +trainer/Q1 Predictions Std 19.8422 +trainer/Q1 Predictions Max -3.51347 +trainer/Q1 Predictions Min -85.1839 +trainer/Q2 Predictions Mean -67.1937 +trainer/Q2 Predictions Std 19.8064 +trainer/Q2 Predictions Max -3.58144 +trainer/Q2 Predictions Min -85.2756 +trainer/Q Targets Mean -66.4685 +trainer/Q Targets Std 20.2106 +trainer/Q Targets Max 0 +trainer/Q Targets Min -84.9922 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0406539 +trainer/policy/mean Std 0.671977 +trainer/policy/mean Max 0.996431 +trainer/policy/mean Min -0.99455 +trainer/policy/std Mean 0.472772 +trainer/policy/std Std 0.0275189 +trainer/policy/std Max 0.506727 +trainer/policy/std Min 0.429906 +trainer/Advantage Weights Mean 5.7009 +trainer/Advantage Weights Std 19.8704 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.4176e-13 +trainer/Advantage Score Mean -0.352996 +trainer/Advantage Score Std 0.473556 +trainer/Advantage Score Max 1.0238 +trainer/Advantage Score Min -2.8448 +trainer/V1 Predictions Mean -66.2765 +trainer/V1 Predictions Std 20.1223 +trainer/V1 Predictions Max -2.65167 +trainer/V1 Predictions Min -84.8347 +trainer/VF Loss 0.0515946 +expl/num steps total 46000 +expl/num paths total 46 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0214552 +expl/Actions Std 0.736658 +expl/Actions Max 2.60507 +expl/Actions Min -2.27368 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 45701 +eval/num paths total 46 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0012539 +eval/Actions Std 0.671534 +eval/Actions Max 0.99636 +eval/Actions Min -0.995002 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.74926e-06 +time/evaluation sampling (s) 3.09364 +time/exploration sampling (s) 4.04836 +time/logging (s) 0.0083035 +time/saving (s) 0.0103108 +time/training (s) 15.6881 +time/epoch (s) 22.8487 +time/total (s) 1089.5 +Epoch -955 +------------------------------ ---------------- +2022-05-15 18:20:57.275447 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -954 finished +------------------------------ ---------------- +epoch -954 +replay_buffer/size 999047 +trainer/num train calls 47000 +trainer/QF1 Loss 1.74595 +trainer/QF2 Loss 1.79117 +trainer/Policy Loss 20.261 +trainer/Q1 Predictions Mean -68.547 +trainer/Q1 Predictions Std 18.5794 +trainer/Q1 Predictions Max -3.75931 +trainer/Q1 Predictions Min -86.6592 +trainer/Q2 Predictions Mean -68.632 +trainer/Q2 Predictions Std 18.5514 +trainer/Q2 Predictions Max -3.81265 +trainer/Q2 Predictions Min -86.8496 +trainer/Q Targets Mean -67.9122 +trainer/Q Targets Std 18.8352 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.091 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0245701 +trainer/policy/mean Std 0.665973 +trainer/policy/mean Max 0.996328 +trainer/policy/mean Min -0.997949 +trainer/policy/std Mean 0.47114 +trainer/policy/std Std 0.0271702 +trainer/policy/std Max 0.504295 +trainer/policy/std Min 0.429236 +trainer/Advantage Weights Mean 2.91215 +trainer/Advantage Weights Std 12.7338 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.04641e-18 +trainer/Advantage Score Mean -0.424304 +trainer/Advantage Score Std 0.499578 +trainer/Advantage Score Max 0.685165 +trainer/Advantage Score Min -4.07304 +trainer/V1 Predictions Mean -67.6974 +trainer/V1 Predictions Std 18.842 +trainer/V1 Predictions Max -3.31532 +trainer/V1 Predictions Min -85.9506 +trainer/VF Loss 0.0510882 +expl/num steps total 47000 +expl/num paths total 47 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0091819 +expl/Actions Std 0.761027 +expl/Actions Max 2.50857 +expl/Actions Min -2.52236 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 46701 +eval/num paths total 47 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00146612 +eval/Actions Std 0.632853 +eval/Actions Max 0.993774 +eval/Actions Min -0.996801 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.54996e-06 +time/evaluation sampling (s) 2.91644 +time/exploration sampling (s) 3.78129 +time/logging (s) 0.00735181 +time/saving (s) 0.0120233 +time/training (s) 15.3283 +time/epoch (s) 22.0454 +time/total (s) 1111.55 +Epoch -954 +------------------------------ ---------------- +2022-05-15 18:21:19.801045 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -953 finished +------------------------------ ---------------- +epoch -953 +replay_buffer/size 999047 +trainer/num train calls 48000 +trainer/QF1 Loss 1.72702 +trainer/QF2 Loss 1.62999 +trainer/Policy Loss 5.15117 +trainer/Q1 Predictions Mean -69.5827 +trainer/Q1 Predictions Std 18.0465 +trainer/Q1 Predictions Max -3.8557 +trainer/Q1 Predictions Min -84.5962 +trainer/Q2 Predictions Mean -69.5576 +trainer/Q2 Predictions Std 18.0415 +trainer/Q2 Predictions Max -3.98623 +trainer/Q2 Predictions Min -84.5681 +trainer/Q Targets Mean -68.8675 +trainer/Q Targets Std 18.5282 +trainer/Q Targets Max 0 +trainer/Q Targets Min -84.7884 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0234965 +trainer/policy/mean Std 0.667998 +trainer/policy/mean Max 0.992367 +trainer/policy/mean Min -0.991938 +trainer/policy/std Mean 0.47094 +trainer/policy/std Std 0.0272295 +trainer/policy/std Max 0.503967 +trainer/policy/std Min 0.429559 +trainer/Advantage Weights Mean 0.707069 +trainer/Advantage Weights Std 6.60775 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.89767e-15 +trainer/Advantage Score Mean -0.672447 +trainer/Advantage Score Std 0.546812 +trainer/Advantage Score Max 0.696411 +trainer/Advantage Score Min -3.31784 +trainer/V1 Predictions Mean -68.6295 +trainer/V1 Predictions Std 18.4652 +trainer/V1 Predictions Max -2.88104 +trainer/V1 Predictions Min -84.5412 +trainer/VF Loss 0.0775007 +expl/num steps total 48000 +expl/num paths total 48 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0577708 +expl/Actions Std 0.797386 +expl/Actions Max 2.42386 +expl/Actions Min -2.38512 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 47701 +eval/num paths total 48 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.111714 +eval/Actions Std 0.650484 +eval/Actions Max 0.994871 +eval/Actions Min -0.995373 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.68897e-06 +time/evaluation sampling (s) 3.08023 +time/exploration sampling (s) 4.09738 +time/logging (s) 0.00707595 +time/saving (s) 0.0109492 +time/training (s) 15.3242 +time/epoch (s) 22.5199 +time/total (s) 1134.07 +Epoch -953 +------------------------------ ---------------- +2022-05-15 18:21:42.865711 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -952 finished +------------------------------ ---------------- +epoch -952 +replay_buffer/size 999047 +trainer/num train calls 49000 +trainer/QF1 Loss 1.08058 +trainer/QF2 Loss 1.22386 +trainer/Policy Loss 9.30083 +trainer/Q1 Predictions Mean -68.6213 +trainer/Q1 Predictions Std 18.3058 +trainer/Q1 Predictions Max -3.82559 +trainer/Q1 Predictions Min -85.7235 +trainer/Q2 Predictions Mean -68.7114 +trainer/Q2 Predictions Std 18.2517 +trainer/Q2 Predictions Max -3.9407 +trainer/Q2 Predictions Min -85.8091 +trainer/Q Targets Mean -68.4256 +trainer/Q Targets Std 18.4549 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.699 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0393983 +trainer/policy/mean Std 0.67206 +trainer/policy/mean Max 0.998542 +trainer/policy/mean Min -0.993122 +trainer/policy/std Mean 0.467991 +trainer/policy/std Std 0.0267254 +trainer/policy/std Max 0.499963 +trainer/policy/std Min 0.428231 +trainer/Advantage Weights Mean 1.34796 +trainer/Advantage Weights Std 9.35941 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.57331e-19 +trainer/Advantage Score Mean -0.689903 +trainer/Advantage Score Std 0.516459 +trainer/Advantage Score Max 0.503544 +trainer/Advantage Score Min -4.24756 +trainer/V1 Predictions Mean -68.1713 +trainer/V1 Predictions Std 18.3598 +trainer/V1 Predictions Max -2.72348 +trainer/V1 Predictions Min -85.7878 +trainer/VF Loss 0.0775885 +expl/num steps total 49000 +expl/num paths total 49 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0198909 +expl/Actions Std 0.786576 +expl/Actions Max 2.18886 +expl/Actions Min -2.4801 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 48701 +eval/num paths total 49 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00173854 +eval/Actions Std 0.63583 +eval/Actions Max 0.992702 +eval/Actions Min -0.996393 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.87406e-06 +time/evaluation sampling (s) 3.1633 +time/exploration sampling (s) 4.29076 +time/logging (s) 0.00693312 +time/saving (s) 0.0105233 +time/training (s) 15.5871 +time/epoch (s) 23.0586 +time/total (s) 1157.14 +Epoch -952 +------------------------------ ---------------- +2022-05-15 18:22:05.478519 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -951 finished +------------------------------ ---------------- +epoch -951 +replay_buffer/size 999047 +trainer/num train calls 50000 +trainer/QF1 Loss 0.451747 +trainer/QF2 Loss 0.464746 +trainer/Policy Loss 32.9553 +trainer/Q1 Predictions Mean -68.6595 +trainer/Q1 Predictions Std 18.5017 +trainer/Q1 Predictions Max -4.47364 +trainer/Q1 Predictions Min -87.4182 +trainer/Q2 Predictions Mean -68.7464 +trainer/Q2 Predictions Std 18.4736 +trainer/Q2 Predictions Max -4.63354 +trainer/Q2 Predictions Min -86.9845 +trainer/Q Targets Mean -68.7363 +trainer/Q Targets Std 18.4702 +trainer/Q Targets Max -5.4631 +trainer/Q Targets Min -86.5216 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0161663 +trainer/policy/mean Std 0.672723 +trainer/policy/mean Max 0.996803 +trainer/policy/mean Min -0.991722 +trainer/policy/std Mean 0.46822 +trainer/policy/std Std 0.0252281 +trainer/policy/std Max 0.497791 +trainer/policy/std Min 0.429704 +trainer/Advantage Weights Mean 5.42814 +trainer/Advantage Weights Std 20.3945 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.29584e-11 +trainer/Advantage Score Mean -0.262261 +trainer/Advantage Score Std 0.408523 +trainer/Advantage Score Max 0.945886 +trainer/Advantage Score Min -2.30989 +trainer/V1 Predictions Mean -68.4572 +trainer/V1 Predictions Std 18.5324 +trainer/V1 Predictions Max -5.64378 +trainer/V1 Predictions Min -86.5422 +trainer/VF Loss 0.0422501 +expl/num steps total 50000 +expl/num paths total 50 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0455345 +expl/Actions Std 0.787931 +expl/Actions Max 2.27067 +expl/Actions Min -2.43982 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 49701 +eval/num paths total 50 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0166288 +eval/Actions Std 0.692213 +eval/Actions Max 0.995126 +eval/Actions Min -0.998127 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.879e-06 +time/evaluation sampling (s) 3.38916 +time/exploration sampling (s) 3.63974 +time/logging (s) 0.00905185 +time/saving (s) 0.0128146 +time/training (s) 15.558 +time/epoch (s) 22.6087 +time/total (s) 1179.75 +Epoch -951 +------------------------------ ---------------- +2022-05-15 18:22:28.424846 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -950 finished +------------------------------ ---------------- +epoch -950 +replay_buffer/size 999047 +trainer/num train calls 51000 +trainer/QF1 Loss 0.6367 +trainer/QF2 Loss 0.654973 +trainer/Policy Loss 30.8229 +trainer/Q1 Predictions Mean -68.3685 +trainer/Q1 Predictions Std 18.7828 +trainer/Q1 Predictions Max -1.66078 +trainer/Q1 Predictions Min -85.3185 +trainer/Q2 Predictions Mean -68.4427 +trainer/Q2 Predictions Std 18.7537 +trainer/Q2 Predictions Max -1.2984 +trainer/Q2 Predictions Min -86.0856 +trainer/Q Targets Mean -68.3758 +trainer/Q Targets Std 18.7444 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4545 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0112316 +trainer/policy/mean Std 0.660609 +trainer/policy/mean Max 0.991953 +trainer/policy/mean Min -0.993269 +trainer/policy/std Mean 0.468624 +trainer/policy/std Std 0.0254783 +trainer/policy/std Max 0.500451 +trainer/policy/std Min 0.429196 +trainer/Advantage Weights Mean 4.18961 +trainer/Advantage Weights Std 17.2631 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.70428e-09 +trainer/Advantage Score Mean -0.384612 +trainer/Advantage Score Std 0.447392 +trainer/Advantage Score Max 0.999306 +trainer/Advantage Score Min -2.01901 +trainer/V1 Predictions Mean -68.1344 +trainer/V1 Predictions Std 18.7811 +trainer/V1 Predictions Max -0.99556 +trainer/V1 Predictions Min -85.868 +trainer/VF Loss 0.048404 +expl/num steps total 51000 +expl/num paths total 51 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.012741 +expl/Actions Std 0.790886 +expl/Actions Max 2.34765 +expl/Actions Min -2.45319 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 50701 +eval/num paths total 51 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0713778 +eval/Actions Std 0.636912 +eval/Actions Max 0.996844 +eval/Actions Min -0.994709 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.79024e-06 +time/evaluation sampling (s) 3.44625 +time/exploration sampling (s) 3.97469 +time/logging (s) 0.00748763 +time/saving (s) 0.0197974 +time/training (s) 15.4898 +time/epoch (s) 22.938 +time/total (s) 1202.69 +Epoch -950 +------------------------------ ---------------- +2022-05-15 18:22:50.875088 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -949 finished +------------------------------ ---------------- +epoch -949 +replay_buffer/size 999047 +trainer/num train calls 52000 +trainer/QF1 Loss 1.39478 +trainer/QF2 Loss 1.39361 +trainer/Policy Loss 3.31834 +trainer/Q1 Predictions Mean -66.2608 +trainer/Q1 Predictions Std 21.8505 +trainer/Q1 Predictions Max -3.07926 +trainer/Q1 Predictions Min -86.8614 +trainer/Q2 Predictions Mean -66.1784 +trainer/Q2 Predictions Std 21.8069 +trainer/Q2 Predictions Max -3.11219 +trainer/Q2 Predictions Min -87.9149 +trainer/Q Targets Mean -65.583 +trainer/Q Targets Std 21.9413 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1627 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0156299 +trainer/policy/mean Std 0.672617 +trainer/policy/mean Max 0.994243 +trainer/policy/mean Min -0.99652 +trainer/policy/std Mean 0.468619 +trainer/policy/std Std 0.026217 +trainer/policy/std Max 0.501363 +trainer/policy/std Min 0.428763 +trainer/Advantage Weights Mean 0.88267 +trainer/Advantage Weights Std 7.58139 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.07916e-18 +trainer/Advantage Score Mean -0.744679 +trainer/Advantage Score Std 0.538478 +trainer/Advantage Score Max 0.474972 +trainer/Advantage Score Min -4.13704 +trainer/V1 Predictions Mean -65.2733 +trainer/V1 Predictions Std 22.1418 +trainer/V1 Predictions Max -1.83588 +trainer/V1 Predictions Min -86.8962 +trainer/VF Loss 0.086451 +expl/num steps total 52000 +expl/num paths total 52 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.143457 +expl/Actions Std 0.816065 +expl/Actions Max 2.49035 +expl/Actions Min -2.29616 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 51701 +eval/num paths total 52 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00921632 +eval/Actions Std 0.690447 +eval/Actions Max 0.99674 +eval/Actions Min -0.997755 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.12809e-06 +time/evaluation sampling (s) 3.03684 +time/exploration sampling (s) 3.81062 +time/logging (s) 0.0106731 +time/saving (s) 0.0183247 +time/training (s) 15.5714 +time/epoch (s) 22.4479 +time/total (s) 1225.15 +Epoch -949 +------------------------------ ---------------- +2022-05-15 18:23:13.870054 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -948 finished +------------------------------ ---------------- +epoch -948 +replay_buffer/size 999047 +trainer/num train calls 53000 +trainer/QF1 Loss 2.98596 +trainer/QF2 Loss 2.8001 +trainer/Policy Loss 183.473 +trainer/Q1 Predictions Mean -68.0043 +trainer/Q1 Predictions Std 18.9961 +trainer/Q1 Predictions Max -2.77465 +trainer/Q1 Predictions Min -85.9528 +trainer/Q2 Predictions Mean -68.0425 +trainer/Q2 Predictions Std 18.998 +trainer/Q2 Predictions Max -2.33625 +trainer/Q2 Predictions Min -86.0601 +trainer/Q Targets Mean -69.0382 +trainer/Q Targets Std 19.3201 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5082 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0255893 +trainer/policy/mean Std 0.666415 +trainer/policy/mean Max 0.99547 +trainer/policy/mean Min -0.995472 +trainer/policy/std Mean 0.466368 +trainer/policy/std Std 0.0270299 +trainer/policy/std Max 0.501592 +trainer/policy/std Min 0.426295 +trainer/Advantage Weights Mean 36.6576 +trainer/Advantage Weights Std 43.9229 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.12583e-09 +trainer/Advantage Score Mean 0.148514 +trainer/Advantage Score Std 0.555822 +trainer/Advantage Score Max 2.02655 +trainer/Advantage Score Min -1.89108 +trainer/V1 Predictions Mean -68.8753 +trainer/V1 Predictions Std 19.1455 +trainer/V1 Predictions Max -2.08213 +trainer/V1 Predictions Min -86.3697 +trainer/VF Loss 0.199845 +expl/num steps total 53000 +expl/num paths total 53 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0268462 +expl/Actions Std 0.750834 +expl/Actions Max 2.42479 +expl/Actions Min -2.13718 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 52701 +eval/num paths total 53 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.128913 +eval/Actions Std 0.637516 +eval/Actions Max 0.996335 +eval/Actions Min -0.990821 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.06824e-06 +time/evaluation sampling (s) 3.43275 +time/exploration sampling (s) 4.03464 +time/logging (s) 0.00935564 +time/saving (s) 0.0146834 +time/training (s) 15.4923 +time/epoch (s) 22.9837 +time/total (s) 1248.14 +Epoch -948 +------------------------------ ---------------- +2022-05-15 18:23:36.150530 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -947 finished +------------------------------ ---------------- +epoch -947 +replay_buffer/size 999047 +trainer/num train calls 54000 +trainer/QF1 Loss 0.823755 +trainer/QF2 Loss 0.833455 +trainer/Policy Loss 18.1992 +trainer/Q1 Predictions Mean -67.8325 +trainer/Q1 Predictions Std 20.349 +trainer/Q1 Predictions Max -2.88706 +trainer/Q1 Predictions Min -89.1437 +trainer/Q2 Predictions Mean -67.8242 +trainer/Q2 Predictions Std 20.3502 +trainer/Q2 Predictions Max -2.69899 +trainer/Q2 Predictions Min -89.2828 +trainer/Q Targets Mean -67.5931 +trainer/Q Targets Std 20.482 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.17 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0218862 +trainer/policy/mean Std 0.676275 +trainer/policy/mean Max 0.997374 +trainer/policy/mean Min -0.9885 +trainer/policy/std Mean 0.467428 +trainer/policy/std Std 0.0267009 +trainer/policy/std Max 0.499085 +trainer/policy/std Min 0.428269 +trainer/Advantage Weights Mean 3.81538 +trainer/Advantage Weights Std 16.592 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.56167e-15 +trainer/Advantage Score Mean -0.434425 +trainer/Advantage Score Std 0.561109 +trainer/Advantage Score Max 0.703918 +trainer/Advantage Score Min -3.30211 +trainer/V1 Predictions Mean -67.3091 +trainer/V1 Predictions Std 20.5613 +trainer/V1 Predictions Max -1.33457 +trainer/V1 Predictions Min -87.9512 +trainer/VF Loss 0.0607667 +expl/num steps total 54000 +expl/num paths total 54 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0198067 +expl/Actions Std 0.771575 +expl/Actions Max 2.57858 +expl/Actions Min -2.39325 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 53701 +eval/num paths total 54 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0151343 +eval/Actions Std 0.666118 +eval/Actions Max 0.993583 +eval/Actions Min -0.995348 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.81492e-06 +time/evaluation sampling (s) 3.21997 +time/exploration sampling (s) 3.62812 +time/logging (s) 0.0117867 +time/saving (s) 0.0184556 +time/training (s) 15.3956 +time/epoch (s) 22.2739 +time/total (s) 1270.42 +Epoch -947 +------------------------------ ---------------- +2022-05-15 18:23:57.487401 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -946 finished +------------------------------ ---------------- +epoch -946 +replay_buffer/size 999047 +trainer/num train calls 55000 +trainer/QF1 Loss 0.680605 +trainer/QF2 Loss 0.7036 +trainer/Policy Loss 15.498 +trainer/Q1 Predictions Mean -69.4076 +trainer/Q1 Predictions Std 17.7876 +trainer/Q1 Predictions Max -0.724685 +trainer/Q1 Predictions Min -87.3827 +trainer/Q2 Predictions Mean -69.3674 +trainer/Q2 Predictions Std 17.7313 +trainer/Q2 Predictions Max -0.970754 +trainer/Q2 Predictions Min -86.9218 +trainer/Q Targets Mean -69.0795 +trainer/Q Targets Std 17.887 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4078 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0127454 +trainer/policy/mean Std 0.667107 +trainer/policy/mean Max 0.997424 +trainer/policy/mean Min -0.997418 +trainer/policy/std Mean 0.466682 +trainer/policy/std Std 0.026535 +trainer/policy/std Max 0.496542 +trainer/policy/std Min 0.426906 +trainer/Advantage Weights Mean 3.19246 +trainer/Advantage Weights Std 13.1193 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.70132e-13 +trainer/Advantage Score Mean -0.452184 +trainer/Advantage Score Std 0.592331 +trainer/Advantage Score Max 0.768909 +trainer/Advantage Score Min -2.86249 +trainer/V1 Predictions Mean -68.7794 +trainer/V1 Predictions Std 18.0487 +trainer/V1 Predictions Max -0.604917 +trainer/V1 Predictions Min -86.2244 +trainer/VF Loss 0.0650319 +expl/num steps total 55000 +expl/num paths total 55 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00908603 +expl/Actions Std 0.778571 +expl/Actions Max 2.55134 +expl/Actions Min -2.45964 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 54701 +eval/num paths total 55 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0440627 +eval/Actions Std 0.521954 +eval/Actions Max 0.996374 +eval/Actions Min -0.997885 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.78374e-06 +time/evaluation sampling (s) 3.20065 +time/exploration sampling (s) 3.51293 +time/logging (s) 0.0121786 +time/saving (s) 0.0184485 +time/training (s) 14.5837 +time/epoch (s) 21.3279 +time/total (s) 1291.75 +Epoch -946 +------------------------------ ---------------- +2022-05-15 18:24:20.029159 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -945 finished +------------------------------ ---------------- +epoch -945 +replay_buffer/size 999047 +trainer/num train calls 56000 +trainer/QF1 Loss 1.46164 +trainer/QF2 Loss 1.4833 +trainer/Policy Loss 31.1495 +trainer/Q1 Predictions Mean -66.2648 +trainer/Q1 Predictions Std 21.1522 +trainer/Q1 Predictions Max -1.75622 +trainer/Q1 Predictions Min -87.4307 +trainer/Q2 Predictions Mean -66.2531 +trainer/Q2 Predictions Std 21.1282 +trainer/Q2 Predictions Max -1.80587 +trainer/Q2 Predictions Min -87.8359 +trainer/Q Targets Mean -66.4518 +trainer/Q Targets Std 21.1149 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1538 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0373119 +trainer/policy/mean Std 0.665877 +trainer/policy/mean Max 0.997948 +trainer/policy/mean Min -0.993196 +trainer/policy/std Mean 0.464375 +trainer/policy/std Std 0.0246733 +trainer/policy/std Max 0.495455 +trainer/policy/std Min 0.42903 +trainer/Advantage Weights Mean 6.33567 +trainer/Advantage Weights Std 21.3962 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.53382e-12 +trainer/Advantage Score Mean -0.264766 +trainer/Advantage Score Std 0.476957 +trainer/Advantage Score Max 1.11505 +trainer/Advantage Score Min -2.5487 +trainer/V1 Predictions Mean -66.1926 +trainer/V1 Predictions Std 21.2101 +trainer/V1 Predictions Max -2.26533 +trainer/V1 Predictions Min -86.8839 +trainer/VF Loss 0.0532145 +expl/num steps total 56000 +expl/num paths total 56 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.193988 +expl/Actions Std 0.799985 +expl/Actions Max 2.583 +expl/Actions Min -2.23406 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 55701 +eval/num paths total 56 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.200568 +eval/Actions Std 0.647053 +eval/Actions Max 0.993315 +eval/Actions Min -0.999376 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.73879e-06 +time/evaluation sampling (s) 3.12355 +time/exploration sampling (s) 3.70588 +time/logging (s) 0.0112268 +time/saving (s) 0.0157612 +time/training (s) 15.6761 +time/epoch (s) 22.5325 +time/total (s) 1314.29 +Epoch -945 +------------------------------ ---------------- +2022-05-15 18:24:42.422945 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -944 finished +------------------------------ ---------------- +epoch -944 +replay_buffer/size 999047 +trainer/num train calls 57000 +trainer/QF1 Loss 0.991711 +trainer/QF2 Loss 0.931801 +trainer/Policy Loss 15.1952 +trainer/Q1 Predictions Mean -67.8223 +trainer/Q1 Predictions Std 19.1534 +trainer/Q1 Predictions Max -1.4926 +trainer/Q1 Predictions Min -87.264 +trainer/Q2 Predictions Mean -67.8522 +trainer/Q2 Predictions Std 19.1511 +trainer/Q2 Predictions Max -1.69836 +trainer/Q2 Predictions Min -87.784 +trainer/Q Targets Mean -67.9147 +trainer/Q Targets Std 19.0994 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.469 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00846165 +trainer/policy/mean Std 0.677528 +trainer/policy/mean Max 0.998827 +trainer/policy/mean Min -0.994229 +trainer/policy/std Mean 0.465048 +trainer/policy/std Std 0.0262159 +trainer/policy/std Max 0.497876 +trainer/policy/std Min 0.42438 +trainer/Advantage Weights Mean 3.03328 +trainer/Advantage Weights Std 13.5412 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.23412e-18 +trainer/Advantage Score Mean -0.469549 +trainer/Advantage Score Std 0.568643 +trainer/Advantage Score Max 1.3804 +trainer/Advantage Score Min -3.93382 +trainer/V1 Predictions Mean -67.5599 +trainer/V1 Predictions Std 19.283 +trainer/V1 Predictions Max -0.940236 +trainer/V1 Predictions Min -87.3168 +trainer/VF Loss 0.0673799 +expl/num steps total 57000 +expl/num paths total 57 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0130917 +expl/Actions Std 0.809838 +expl/Actions Max 2.21729 +expl/Actions Min -2.59925 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 56701 +eval/num paths total 57 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00568691 +eval/Actions Std 0.632255 +eval/Actions Max 0.998616 +eval/Actions Min -0.996228 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83401e-06 +time/evaluation sampling (s) 3.09493 +time/exploration sampling (s) 3.67585 +time/logging (s) 0.011904 +time/saving (s) 0.0163336 +time/training (s) 15.5888 +time/epoch (s) 22.3878 +time/total (s) 1336.69 +Epoch -944 +------------------------------ ---------------- +2022-05-15 18:25:04.957077 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -943 finished +------------------------------ ---------------- +epoch -943 +replay_buffer/size 999047 +trainer/num train calls 58000 +trainer/QF1 Loss 1.34245 +trainer/QF2 Loss 1.46972 +trainer/Policy Loss 37.8295 +trainer/Q1 Predictions Mean -68.4818 +trainer/Q1 Predictions Std 19.6717 +trainer/Q1 Predictions Max -2.09334 +trainer/Q1 Predictions Min -86.7357 +trainer/Q2 Predictions Mean -68.4916 +trainer/Q2 Predictions Std 19.702 +trainer/Q2 Predictions Max -2.57137 +trainer/Q2 Predictions Min -86.9007 +trainer/Q Targets Mean -69.1181 +trainer/Q Targets Std 19.5618 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3622 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0150785 +trainer/policy/mean Std 0.682265 +trainer/policy/mean Max 0.994206 +trainer/policy/mean Min -0.992368 +trainer/policy/std Mean 0.465463 +trainer/policy/std Std 0.0256105 +trainer/policy/std Max 0.496563 +trainer/policy/std Min 0.428197 +trainer/Advantage Weights Mean 8.0775 +trainer/Advantage Weights Std 21.9754 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.87044e-11 +trainer/Advantage Score Mean -0.153722 +trainer/Advantage Score Std 0.453478 +trainer/Advantage Score Max 0.968988 +trainer/Advantage Score Min -2.47023 +trainer/V1 Predictions Mean -68.8326 +trainer/V1 Predictions Std 19.7087 +trainer/V1 Predictions Max -2.40643 +trainer/V1 Predictions Min -87.1739 +trainer/VF Loss 0.0491481 +expl/num steps total 58000 +expl/num paths total 58 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0162122 +expl/Actions Std 0.753036 +expl/Actions Max 2.44923 +expl/Actions Min -2.507 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 57701 +eval/num paths total 58 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.217717 +eval/Actions Std 0.720578 +eval/Actions Max 0.993694 +eval/Actions Min -0.995314 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.82122e-06 +time/evaluation sampling (s) 2.95492 +time/exploration sampling (s) 3.83692 +time/logging (s) 0.00980654 +time/saving (s) 0.0136806 +time/training (s) 15.709 +time/epoch (s) 22.5243 +time/total (s) 1359.22 +Epoch -943 +------------------------------ ---------------- +2022-05-15 18:25:27.465596 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -942 finished +------------------------------ ---------------- +epoch -942 +replay_buffer/size 999047 +trainer/num train calls 59000 +trainer/QF1 Loss 0.694268 +trainer/QF2 Loss 0.747514 +trainer/Policy Loss 17.9411 +trainer/Q1 Predictions Mean -68.8731 +trainer/Q1 Predictions Std 19.3061 +trainer/Q1 Predictions Max -2.73777 +trainer/Q1 Predictions Min -88.9303 +trainer/Q2 Predictions Mean -68.7786 +trainer/Q2 Predictions Std 19.2842 +trainer/Q2 Predictions Max -2.63307 +trainer/Q2 Predictions Min -88.7148 +trainer/Q Targets Mean -69.0056 +trainer/Q Targets Std 19.0355 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.0681 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0121301 +trainer/policy/mean Std 0.690841 +trainer/policy/mean Max 0.998074 +trainer/policy/mean Min -0.997679 +trainer/policy/std Mean 0.466304 +trainer/policy/std Std 0.0245933 +trainer/policy/std Max 0.496675 +trainer/policy/std Min 0.427591 +trainer/Advantage Weights Mean 3.59415 +trainer/Advantage Weights Std 15.1882 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.80581e-09 +trainer/Advantage Score Mean -0.301807 +trainer/Advantage Score Std 0.396704 +trainer/Advantage Score Max 1.07346 +trainer/Advantage Score Min -2.01323 +trainer/V1 Predictions Mean -68.6726 +trainer/V1 Predictions Std 19.228 +trainer/V1 Predictions Max -2.23757 +trainer/V1 Predictions Min -88.1453 +trainer/VF Loss 0.038005 +expl/num steps total 59000 +expl/num paths total 59 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.315219 +expl/Actions Std 0.80985 +expl/Actions Max 2.71375 +expl/Actions Min -2.37276 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 58701 +eval/num paths total 59 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.018508 +eval/Actions Std 0.689586 +eval/Actions Max 0.995923 +eval/Actions Min -0.99688 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95788e-06 +time/evaluation sampling (s) 3.05667 +time/exploration sampling (s) 3.61168 +time/logging (s) 0.0110816 +time/saving (s) 0.0154207 +time/training (s) 15.8043 +time/epoch (s) 22.4992 +time/total (s) 1381.72 +Epoch -942 +------------------------------ ---------------- +2022-05-15 18:25:49.778986 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -941 finished +------------------------------ ---------------- +epoch -941 +replay_buffer/size 999047 +trainer/num train calls 60000 +trainer/QF1 Loss 1.57743 +trainer/QF2 Loss 1.63567 +trainer/Policy Loss 56.1252 +trainer/Q1 Predictions Mean -67.304 +trainer/Q1 Predictions Std 20.1863 +trainer/Q1 Predictions Max -2.63771 +trainer/Q1 Predictions Min -87.3455 +trainer/Q2 Predictions Mean -67.282 +trainer/Q2 Predictions Std 20.1495 +trainer/Q2 Predictions Max -2.63772 +trainer/Q2 Predictions Min -87.4999 +trainer/Q Targets Mean -68.134 +trainer/Q Targets Std 20.2291 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.1236 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00222012 +trainer/policy/mean Std 0.677869 +trainer/policy/mean Max 0.997828 +trainer/policy/mean Min -0.99808 +trainer/policy/std Mean 0.464851 +trainer/policy/std Std 0.025684 +trainer/policy/std Max 0.498749 +trainer/policy/std Min 0.427129 +trainer/Advantage Weights Mean 12.4097 +trainer/Advantage Weights Std 26.6569 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.23025e-08 +trainer/Advantage Score Mean -0.0576065 +trainer/Advantage Score Std 0.457345 +trainer/Advantage Score Max 2.23731 +trainer/Advantage Score Min -1.82135 +trainer/V1 Predictions Mean -67.8774 +trainer/V1 Predictions Std 20.273 +trainer/V1 Predictions Max -2.67017 +trainer/V1 Predictions Min -88.2813 +trainer/VF Loss 0.0805285 +expl/num steps total 60000 +expl/num paths total 60 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00458206 +expl/Actions Std 0.783678 +expl/Actions Max 2.34069 +expl/Actions Min -2.50125 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 59701 +eval/num paths total 60 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.224699 +eval/Actions Std 0.668303 +eval/Actions Max 0.992144 +eval/Actions Min -0.995923 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93273e-06 +time/evaluation sampling (s) 3.04165 +time/exploration sampling (s) 3.65129 +time/logging (s) 0.00739531 +time/saving (s) 0.0114902 +time/training (s) 15.5892 +time/epoch (s) 22.301 +time/total (s) 1404.03 +Epoch -941 +------------------------------ ---------------- +2022-05-15 18:26:12.528898 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -940 finished +------------------------------ ---------------- +epoch -940 +replay_buffer/size 999047 +trainer/num train calls 61000 +trainer/QF1 Loss 1.86551 +trainer/QF2 Loss 2.14977 +trainer/Policy Loss 32.6083 +trainer/Q1 Predictions Mean -69.937 +trainer/Q1 Predictions Std 18.0608 +trainer/Q1 Predictions Max -1.05013 +trainer/Q1 Predictions Min -86.9778 +trainer/Q2 Predictions Mean -69.7988 +trainer/Q2 Predictions Std 18.1334 +trainer/Q2 Predictions Max -0.881798 +trainer/Q2 Predictions Min -86.799 +trainer/Q Targets Mean -70.2321 +trainer/Q Targets Std 18.1755 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4412 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0273479 +trainer/policy/mean Std 0.688692 +trainer/policy/mean Max 0.996788 +trainer/policy/mean Min -0.996574 +trainer/policy/std Mean 0.463761 +trainer/policy/std Std 0.026313 +trainer/policy/std Max 0.497557 +trainer/policy/std Min 0.425466 +trainer/Advantage Weights Mean 6.13676 +trainer/Advantage Weights Std 20.7 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.15115e-10 +trainer/Advantage Score Mean -0.268371 +trainer/Advantage Score Std 0.474308 +trainer/Advantage Score Max 1.12045 +trainer/Advantage Score Min -2.28851 +trainer/V1 Predictions Mean -70.0021 +trainer/V1 Predictions Std 18.1007 +trainer/V1 Predictions Max -1.0592 +trainer/V1 Predictions Min -87.0796 +trainer/VF Loss 0.0551207 +expl/num steps total 61000 +expl/num paths total 61 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.205438 +expl/Actions Std 0.82128 +expl/Actions Max 2.34765 +expl/Actions Min -2.51216 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 60701 +eval/num paths total 61 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.171565 +eval/Actions Std 0.656212 +eval/Actions Max 0.993361 +eval/Actions Min -0.993118 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.87197e-06 +time/evaluation sampling (s) 3.02909 +time/exploration sampling (s) 3.77116 +time/logging (s) 0.00738733 +time/saving (s) 0.0153933 +time/training (s) 15.9204 +time/epoch (s) 22.7434 +time/total (s) 1426.78 +Epoch -940 +------------------------------ ---------------- +2022-05-15 18:26:35.332569 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -939 finished +------------------------------ ---------------- +epoch -939 +replay_buffer/size 999047 +trainer/num train calls 62000 +trainer/QF1 Loss 0.733874 +trainer/QF2 Loss 0.67704 +trainer/Policy Loss 11.8739 +trainer/Q1 Predictions Mean -69.8742 +trainer/Q1 Predictions Std 17.1942 +trainer/Q1 Predictions Max -8.69183 +trainer/Q1 Predictions Min -88.1444 +trainer/Q2 Predictions Mean -69.9547 +trainer/Q2 Predictions Std 17.1415 +trainer/Q2 Predictions Max -9.05925 +trainer/Q2 Predictions Min -88.1537 +trainer/Q Targets Mean -69.7503 +trainer/Q Targets Std 17.2902 +trainer/Q Targets Max -7.90007 +trainer/Q Targets Min -87.8048 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0171612 +trainer/policy/mean Std 0.677574 +trainer/policy/mean Max 0.99815 +trainer/policy/mean Min -0.992878 +trainer/policy/std Mean 0.462576 +trainer/policy/std Std 0.0269837 +trainer/policy/std Max 0.498548 +trainer/policy/std Min 0.421524 +trainer/Advantage Weights Mean 2.02907 +trainer/Advantage Weights Std 11.0267 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.7617e-13 +trainer/Advantage Score Mean -0.462693 +trainer/Advantage Score Std 0.518256 +trainer/Advantage Score Max 0.993065 +trainer/Advantage Score Min -2.81824 +trainer/V1 Predictions Mean -69.4611 +trainer/V1 Predictions Std 17.4376 +trainer/V1 Predictions Max -9.03298 +trainer/V1 Predictions Min -87.5724 +trainer/VF Loss 0.0556742 +expl/num steps total 62000 +expl/num paths total 62 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.215725 +expl/Actions Std 0.814984 +expl/Actions Max 2.60271 +expl/Actions Min -2.12922 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 61701 +eval/num paths total 62 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0536235 +eval/Actions Std 0.705397 +eval/Actions Max 0.996836 +eval/Actions Min -0.998811 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95835e-06 +time/evaluation sampling (s) 3.49618 +time/exploration sampling (s) 3.97045 +time/logging (s) 0.00691934 +time/saving (s) 0.00986632 +time/training (s) 15.3141 +time/epoch (s) 22.7975 +time/total (s) 1449.58 +Epoch -939 +------------------------------ ---------------- +2022-05-15 18:26:57.951196 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -938 finished +------------------------------ ---------------- +epoch -938 +replay_buffer/size 999047 +trainer/num train calls 63000 +trainer/QF1 Loss 1.75932 +trainer/QF2 Loss 1.85888 +trainer/Policy Loss 28.6979 +trainer/Q1 Predictions Mean -67.8028 +trainer/Q1 Predictions Std 20.6619 +trainer/Q1 Predictions Max -2.83281 +trainer/Q1 Predictions Min -86.0971 +trainer/Q2 Predictions Mean -67.7898 +trainer/Q2 Predictions Std 20.6632 +trainer/Q2 Predictions Max -2.5589 +trainer/Q2 Predictions Min -86.4569 +trainer/Q Targets Mean -68.075 +trainer/Q Targets Std 20.9006 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7627 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00441123 +trainer/policy/mean Std 0.689579 +trainer/policy/mean Max 0.996863 +trainer/policy/mean Min -0.99461 +trainer/policy/std Mean 0.462118 +trainer/policy/std Std 0.0268844 +trainer/policy/std Max 0.497695 +trainer/policy/std Min 0.421609 +trainer/Advantage Weights Mean 5.7855 +trainer/Advantage Weights Std 18.8969 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.19041e-10 +trainer/Advantage Score Mean -0.210748 +trainer/Advantage Score Std 0.406785 +trainer/Advantage Score Max 0.925791 +trainer/Advantage Score Min -2.22418 +trainer/V1 Predictions Mean -67.8848 +trainer/V1 Predictions Std 20.8118 +trainer/V1 Predictions Max -1.74764 +trainer/V1 Predictions Min -86.7626 +trainer/VF Loss 0.0395174 +expl/num steps total 63000 +expl/num paths total 63 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0354219 +expl/Actions Std 0.825923 +expl/Actions Max 2.44693 +expl/Actions Min -2.45655 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 62701 +eval/num paths total 63 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0478226 +eval/Actions Std 0.637823 +eval/Actions Max 0.999418 +eval/Actions Min -0.994391 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.67196e-06 +time/evaluation sampling (s) 3.15259 +time/exploration sampling (s) 3.96863 +time/logging (s) 0.00725077 +time/saving (s) 0.0142637 +time/training (s) 15.4706 +time/epoch (s) 22.6134 +time/total (s) 1472.2 +Epoch -938 +------------------------------ ---------------- +2022-05-15 18:27:20.435781 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -937 finished +------------------------------ ---------------- +epoch -937 +replay_buffer/size 999047 +trainer/num train calls 64000 +trainer/QF1 Loss 0.807256 +trainer/QF2 Loss 0.761805 +trainer/Policy Loss 8.75678 +trainer/Q1 Predictions Mean -68.9057 +trainer/Q1 Predictions Std 19.2947 +trainer/Q1 Predictions Max -2.20801 +trainer/Q1 Predictions Min -87.3197 +trainer/Q2 Predictions Mean -68.9525 +trainer/Q2 Predictions Std 19.29 +trainer/Q2 Predictions Max -1.92872 +trainer/Q2 Predictions Min -87.8024 +trainer/Q Targets Mean -68.9544 +trainer/Q Targets Std 19.1199 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3288 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0145806 +trainer/policy/mean Std 0.66779 +trainer/policy/mean Max 0.996078 +trainer/policy/mean Min -0.994972 +trainer/policy/std Mean 0.462762 +trainer/policy/std Std 0.025433 +trainer/policy/std Max 0.495128 +trainer/policy/std Min 0.423943 +trainer/Advantage Weights Mean 1.79673 +trainer/Advantage Weights Std 11.2507 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.12769e-12 +trainer/Advantage Score Mean -0.494087 +trainer/Advantage Score Std 0.497035 +trainer/Advantage Score Max 1.46127 +trainer/Advantage Score Min -2.62133 +trainer/V1 Predictions Mean -68.5977 +trainer/V1 Predictions Std 19.3464 +trainer/V1 Predictions Max -1.94463 +trainer/V1 Predictions Min -87.2243 +trainer/VF Loss 0.0593366 +expl/num steps total 64000 +expl/num paths total 64 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.118114 +expl/Actions Std 0.799315 +expl/Actions Max 2.41271 +expl/Actions Min -2.41572 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 63701 +eval/num paths total 64 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.138637 +eval/Actions Std 0.691003 +eval/Actions Max 0.993502 +eval/Actions Min -0.997163 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.50619e-06 +time/evaluation sampling (s) 3.38338 +time/exploration sampling (s) 3.92235 +time/logging (s) 0.00673186 +time/saving (s) 0.00985875 +time/training (s) 15.156 +time/epoch (s) 22.4783 +time/total (s) 1494.68 +Epoch -937 +------------------------------ ---------------- +2022-05-15 18:27:43.386328 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -936 finished +------------------------------ ---------------- +epoch -936 +replay_buffer/size 999047 +trainer/num train calls 65000 +trainer/QF1 Loss 0.771712 +trainer/QF2 Loss 0.816047 +trainer/Policy Loss 12.279 +trainer/Q1 Predictions Mean -70.5897 +trainer/Q1 Predictions Std 17.0969 +trainer/Q1 Predictions Max -3.81965 +trainer/Q1 Predictions Min -88.0795 +trainer/Q2 Predictions Mean -70.5833 +trainer/Q2 Predictions Std 17.1245 +trainer/Q2 Predictions Max -3.37026 +trainer/Q2 Predictions Min -88.0782 +trainer/Q Targets Mean -70.2035 +trainer/Q Targets Std 17.2525 +trainer/Q Targets Max -2.92031 +trainer/Q Targets Min -86.8467 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0057396 +trainer/policy/mean Std 0.682667 +trainer/policy/mean Max 0.994968 +trainer/policy/mean Min -0.996281 +trainer/policy/std Mean 0.462516 +trainer/policy/std Std 0.026023 +trainer/policy/std Max 0.495553 +trainer/policy/std Min 0.421442 +trainer/Advantage Weights Mean 1.44168 +trainer/Advantage Weights Std 10.8359 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.98795e-15 +trainer/Advantage Score Mean -0.545428 +trainer/Advantage Score Std 0.508477 +trainer/Advantage Score Max 1.06314 +trainer/Advantage Score Min -3.24608 +trainer/V1 Predictions Mean -69.833 +trainer/V1 Predictions Std 17.466 +trainer/V1 Predictions Max -2.01243 +trainer/V1 Predictions Min -86.8539 +trainer/VF Loss 0.0614788 +expl/num steps total 65000 +expl/num paths total 65 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0618901 +expl/Actions Std 0.818573 +expl/Actions Max 2.2698 +expl/Actions Min -2.53511 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 64701 +eval/num paths total 65 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0832869 +eval/Actions Std 0.661877 +eval/Actions Max 0.997154 +eval/Actions Min -0.997132 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.86615e-06 +time/evaluation sampling (s) 3.16817 +time/exploration sampling (s) 3.97204 +time/logging (s) 0.00888525 +time/saving (s) 0.0122958 +time/training (s) 15.7858 +time/epoch (s) 22.9472 +time/total (s) 1517.64 +Epoch -936 +------------------------------ ---------------- +2022-05-15 18:28:05.862814 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -935 finished +------------------------------ ---------------- +epoch -935 +replay_buffer/size 999047 +trainer/num train calls 66000 +trainer/QF1 Loss 0.853031 +trainer/QF2 Loss 0.961704 +trainer/Policy Loss 16.3423 +trainer/Q1 Predictions Mean -71.6167 +trainer/Q1 Predictions Std 15.9618 +trainer/Q1 Predictions Max -5.19224 +trainer/Q1 Predictions Min -88.7811 +trainer/Q2 Predictions Mean -71.6864 +trainer/Q2 Predictions Std 15.9685 +trainer/Q2 Predictions Max -5.33562 +trainer/Q2 Predictions Min -88.8114 +trainer/Q Targets Mean -71.4385 +trainer/Q Targets Std 16.3283 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4146 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0174022 +trainer/policy/mean Std 0.681549 +trainer/policy/mean Max 0.99357 +trainer/policy/mean Min -0.992989 +trainer/policy/std Mean 0.460839 +trainer/policy/std Std 0.0239055 +trainer/policy/std Max 0.490421 +trainer/policy/std Min 0.421856 +trainer/Advantage Weights Mean 3.42639 +trainer/Advantage Weights Std 13.8122 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.2097e-12 +trainer/Advantage Score Mean -0.287874 +trainer/Advantage Score Std 0.432853 +trainer/Advantage Score Max 0.708649 +trainer/Advantage Score Min -2.56556 +trainer/V1 Predictions Mean -71.217 +trainer/V1 Predictions Std 16.1739 +trainer/V1 Predictions Max -3.9141 +trainer/V1 Predictions Min -88.3215 +trainer/VF Loss 0.0364612 +expl/num steps total 66000 +expl/num paths total 66 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0258007 +expl/Actions Std 0.812147 +expl/Actions Max 2.61276 +expl/Actions Min -2.31911 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 65701 +eval/num paths total 66 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.108291 +eval/Actions Std 0.711454 +eval/Actions Max 0.996329 +eval/Actions Min -0.996251 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.17395e-06 +time/evaluation sampling (s) 3.01513 +time/exploration sampling (s) 3.66354 +time/logging (s) 0.0104468 +time/saving (s) 0.0150371 +time/training (s) 15.7675 +time/epoch (s) 22.4717 +time/total (s) 1540.11 +Epoch -935 +------------------------------ ---------------- +2022-05-15 18:28:28.410439 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -934 finished +------------------------------ ---------------- +epoch -934 +replay_buffer/size 999047 +trainer/num train calls 67000 +trainer/QF1 Loss 2.82488 +trainer/QF2 Loss 3.13472 +trainer/Policy Loss 14.302 +trainer/Q1 Predictions Mean -65.8895 +trainer/Q1 Predictions Std 20.5733 +trainer/Q1 Predictions Max -0.643622 +trainer/Q1 Predictions Min -86.104 +trainer/Q2 Predictions Mean -65.8156 +trainer/Q2 Predictions Std 20.5432 +trainer/Q2 Predictions Max -1.00113 +trainer/Q2 Predictions Min -85.9489 +trainer/Q Targets Mean -66.9227 +trainer/Q Targets Std 20.2651 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7324 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00328829 +trainer/policy/mean Std 0.696857 +trainer/policy/mean Max 0.995248 +trainer/policy/mean Min -0.9962 +trainer/policy/std Mean 0.459121 +trainer/policy/std Std 0.0243493 +trainer/policy/std Max 0.492239 +trainer/policy/std Min 0.420947 +trainer/Advantage Weights Mean 3.29682 +trainer/Advantage Weights Std 16.5025 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.94592e-10 +trainer/Advantage Score Mean -0.583558 +trainer/Advantage Score Std 0.479392 +trainer/Advantage Score Max 0.856964 +trainer/Advantage Score Min -2.23601 +trainer/V1 Predictions Mean -66.6632 +trainer/V1 Predictions Std 20.3856 +trainer/V1 Predictions Max -1.1593 +trainer/V1 Predictions Min -85.8382 +trainer/VF Loss 0.0669742 +expl/num steps total 67000 +expl/num paths total 67 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0165534 +expl/Actions Std 0.803929 +expl/Actions Max 2.33376 +expl/Actions Min -2.48868 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 66701 +eval/num paths total 67 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0707238 +eval/Actions Std 0.680936 +eval/Actions Max 0.997865 +eval/Actions Min -0.996785 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 5.06267e-06 +time/evaluation sampling (s) 3.03582 +time/exploration sampling (s) 3.57252 +time/logging (s) 0.00767592 +time/saving (s) 0.0123798 +time/training (s) 15.9096 +time/epoch (s) 22.538 +time/total (s) 1562.65 +Epoch -934 +------------------------------ ---------------- +2022-05-15 18:28:51.004013 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -933 finished +------------------------------ ---------------- +epoch -933 +replay_buffer/size 999047 +trainer/num train calls 68000 +trainer/QF1 Loss 0.713693 +trainer/QF2 Loss 0.73287 +trainer/Policy Loss 35.8056 +trainer/Q1 Predictions Mean -72.3545 +trainer/Q1 Predictions Std 15.3663 +trainer/Q1 Predictions Max -3.29242 +trainer/Q1 Predictions Min -87.3854 +trainer/Q2 Predictions Mean -72.3455 +trainer/Q2 Predictions Std 15.4028 +trainer/Q2 Predictions Max -2.89979 +trainer/Q2 Predictions Min -87.1387 +trainer/Q Targets Mean -72.3909 +trainer/Q Targets Std 15.2692 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6109 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00867469 +trainer/policy/mean Std 0.671507 +trainer/policy/mean Max 0.997013 +trainer/policy/mean Min -0.995831 +trainer/policy/std Mean 0.458873 +trainer/policy/std Std 0.0245919 +trainer/policy/std Max 0.492475 +trainer/policy/std Min 0.419872 +trainer/Advantage Weights Mean 5.8957 +trainer/Advantage Weights Std 19.8247 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.82396e-07 +trainer/Advantage Score Mean -0.227305 +trainer/Advantage Score Std 0.408859 +trainer/Advantage Score Max 1.10635 +trainer/Advantage Score Min -1.508 +trainer/V1 Predictions Mean -72.1556 +trainer/V1 Predictions Std 15.3452 +trainer/V1 Predictions Max -4.16389 +trainer/V1 Predictions Min -87.5687 +trainer/VF Loss 0.0492646 +expl/num steps total 68000 +expl/num paths total 68 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0122719 +expl/Actions Std 0.809189 +expl/Actions Max 2.53102 +expl/Actions Min -2.59315 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 67701 +eval/num paths total 68 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.089157 +eval/Actions Std 0.680563 +eval/Actions Max 0.996259 +eval/Actions Min -0.996599 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.01893e-06 +time/evaluation sampling (s) 2.9063 +time/exploration sampling (s) 3.96481 +time/logging (s) 0.0137037 +time/saving (s) 0.0187756 +time/training (s) 15.6899 +time/epoch (s) 22.5935 +time/total (s) 1585.25 +Epoch -933 +------------------------------ ---------------- +2022-05-15 18:29:14.037491 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -932 finished +------------------------------ ---------------- +epoch -932 +replay_buffer/size 999047 +trainer/num train calls 69000 +trainer/QF1 Loss 14.0415 +trainer/QF2 Loss 14.3936 +trainer/Policy Loss 39.4605 +trainer/Q1 Predictions Mean -68.6443 +trainer/Q1 Predictions Std 19.6595 +trainer/Q1 Predictions Max -0.893798 +trainer/Q1 Predictions Min -88.0634 +trainer/Q2 Predictions Mean -68.5132 +trainer/Q2 Predictions Std 19.6841 +trainer/Q2 Predictions Max -1.1098 +trainer/Q2 Predictions Min -87.9955 +trainer/Q Targets Mean -68.5975 +trainer/Q Targets Std 19.579 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.2523 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0243305 +trainer/policy/mean Std 0.680999 +trainer/policy/mean Max 0.997368 +trainer/policy/mean Min -0.998095 +trainer/policy/std Mean 0.458279 +trainer/policy/std Std 0.0247729 +trainer/policy/std Max 0.491453 +trainer/policy/std Min 0.420661 +trainer/Advantage Weights Mean 6.41896 +trainer/Advantage Weights Std 21.282 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.90005e-13 +trainer/Advantage Score Mean -0.281214 +trainer/Advantage Score Std 0.507232 +trainer/Advantage Score Max 1.71602 +trainer/Advantage Score Min -2.92917 +trainer/V1 Predictions Mean -68.4816 +trainer/V1 Predictions Std 19.6074 +trainer/V1 Predictions Max -0.315899 +trainer/V1 Predictions Min -87.9256 +trainer/VF Loss 0.0679684 +expl/num steps total 69000 +expl/num paths total 69 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.127981 +expl/Actions Std 0.774514 +expl/Actions Max 2.48249 +expl/Actions Min -2.23342 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 68701 +eval/num paths total 69 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0123606 +eval/Actions Std 0.726816 +eval/Actions Max 0.998836 +eval/Actions Min -0.997089 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18419e-06 +time/evaluation sampling (s) 3.37619 +time/exploration sampling (s) 3.70601 +time/logging (s) 0.00871516 +time/saving (s) 0.0126088 +time/training (s) 15.9164 +time/epoch (s) 23.0199 +time/total (s) 1608.28 +Epoch -932 +------------------------------ ---------------- +2022-05-15 18:29:36.939582 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -931 finished +------------------------------ ---------------- +epoch -931 +replay_buffer/size 999047 +trainer/num train calls 70000 +trainer/QF1 Loss 1.23743 +trainer/QF2 Loss 1.26638 +trainer/Policy Loss 40.2902 +trainer/Q1 Predictions Mean -69.1552 +trainer/Q1 Predictions Std 18.9178 +trainer/Q1 Predictions Max -1.26524 +trainer/Q1 Predictions Min -87.0299 +trainer/Q2 Predictions Mean -69.1524 +trainer/Q2 Predictions Std 18.8897 +trainer/Q2 Predictions Max -1.30857 +trainer/Q2 Predictions Min -87.0528 +trainer/Q Targets Mean -69.8403 +trainer/Q Targets Std 18.9187 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7361 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0083972 +trainer/policy/mean Std 0.68233 +trainer/policy/mean Max 0.997044 +trainer/policy/mean Min -0.996716 +trainer/policy/std Mean 0.457599 +trainer/policy/std Std 0.0243562 +trainer/policy/std Max 0.490293 +trainer/policy/std Min 0.420767 +trainer/Advantage Weights Mean 6.72025 +trainer/Advantage Weights Std 20.0904 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.4577e-09 +trainer/Advantage Score Mean -0.194653 +trainer/Advantage Score Std 0.447794 +trainer/Advantage Score Max 1.34494 +trainer/Advantage Score Min -1.92286 +trainer/V1 Predictions Mean -69.565 +trainer/V1 Predictions Std 18.9277 +trainer/V1 Predictions Max -1.71538 +trainer/V1 Predictions Min -87.5384 +trainer/VF Loss 0.0522424 +expl/num steps total 70000 +expl/num paths total 70 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0319439 +expl/Actions Std 0.794609 +expl/Actions Max 2.40422 +expl/Actions Min -2.32944 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 69701 +eval/num paths total 70 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0417566 +eval/Actions Std 0.712949 +eval/Actions Max 0.997409 +eval/Actions Min -0.997783 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.70689e-06 +time/evaluation sampling (s) 3.02632 +time/exploration sampling (s) 3.73376 +time/logging (s) 0.0102332 +time/saving (s) 0.0144754 +time/training (s) 16.1122 +time/epoch (s) 22.897 +time/total (s) 1631.18 +Epoch -931 +------------------------------ ---------------- +2022-05-15 18:29:59.463548 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -930 finished +------------------------------ ---------------- +epoch -930 +replay_buffer/size 999047 +trainer/num train calls 71000 +trainer/QF1 Loss 1.43414 +trainer/QF2 Loss 1.48106 +trainer/Policy Loss 16.1478 +trainer/Q1 Predictions Mean -71.2265 +trainer/Q1 Predictions Std 15.8464 +trainer/Q1 Predictions Max -3.40613 +trainer/Q1 Predictions Min -88.2444 +trainer/Q2 Predictions Mean -71.2525 +trainer/Q2 Predictions Std 15.7928 +trainer/Q2 Predictions Max -4.01556 +trainer/Q2 Predictions Min -87.9579 +trainer/Q Targets Mean -71.1912 +trainer/Q Targets Std 16.0824 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7946 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000623431 +trainer/policy/mean Std 0.690372 +trainer/policy/mean Max 0.996222 +trainer/policy/mean Min -0.99791 +trainer/policy/std Mean 0.457412 +trainer/policy/std Std 0.0253022 +trainer/policy/std Max 0.491657 +trainer/policy/std Min 0.420133 +trainer/Advantage Weights Mean 3.49258 +trainer/Advantage Weights Std 15.1653 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.07032e-10 +trainer/Advantage Score Mean -0.329634 +trainer/Advantage Score Std 0.485105 +trainer/Advantage Score Max 0.874054 +trainer/Advantage Score Min -2.29579 +trainer/V1 Predictions Mean -70.9581 +trainer/V1 Predictions Std 15.9966 +trainer/V1 Predictions Max -3.09868 +trainer/V1 Predictions Min -87.6869 +trainer/VF Loss 0.0476034 +expl/num steps total 71000 +expl/num paths total 71 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00614522 +expl/Actions Std 0.810537 +expl/Actions Max 2.3077 +expl/Actions Min -2.53004 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 70701 +eval/num paths total 71 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0309017 +eval/Actions Std 0.698289 +eval/Actions Max 0.997473 +eval/Actions Min -0.997285 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18279e-06 +time/evaluation sampling (s) 2.67435 +time/exploration sampling (s) 3.57076 +time/logging (s) 0.0116753 +time/saving (s) 0.0158293 +time/training (s) 16.2446 +time/epoch (s) 22.5173 +time/total (s) 1653.7 +Epoch -930 +------------------------------ ---------------- +2022-05-15 18:30:22.290942 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -929 finished +------------------------------ ---------------- +epoch -929 +replay_buffer/size 999047 +trainer/num train calls 72000 +trainer/QF1 Loss 0.671814 +trainer/QF2 Loss 0.713346 +trainer/Policy Loss 12.9045 +trainer/Q1 Predictions Mean -72.0063 +trainer/Q1 Predictions Std 17.1722 +trainer/Q1 Predictions Max -2.08358 +trainer/Q1 Predictions Min -87.7769 +trainer/Q2 Predictions Mean -72.0114 +trainer/Q2 Predictions Std 17.1952 +trainer/Q2 Predictions Max -2.20455 +trainer/Q2 Predictions Min -87.7153 +trainer/Q Targets Mean -71.5605 +trainer/Q Targets Std 16.9784 +trainer/Q Targets Max -1.8171 +trainer/Q Targets Min -87.7408 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0391247 +trainer/policy/mean Std 0.679335 +trainer/policy/mean Max 0.994073 +trainer/policy/mean Min -0.997846 +trainer/policy/std Mean 0.457239 +trainer/policy/std Std 0.0238448 +trainer/policy/std Max 0.490421 +trainer/policy/std Min 0.423775 +trainer/Advantage Weights Mean 3.24846 +trainer/Advantage Weights Std 15.3541 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19099e-09 +trainer/Advantage Score Mean -0.386866 +trainer/Advantage Score Std 0.399293 +trainer/Advantage Score Max 1.49301 +trainer/Advantage Score Min -2.05485 +trainer/V1 Predictions Mean -71.296 +trainer/V1 Predictions Std 17.1353 +trainer/V1 Predictions Max -0.383327 +trainer/V1 Predictions Min -87.6745 +trainer/VF Loss 0.0451958 +expl/num steps total 72000 +expl/num paths total 72 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.018018 +expl/Actions Std 0.790991 +expl/Actions Max 2.80735 +expl/Actions Min -2.41245 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 71701 +eval/num paths total 72 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0048203 +eval/Actions Std 0.723237 +eval/Actions Max 0.99827 +eval/Actions Min -0.998505 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.96025e-06 +time/evaluation sampling (s) 2.87422 +time/exploration sampling (s) 3.7842 +time/logging (s) 0.0105547 +time/saving (s) 0.0147819 +time/training (s) 16.133 +time/epoch (s) 22.8168 +time/total (s) 1676.53 +Epoch -929 +------------------------------ ---------------- +2022-05-15 18:30:44.994406 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -928 finished +------------------------------ ---------------- +epoch -928 +replay_buffer/size 999047 +trainer/num train calls 73000 +trainer/QF1 Loss 0.786905 +trainer/QF2 Loss 0.782721 +trainer/Policy Loss 9.59267 +trainer/Q1 Predictions Mean -67.5588 +trainer/Q1 Predictions Std 21.1144 +trainer/Q1 Predictions Max -1.44744 +trainer/Q1 Predictions Min -87.0151 +trainer/Q2 Predictions Mean -67.5587 +trainer/Q2 Predictions Std 21.1193 +trainer/Q2 Predictions Max -1.96186 +trainer/Q2 Predictions Min -87.2169 +trainer/Q Targets Mean -67.3801 +trainer/Q Targets Std 21.3569 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1322 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00243692 +trainer/policy/mean Std 0.68458 +trainer/policy/mean Max 0.998577 +trainer/policy/mean Min -0.995907 +trainer/policy/std Mean 0.458261 +trainer/policy/std Std 0.0238687 +trainer/policy/std Max 0.489999 +trainer/policy/std Min 0.422376 +trainer/Advantage Weights Mean 1.97396 +trainer/Advantage Weights Std 11.9096 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.48524e-19 +trainer/Advantage Score Mean -0.601993 +trainer/Advantage Score Std 0.671614 +trainer/Advantage Score Max 0.766785 +trainer/Advantage Score Min -4.17362 +trainer/V1 Predictions Mean -67.1121 +trainer/V1 Predictions Std 21.4818 +trainer/V1 Predictions Max -1.10639 +trainer/V1 Predictions Min -86.9403 +trainer/VF Loss 0.0870169 +expl/num steps total 73000 +expl/num paths total 73 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.155976 +expl/Actions Std 0.781052 +expl/Actions Max 2.4331 +expl/Actions Min -2.39324 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 72701 +eval/num paths total 73 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.278536 +eval/Actions Std 0.678281 +eval/Actions Max 0.984156 +eval/Actions Min -0.989028 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93879e-06 +time/evaluation sampling (s) 3.08907 +time/exploration sampling (s) 4.17756 +time/logging (s) 0.0106759 +time/saving (s) 0.0149597 +time/training (s) 15.4031 +time/epoch (s) 22.6954 +time/total (s) 1699.23 +Epoch -928 +------------------------------ ---------------- +2022-05-15 18:31:07.455299 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -927 finished +------------------------------ ---------------- +epoch -927 +replay_buffer/size 999047 +trainer/num train calls 74000 +trainer/QF1 Loss 2.32307 +trainer/QF2 Loss 2.45805 +trainer/Policy Loss 5.39785 +trainer/Q1 Predictions Mean -69.0167 +trainer/Q1 Predictions Std 19.3713 +trainer/Q1 Predictions Max -2.41092 +trainer/Q1 Predictions Min -86.8195 +trainer/Q2 Predictions Mean -69.1087 +trainer/Q2 Predictions Std 19.3256 +trainer/Q2 Predictions Max -2.52002 +trainer/Q2 Predictions Min -87.0902 +trainer/Q Targets Mean -68.8983 +trainer/Q Targets Std 19.7655 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2011 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00125892 +trainer/policy/mean Std 0.691217 +trainer/policy/mean Max 0.995347 +trainer/policy/mean Min -0.99191 +trainer/policy/std Mean 0.455966 +trainer/policy/std Std 0.0236886 +trainer/policy/std Max 0.484517 +trainer/policy/std Min 0.420113 +trainer/Advantage Weights Mean 1.04061 +trainer/Advantage Weights Std 7.22453 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.40412e-10 +trainer/Advantage Score Mean -0.484197 +trainer/Advantage Score Std 0.411379 +trainer/Advantage Score Max 0.82355 +trainer/Advantage Score Min -2.21487 +trainer/V1 Predictions Mean -68.7664 +trainer/V1 Predictions Std 19.4712 +trainer/V1 Predictions Max -1.53884 +trainer/V1 Predictions Min -86.937 +trainer/VF Loss 0.0442767 +expl/num steps total 74000 +expl/num paths total 74 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.108147 +expl/Actions Std 0.792414 +expl/Actions Max 2.41452 +expl/Actions Min -2.4313 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 73701 +eval/num paths total 74 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.26215 +eval/Actions Std 0.645956 +eval/Actions Max 0.994382 +eval/Actions Min -0.989701 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.06981e-05 +time/evaluation sampling (s) 2.98557 +time/exploration sampling (s) 3.98898 +time/logging (s) 0.0116269 +time/saving (s) 0.0163063 +time/training (s) 15.4514 +time/epoch (s) 22.4539 +time/total (s) 1721.69 +Epoch -927 +------------------------------ ---------------- +2022-05-15 18:31:30.111082 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -926 finished +------------------------------ ---------------- +epoch -926 +replay_buffer/size 999047 +trainer/num train calls 75000 +trainer/QF1 Loss 0.8026 +trainer/QF2 Loss 0.859005 +trainer/Policy Loss 30.906 +trainer/Q1 Predictions Mean -68.6989 +trainer/Q1 Predictions Std 18.3562 +trainer/Q1 Predictions Max -2.29298 +trainer/Q1 Predictions Min -86.8902 +trainer/Q2 Predictions Mean -68.7387 +trainer/Q2 Predictions Std 18.3105 +trainer/Q2 Predictions Max -2.7971 +trainer/Q2 Predictions Min -86.8928 +trainer/Q Targets Mean -68.8964 +trainer/Q Targets Std 18.3125 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.465 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0035658 +trainer/policy/mean Std 0.682195 +trainer/policy/mean Max 0.994619 +trainer/policy/mean Min -0.994924 +trainer/policy/std Mean 0.456084 +trainer/policy/std Std 0.0226604 +trainer/policy/std Max 0.48326 +trainer/policy/std Min 0.420538 +trainer/Advantage Weights Mean 6.04969 +trainer/Advantage Weights Std 20.5926 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.25374e-10 +trainer/Advantage Score Mean -0.316969 +trainer/Advantage Score Std 0.495404 +trainer/Advantage Score Max 1.02792 +trainer/Advantage Score Min -2.22133 +trainer/V1 Predictions Mean -68.5585 +trainer/V1 Predictions Std 18.4609 +trainer/V1 Predictions Max -2.41544 +trainer/V1 Predictions Min -86.2947 +trainer/VF Loss 0.0554133 +expl/num steps total 75000 +expl/num paths total 75 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0450506 +expl/Actions Std 0.771324 +expl/Actions Max 2.43128 +expl/Actions Min -2.63468 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 74701 +eval/num paths total 75 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.359752 +eval/Actions Std 0.521134 +eval/Actions Max 0.994769 +eval/Actions Min -0.99578 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.7041e-06 +time/evaluation sampling (s) 3.20923 +time/exploration sampling (s) 4.04526 +time/logging (s) 0.00685477 +time/saving (s) 0.0125004 +time/training (s) 15.3695 +time/epoch (s) 22.6434 +time/total (s) 1744.34 +Epoch -926 +------------------------------ ---------------- +2022-05-15 18:31:52.847569 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -925 finished +------------------------------ ---------------- +epoch -925 +replay_buffer/size 999047 +trainer/num train calls 76000 +trainer/QF1 Loss 0.875245 +trainer/QF2 Loss 0.777196 +trainer/Policy Loss 29.2381 +trainer/Q1 Predictions Mean -68.6065 +trainer/Q1 Predictions Std 19.6599 +trainer/Q1 Predictions Max -1.38778 +trainer/Q1 Predictions Min -86.2748 +trainer/Q2 Predictions Mean -68.4877 +trainer/Q2 Predictions Std 19.7222 +trainer/Q2 Predictions Max -1.33633 +trainer/Q2 Predictions Min -86.4281 +trainer/Q Targets Mean -68.1586 +trainer/Q Targets Std 19.6685 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1417 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00408105 +trainer/policy/mean Std 0.690512 +trainer/policy/mean Max 0.996279 +trainer/policy/mean Min -0.995062 +trainer/policy/std Mean 0.455134 +trainer/policy/std Std 0.0251977 +trainer/policy/std Max 0.485371 +trainer/policy/std Min 0.417961 +trainer/Advantage Weights Mean 6.51823 +trainer/Advantage Weights Std 21.7741 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.24714e-17 +trainer/Advantage Score Mean -0.27467 +trainer/Advantage Score Std 0.476725 +trainer/Advantage Score Max 1.01256 +trainer/Advantage Score Min -3.89231 +trainer/V1 Predictions Mean -67.9321 +trainer/V1 Predictions Std 19.6791 +trainer/V1 Predictions Max -1.29193 +trainer/V1 Predictions Min -85.9238 +trainer/VF Loss 0.0562947 +expl/num steps total 76000 +expl/num paths total 76 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0492664 +expl/Actions Std 0.790004 +expl/Actions Max 2.44723 +expl/Actions Min -2.45458 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 75701 +eval/num paths total 76 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0656155 +eval/Actions Std 0.693908 +eval/Actions Max 0.998728 +eval/Actions Min -0.995096 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66731e-06 +time/evaluation sampling (s) 3.07025 +time/exploration sampling (s) 4.02567 +time/logging (s) 0.0080587 +time/saving (s) 0.0113694 +time/training (s) 15.6158 +time/epoch (s) 22.7311 +time/total (s) 1767.08 +Epoch -925 +------------------------------ ---------------- +2022-05-15 18:32:15.279712 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -924 finished +------------------------------ ---------------- +epoch -924 +replay_buffer/size 999047 +trainer/num train calls 77000 +trainer/QF1 Loss 1.30147 +trainer/QF2 Loss 1.38754 +trainer/Policy Loss 11.6168 +trainer/Q1 Predictions Mean -68.5772 +trainer/Q1 Predictions Std 20.3363 +trainer/Q1 Predictions Max -1.93743 +trainer/Q1 Predictions Min -86.898 +trainer/Q2 Predictions Mean -68.4823 +trainer/Q2 Predictions Std 20.3196 +trainer/Q2 Predictions Max -1.70256 +trainer/Q2 Predictions Min -87.1536 +trainer/Q Targets Mean -68.7205 +trainer/Q Targets Std 20.3545 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4805 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0139148 +trainer/policy/mean Std 0.694144 +trainer/policy/mean Max 0.998842 +trainer/policy/mean Min -0.997212 +trainer/policy/std Mean 0.457879 +trainer/policy/std Std 0.0269918 +trainer/policy/std Max 0.48947 +trainer/policy/std Min 0.414533 +trainer/Advantage Weights Mean 3.60576 +trainer/Advantage Weights Std 15.3897 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.33573e-12 +trainer/Advantage Score Mean -0.437706 +trainer/Advantage Score Std 0.522894 +trainer/Advantage Score Max 0.894348 +trainer/Advantage Score Min -2.61641 +trainer/V1 Predictions Mean -68.3536 +trainer/V1 Predictions Std 20.5409 +trainer/V1 Predictions Max 0.490152 +trainer/V1 Predictions Min -87.5322 +trainer/VF Loss 0.0597101 +expl/num steps total 77000 +expl/num paths total 77 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.138441 +expl/Actions Std 0.806968 +expl/Actions Max 2.65045 +expl/Actions Min -2.24659 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 76701 +eval/num paths total 77 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.334211 +eval/Actions Std 0.647859 +eval/Actions Max 0.997539 +eval/Actions Min -0.99303 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.43518e-06 +time/evaluation sampling (s) 3.07577 +time/exploration sampling (s) 4.08817 +time/logging (s) 0.00751632 +time/saving (s) 0.0102498 +time/training (s) 15.2432 +time/epoch (s) 22.4249 +time/total (s) 1789.51 +Epoch -924 +------------------------------ ---------------- +2022-05-15 18:32:37.609517 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -923 finished +------------------------------ ---------------- +epoch -923 +replay_buffer/size 999047 +trainer/num train calls 78000 +trainer/QF1 Loss 0.638723 +trainer/QF2 Loss 0.633464 +trainer/Policy Loss 2.47922 +trainer/Q1 Predictions Mean -71.0973 +trainer/Q1 Predictions Std 16.7058 +trainer/Q1 Predictions Max -2.23364 +trainer/Q1 Predictions Min -86.6865 +trainer/Q2 Predictions Mean -71.0761 +trainer/Q2 Predictions Std 16.718 +trainer/Q2 Predictions Max -2.03072 +trainer/Q2 Predictions Min -86.6052 +trainer/Q Targets Mean -70.9476 +trainer/Q Targets Std 16.8707 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5068 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0367201 +trainer/policy/mean Std 0.686336 +trainer/policy/mean Max 0.998815 +trainer/policy/mean Min -0.997132 +trainer/policy/std Mean 0.455013 +trainer/policy/std Std 0.0266891 +trainer/policy/std Max 0.485617 +trainer/policy/std Min 0.411058 +trainer/Advantage Weights Mean 0.597981 +trainer/Advantage Weights Std 3.26571 +trainer/Advantage Weights Max 34.0176 +trainer/Advantage Weights Min 3.25436e-14 +trainer/Advantage Score Mean -0.624509 +trainer/Advantage Score Std 0.530899 +trainer/Advantage Score Max 0.352688 +trainer/Advantage Score Min -3.10562 +trainer/V1 Predictions Mean -70.6895 +trainer/V1 Predictions Std 16.9589 +trainer/V1 Predictions Max -0.794411 +trainer/V1 Predictions Min -86.3705 +trainer/VF Loss 0.0689833 +expl/num steps total 78000 +expl/num paths total 78 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0197402 +expl/Actions Std 0.808959 +expl/Actions Max 2.54006 +expl/Actions Min -2.51707 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 77701 +eval/num paths total 78 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.127562 +eval/Actions Std 0.61622 +eval/Actions Max 0.998511 +eval/Actions Min -0.995602 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.48361e-06 +time/evaluation sampling (s) 3.03352 +time/exploration sampling (s) 4.28954 +time/logging (s) 0.00676556 +time/saving (s) 0.00969482 +time/training (s) 14.9841 +time/epoch (s) 22.3236 +time/total (s) 1811.83 +Epoch -923 +------------------------------ ---------------- +2022-05-15 18:33:00.522035 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -922 finished +------------------------------ ---------------- +epoch -922 +replay_buffer/size 999047 +trainer/num train calls 79000 +trainer/QF1 Loss 0.998697 +trainer/QF2 Loss 1.00633 +trainer/Policy Loss 27.3788 +trainer/Q1 Predictions Mean -70.5445 +trainer/Q1 Predictions Std 18.3426 +trainer/Q1 Predictions Max -2.83115 +trainer/Q1 Predictions Min -87.0673 +trainer/Q2 Predictions Mean -70.5465 +trainer/Q2 Predictions Std 18.3302 +trainer/Q2 Predictions Max -2.55378 +trainer/Q2 Predictions Min -86.8976 +trainer/Q Targets Mean -71.0358 +trainer/Q Targets Std 18.2424 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2592 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0209136 +trainer/policy/mean Std 0.684437 +trainer/policy/mean Max 0.999271 +trainer/policy/mean Min -0.9978 +trainer/policy/std Mean 0.454237 +trainer/policy/std Std 0.0253034 +trainer/policy/std Max 0.488329 +trainer/policy/std Min 0.410592 +trainer/Advantage Weights Mean 5.11321 +trainer/Advantage Weights Std 17.6151 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.56827e-13 +trainer/Advantage Score Mean -0.198313 +trainer/Advantage Score Std 0.426871 +trainer/Advantage Score Max 0.980316 +trainer/Advantage Score Min -2.79096 +trainer/V1 Predictions Mean -70.7715 +trainer/V1 Predictions Std 18.2586 +trainer/V1 Predictions Max -1.74185 +trainer/V1 Predictions Min -86.7009 +trainer/VF Loss 0.0395057 +expl/num steps total 79000 +expl/num paths total 79 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00868941 +expl/Actions Std 0.786312 +expl/Actions Max 2.50406 +expl/Actions Min -2.51414 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 78701 +eval/num paths total 79 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00695943 +eval/Actions Std 0.685867 +eval/Actions Max 0.998497 +eval/Actions Min -0.997374 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.79002e-06 +time/evaluation sampling (s) 2.96653 +time/exploration sampling (s) 4.24306 +time/logging (s) 0.00859289 +time/saving (s) 0.0128826 +time/training (s) 15.6771 +time/epoch (s) 22.9081 +time/total (s) 1834.75 +Epoch -922 +------------------------------ ---------------- +2022-05-15 18:33:23.443452 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -921 finished +------------------------------ ---------------- +epoch -921 +replay_buffer/size 999047 +trainer/num train calls 80000 +trainer/QF1 Loss 0.516682 +trainer/QF2 Loss 0.495086 +trainer/Policy Loss 13.0953 +trainer/Q1 Predictions Mean -70.6576 +trainer/Q1 Predictions Std 17.4397 +trainer/Q1 Predictions Max -3.01772 +trainer/Q1 Predictions Min -86.6361 +trainer/Q2 Predictions Mean -70.6614 +trainer/Q2 Predictions Std 17.4677 +trainer/Q2 Predictions Max -3.0367 +trainer/Q2 Predictions Min -86.4545 +trainer/Q Targets Mean -70.8223 +trainer/Q Targets Std 17.6064 +trainer/Q Targets Max -3.26307 +trainer/Q Targets Min -86.8903 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00630094 +trainer/policy/mean Std 0.683098 +trainer/policy/mean Max 0.998737 +trainer/policy/mean Min -0.995634 +trainer/policy/std Mean 0.457552 +trainer/policy/std Std 0.0239482 +trainer/policy/std Max 0.487851 +trainer/policy/std Min 0.418471 +trainer/Advantage Weights Mean 3.33367 +trainer/Advantage Weights Std 15.2752 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.48314e-17 +trainer/Advantage Score Mean -0.378673 +trainer/Advantage Score Std 0.531665 +trainer/Advantage Score Max 0.707581 +trainer/Advantage Score Min -3.82344 +trainer/V1 Predictions Mean -70.5692 +trainer/V1 Predictions Std 17.6902 +trainer/V1 Predictions Max -2.05459 +trainer/V1 Predictions Min -86.7722 +trainer/VF Loss 0.0519111 +expl/num steps total 80000 +expl/num paths total 80 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.152681 +expl/Actions Std 0.821025 +expl/Actions Max 2.55007 +expl/Actions Min -2.4021 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 79701 +eval/num paths total 80 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0118509 +eval/Actions Std 0.712812 +eval/Actions Max 0.998084 +eval/Actions Min -0.998086 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.51295e-06 +time/evaluation sampling (s) 3.12386 +time/exploration sampling (s) 4.43871 +time/logging (s) 0.00834197 +time/saving (s) 0.0101282 +time/training (s) 15.3346 +time/epoch (s) 22.9156 +time/total (s) 1857.67 +Epoch -921 +------------------------------ ---------------- +2022-05-15 18:33:45.887922 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -920 finished +------------------------------ ---------------- +epoch -920 +replay_buffer/size 999047 +trainer/num train calls 81000 +trainer/QF1 Loss 1.3129 +trainer/QF2 Loss 1.32921 +trainer/Policy Loss 19.2106 +trainer/Q1 Predictions Mean -68.7341 +trainer/Q1 Predictions Std 19.947 +trainer/Q1 Predictions Max -2.38012 +trainer/Q1 Predictions Min -85.5891 +trainer/Q2 Predictions Mean -68.8093 +trainer/Q2 Predictions Std 19.9525 +trainer/Q2 Predictions Max -2.47371 +trainer/Q2 Predictions Min -85.5344 +trainer/Q Targets Mean -69.1141 +trainer/Q Targets Std 20.2789 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.972 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00561486 +trainer/policy/mean Std 0.677474 +trainer/policy/mean Max 0.997842 +trainer/policy/mean Min -0.995383 +trainer/policy/std Mean 0.456477 +trainer/policy/std Std 0.0236596 +trainer/policy/std Max 0.486785 +trainer/policy/std Min 0.418402 +trainer/Advantage Weights Mean 3.67248 +trainer/Advantage Weights Std 15.0013 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.10649e-13 +trainer/Advantage Score Mean -0.409279 +trainer/Advantage Score Std 0.550698 +trainer/Advantage Score Max 0.778763 +trainer/Advantage Score Min -2.81243 +trainer/V1 Predictions Mean -68.8489 +trainer/V1 Predictions Std 20.3505 +trainer/V1 Predictions Max -2.75307 +trainer/V1 Predictions Min -86.0453 +trainer/VF Loss 0.0574375 +expl/num steps total 81000 +expl/num paths total 81 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0327533 +expl/Actions Std 0.795338 +expl/Actions Max 2.74906 +expl/Actions Min -2.58445 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 80701 +eval/num paths total 81 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0548091 +eval/Actions Std 0.71847 +eval/Actions Max 0.999033 +eval/Actions Min -0.997066 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.78e-06 +time/evaluation sampling (s) 3.0256 +time/exploration sampling (s) 4.17423 +time/logging (s) 0.00691228 +time/saving (s) 0.0106933 +time/training (s) 15.2204 +time/epoch (s) 22.4378 +time/total (s) 1880.11 +Epoch -920 +------------------------------ ---------------- +2022-05-15 18:34:09.096262 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -919 finished +------------------------------ ---------------- +epoch -919 +replay_buffer/size 999047 +trainer/num train calls 82000 +trainer/QF1 Loss 1.04751 +trainer/QF2 Loss 1.09245 +trainer/Policy Loss 26.2446 +trainer/Q1 Predictions Mean -71.4141 +trainer/Q1 Predictions Std 16.5175 +trainer/Q1 Predictions Max -1.50162 +trainer/Q1 Predictions Min -87.4113 +trainer/Q2 Predictions Mean -71.4269 +trainer/Q2 Predictions Std 16.5296 +trainer/Q2 Predictions Max -1.69709 +trainer/Q2 Predictions Min -87.0186 +trainer/Q Targets Mean -71.7476 +trainer/Q Targets Std 16.5517 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8744 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0135843 +trainer/policy/mean Std 0.682482 +trainer/policy/mean Max 0.994973 +trainer/policy/mean Min -0.994499 +trainer/policy/std Mean 0.454612 +trainer/policy/std Std 0.0243315 +trainer/policy/std Max 0.484447 +trainer/policy/std Min 0.414902 +trainer/Advantage Weights Mean 5.70384 +trainer/Advantage Weights Std 20.2368 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.20687e-12 +trainer/Advantage Score Mean -0.353153 +trainer/Advantage Score Std 0.529461 +trainer/Advantage Score Max 0.963005 +trainer/Advantage Score Min -2.68394 +trainer/V1 Predictions Mean -71.4769 +trainer/V1 Predictions Std 16.5024 +trainer/V1 Predictions Max -1.4373 +trainer/V1 Predictions Min -86.9395 +trainer/VF Loss 0.0579716 +expl/num steps total 82000 +expl/num paths total 82 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.140712 +expl/Actions Std 0.86048 +expl/Actions Max 2.6299 +expl/Actions Min -2.44143 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 81701 +eval/num paths total 82 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0106498 +eval/Actions Std 0.661437 +eval/Actions Max 0.993737 +eval/Actions Min -0.990723 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.84892e-06 +time/evaluation sampling (s) 3.15158 +time/exploration sampling (s) 4.3761 +time/logging (s) 0.0121154 +time/saving (s) 0.0165679 +time/training (s) 15.6504 +time/epoch (s) 23.2068 +time/total (s) 1903.32 +Epoch -919 +------------------------------ ---------------- +2022-05-15 18:34:32.626110 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -918 finished +------------------------------ ---------------- +epoch -918 +replay_buffer/size 999047 +trainer/num train calls 83000 +trainer/QF1 Loss 0.640012 +trainer/QF2 Loss 0.663219 +trainer/Policy Loss 38.5176 +trainer/Q1 Predictions Mean -71.6485 +trainer/Q1 Predictions Std 17.2779 +trainer/Q1 Predictions Max -1.85668 +trainer/Q1 Predictions Min -86.7311 +trainer/Q2 Predictions Mean -71.6804 +trainer/Q2 Predictions Std 17.2747 +trainer/Q2 Predictions Max -1.62075 +trainer/Q2 Predictions Min -86.6497 +trainer/Q Targets Mean -71.4137 +trainer/Q Targets Std 17.4174 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9235 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0189029 +trainer/policy/mean Std 0.694187 +trainer/policy/mean Max 0.997379 +trainer/policy/mean Min -0.995335 +trainer/policy/std Mean 0.454364 +trainer/policy/std Std 0.0242887 +trainer/policy/std Max 0.488348 +trainer/policy/std Min 0.415289 +trainer/Advantage Weights Mean 7.53178 +trainer/Advantage Weights Std 22.6809 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.49822e-15 +trainer/Advantage Score Mean -0.212696 +trainer/Advantage Score Std 0.453384 +trainer/Advantage Score Max 0.93055 +trainer/Advantage Score Min -3.36232 +trainer/V1 Predictions Mean -71.2249 +trainer/V1 Predictions Std 17.4516 +trainer/V1 Predictions Max -0.913097 +trainer/V1 Predictions Min -86.6012 +trainer/VF Loss 0.0467619 +expl/num steps total 83000 +expl/num paths total 83 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.216259 +expl/Actions Std 0.82017 +expl/Actions Max 2.52466 +expl/Actions Min -2.26327 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 82701 +eval/num paths total 83 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.139381 +eval/Actions Std 0.716453 +eval/Actions Max 0.995774 +eval/Actions Min -0.99414 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.05067e-05 +time/evaluation sampling (s) 3.29825 +time/exploration sampling (s) 4.46872 +time/logging (s) 0.0105487 +time/saving (s) 0.0165555 +time/training (s) 15.7267 +time/epoch (s) 23.5208 +time/total (s) 1926.85 +Epoch -918 +------------------------------ ---------------- +2022-05-15 18:34:55.344939 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -917 finished +------------------------------ ---------------- +epoch -917 +replay_buffer/size 999047 +trainer/num train calls 84000 +trainer/QF1 Loss 0.914102 +trainer/QF2 Loss 1.12736 +trainer/Policy Loss 23.8046 +trainer/Q1 Predictions Mean -71.3463 +trainer/Q1 Predictions Std 18.2625 +trainer/Q1 Predictions Max -2.41269 +trainer/Q1 Predictions Min -86.5645 +trainer/Q2 Predictions Mean -71.2444 +trainer/Q2 Predictions Std 18.2992 +trainer/Q2 Predictions Max -2.39337 +trainer/Q2 Predictions Min -86.357 +trainer/Q Targets Mean -71.6706 +trainer/Q Targets Std 18.1198 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4669 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0161397 +trainer/policy/mean Std 0.694699 +trainer/policy/mean Max 0.999356 +trainer/policy/mean Min -0.995748 +trainer/policy/std Mean 0.452743 +trainer/policy/std Std 0.0233133 +trainer/policy/std Max 0.486447 +trainer/policy/std Min 0.415524 +trainer/Advantage Weights Mean 5.01733 +trainer/Advantage Weights Std 18.8445 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.88681e-11 +trainer/Advantage Score Mean -0.251211 +trainer/Advantage Score Std 0.42252 +trainer/Advantage Score Max 1.22816 +trainer/Advantage Score Min -2.42683 +trainer/V1 Predictions Mean -71.3532 +trainer/V1 Predictions Std 18.2458 +trainer/V1 Predictions Max -2.68077 +trainer/V1 Predictions Min -86.4178 +trainer/VF Loss 0.0462826 +expl/num steps total 84000 +expl/num paths total 84 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.225073 +expl/Actions Std 0.807873 +expl/Actions Max 2.40826 +expl/Actions Min -2.49675 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 83701 +eval/num paths total 84 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.028081 +eval/Actions Std 0.707273 +eval/Actions Max 0.998737 +eval/Actions Min -0.997776 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.73809e-06 +time/evaluation sampling (s) 3.21565 +time/exploration sampling (s) 3.96397 +time/logging (s) 0.0114405 +time/saving (s) 0.0159824 +time/training (s) 15.5024 +time/epoch (s) 22.7094 +time/total (s) 1949.56 +Epoch -917 +------------------------------ ---------------- +2022-05-15 18:35:17.783173 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -916 finished +------------------------------ ---------------- +epoch -916 +replay_buffer/size 999047 +trainer/num train calls 85000 +trainer/QF1 Loss 0.940354 +trainer/QF2 Loss 0.984834 +trainer/Policy Loss 83.4359 +trainer/Q1 Predictions Mean -71.6017 +trainer/Q1 Predictions Std 15.2621 +trainer/Q1 Predictions Max -1.64745 +trainer/Q1 Predictions Min -86.7649 +trainer/Q2 Predictions Mean -71.6728 +trainer/Q2 Predictions Std 15.2779 +trainer/Q2 Predictions Max -1.69058 +trainer/Q2 Predictions Min -86.7032 +trainer/Q Targets Mean -72.0031 +trainer/Q Targets Std 15.4525 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.361 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000229848 +trainer/policy/mean Std 0.676497 +trainer/policy/mean Max 0.996561 +trainer/policy/mean Min -0.996969 +trainer/policy/std Mean 0.453558 +trainer/policy/std Std 0.0243087 +trainer/policy/std Max 0.486575 +trainer/policy/std Min 0.414517 +trainer/Advantage Weights Mean 16.2061 +trainer/Advantage Weights Std 31.4156 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.25534e-11 +trainer/Advantage Score Mean -0.0129967 +trainer/Advantage Score Std 0.466713 +trainer/Advantage Score Max 1.07763 +trainer/Advantage Score Min -2.5101 +trainer/V1 Predictions Mean -71.7873 +trainer/V1 Predictions Std 15.4622 +trainer/V1 Predictions Max -2.49363 +trainer/V1 Predictions Min -87.0598 +trainer/VF Loss 0.0807705 +expl/num steps total 85000 +expl/num paths total 85 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0143481 +expl/Actions Std 0.770581 +expl/Actions Max 2.21914 +expl/Actions Min -2.45678 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 84701 +eval/num paths total 85 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.26962 +eval/Actions Std 0.656646 +eval/Actions Max 0.998025 +eval/Actions Min -0.998884 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.01888e-06 +time/evaluation sampling (s) 3.21023 +time/exploration sampling (s) 4.0494 +time/logging (s) 0.00670768 +time/saving (s) 0.0108163 +time/training (s) 15.1482 +time/epoch (s) 22.4254 +time/total (s) 1972 +Epoch -916 +------------------------------ ---------------- +2022-05-15 18:35:39.531935 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -915 finished +------------------------------ ---------------- +epoch -915 +replay_buffer/size 999047 +trainer/num train calls 86000 +trainer/QF1 Loss 1.11946 +trainer/QF2 Loss 1.17602 +trainer/Policy Loss 46.303 +trainer/Q1 Predictions Mean -69.518 +trainer/Q1 Predictions Std 19.4503 +trainer/Q1 Predictions Max -1.12547 +trainer/Q1 Predictions Min -87.4411 +trainer/Q2 Predictions Mean -69.4956 +trainer/Q2 Predictions Std 19.4528 +trainer/Q2 Predictions Max -0.791773 +trainer/Q2 Predictions Min -87.7286 +trainer/Q Targets Mean -69.9468 +trainer/Q Targets Std 19.5976 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5295 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0137853 +trainer/policy/mean Std 0.683057 +trainer/policy/mean Max 0.99749 +trainer/policy/mean Min -0.997552 +trainer/policy/std Mean 0.453364 +trainer/policy/std Std 0.0245069 +trainer/policy/std Max 0.484305 +trainer/policy/std Min 0.415674 +trainer/Advantage Weights Mean 10.608 +trainer/Advantage Weights Std 25.7499 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.08389e-10 +trainer/Advantage Score Mean -0.0977923 +trainer/Advantage Score Std 0.461106 +trainer/Advantage Score Max 1.42612 +trainer/Advantage Score Min -2.12202 +trainer/V1 Predictions Mean -69.7537 +trainer/V1 Predictions Std 19.4116 +trainer/V1 Predictions Max -1.62383 +trainer/V1 Predictions Min -88.3429 +trainer/VF Loss 0.0709491 +expl/num steps total 86000 +expl/num paths total 86 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0928963 +expl/Actions Std 0.836425 +expl/Actions Max 2.38474 +expl/Actions Min -2.32529 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 85701 +eval/num paths total 86 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0240454 +eval/Actions Std 0.676054 +eval/Actions Max 0.992683 +eval/Actions Min -0.994803 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.15113e-06 +time/evaluation sampling (s) 2.86104 +time/exploration sampling (s) 4.05607 +time/logging (s) 0.0082069 +time/saving (s) 0.0116227 +time/training (s) 14.8079 +time/epoch (s) 21.7448 +time/total (s) 1993.74 +Epoch -915 +------------------------------ ---------------- +2022-05-15 18:36:02.049145 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -914 finished +------------------------------ ---------------- +epoch -914 +replay_buffer/size 999047 +trainer/num train calls 87000 +trainer/QF1 Loss 0.808763 +trainer/QF2 Loss 0.887866 +trainer/Policy Loss 28.5944 +trainer/Q1 Predictions Mean -69.5157 +trainer/Q1 Predictions Std 19.6358 +trainer/Q1 Predictions Max -2.34089 +trainer/Q1 Predictions Min -86.635 +trainer/Q2 Predictions Mean -69.4906 +trainer/Q2 Predictions Std 19.6606 +trainer/Q2 Predictions Max -2.29465 +trainer/Q2 Predictions Min -86.7322 +trainer/Q Targets Mean -69.9356 +trainer/Q Targets Std 19.6378 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8925 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00778632 +trainer/policy/mean Std 0.683804 +trainer/policy/mean Max 0.997358 +trainer/policy/mean Min -0.996953 +trainer/policy/std Mean 0.452155 +trainer/policy/std Std 0.0239484 +trainer/policy/std Max 0.4832 +trainer/policy/std Min 0.415922 +trainer/Advantage Weights Mean 7.10895 +trainer/Advantage Weights Std 21.676 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.49103e-16 +trainer/Advantage Score Mean -0.225326 +trainer/Advantage Score Std 0.538507 +trainer/Advantage Score Max 1.96484 +trainer/Advantage Score Min -3.53393 +trainer/V1 Predictions Mean -69.7071 +trainer/V1 Predictions Std 19.7239 +trainer/V1 Predictions Max -1.87734 +trainer/V1 Predictions Min -86.5717 +trainer/VF Loss 0.0685402 +expl/num steps total 87000 +expl/num paths total 87 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0101031 +expl/Actions Std 0.79338 +expl/Actions Max 2.34343 +expl/Actions Min -2.54819 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 86701 +eval/num paths total 87 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.142581 +eval/Actions Std 0.560187 +eval/Actions Max 0.999365 +eval/Actions Min -0.996331 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.68733e-06 +time/evaluation sampling (s) 3.23069 +time/exploration sampling (s) 4.295 +time/logging (s) 0.0138805 +time/saving (s) 0.0205814 +time/training (s) 14.9563 +time/epoch (s) 22.5164 +time/total (s) 2016.27 +Epoch -914 +------------------------------ ---------------- +2022-05-15 18:36:24.843105 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -913 finished +------------------------------ ---------------- +epoch -913 +replay_buffer/size 999047 +trainer/num train calls 88000 +trainer/QF1 Loss 2.2354 +trainer/QF2 Loss 2.47076 +trainer/Policy Loss 7.62392 +trainer/Q1 Predictions Mean -68.9634 +trainer/Q1 Predictions Std 20.0688 +trainer/Q1 Predictions Max -0.945536 +trainer/Q1 Predictions Min -87.171 +trainer/Q2 Predictions Mean -69.0312 +trainer/Q2 Predictions Std 20.0769 +trainer/Q2 Predictions Max -1.02402 +trainer/Q2 Predictions Min -87.3412 +trainer/Q Targets Mean -67.872 +trainer/Q Targets Std 20.1447 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1624 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00446116 +trainer/policy/mean Std 0.687751 +trainer/policy/mean Max 0.998293 +trainer/policy/mean Min -0.995841 +trainer/policy/std Mean 0.453165 +trainer/policy/std Std 0.0243002 +trainer/policy/std Max 0.486946 +trainer/policy/std Min 0.415862 +trainer/Advantage Weights Mean 1.75371 +trainer/Advantage Weights Std 12.4916 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.6581e-13 +trainer/Advantage Score Mean -0.745262 +trainer/Advantage Score Std 0.510242 +trainer/Advantage Score Max 0.823655 +trainer/Advantage Score Min -2.8956 +trainer/V1 Predictions Mean -67.6223 +trainer/V1 Predictions Std 20.1735 +trainer/V1 Predictions Max 0.756365 +trainer/V1 Predictions Min -85.9371 +trainer/VF Loss 0.0884082 +expl/num steps total 88000 +expl/num paths total 88 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.000164783 +expl/Actions Std 0.797 +expl/Actions Max 2.77739 +expl/Actions Min -2.31004 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 87701 +eval/num paths total 88 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.228279 +eval/Actions Std 0.626949 +eval/Actions Max 0.998367 +eval/Actions Min -0.995354 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.3332e-06 +time/evaluation sampling (s) 3.31807 +time/exploration sampling (s) 4.14845 +time/logging (s) 0.0112783 +time/saving (s) 0.0179599 +time/training (s) 15.2862 +time/epoch (s) 22.782 +time/total (s) 2039.05 +Epoch -913 +------------------------------ ---------------- +2022-05-15 18:36:47.213688 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -912 finished +------------------------------ ---------------- +epoch -912 +replay_buffer/size 999047 +trainer/num train calls 89000 +trainer/QF1 Loss 0.721534 +trainer/QF2 Loss 0.769354 +trainer/Policy Loss 8.1002 +trainer/Q1 Predictions Mean -70.3636 +trainer/Q1 Predictions Std 19.096 +trainer/Q1 Predictions Max -0.571652 +trainer/Q1 Predictions Min -86.9937 +trainer/Q2 Predictions Mean -70.4208 +trainer/Q2 Predictions Std 19.0506 +trainer/Q2 Predictions Max -0.73515 +trainer/Q2 Predictions Min -86.6908 +trainer/Q Targets Mean -70.58 +trainer/Q Targets Std 19.0619 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1653 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0171592 +trainer/policy/mean Std 0.695369 +trainer/policy/mean Max 0.999632 +trainer/policy/mean Min -0.995536 +trainer/policy/std Mean 0.452473 +trainer/policy/std Std 0.0244021 +trainer/policy/std Max 0.484416 +trainer/policy/std Min 0.41465 +trainer/Advantage Weights Mean 1.62278 +trainer/Advantage Weights Std 7.37679 +trainer/Advantage Weights Max 59.6058 +trainer/Advantage Weights Min 3.53528e-10 +trainer/Advantage Score Mean -0.422977 +trainer/Advantage Score Std 0.400531 +trainer/Advantage Score Max 0.408775 +trainer/Advantage Score Min -2.17631 +trainer/V1 Predictions Mean -70.2758 +trainer/V1 Predictions Std 19.2275 +trainer/V1 Predictions Max -1.00202 +trainer/V1 Predictions Min -87.0834 +trainer/VF Loss 0.0381428 +expl/num steps total 89000 +expl/num paths total 89 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0156362 +expl/Actions Std 0.79874 +expl/Actions Max 2.62507 +expl/Actions Min -2.41818 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 88701 +eval/num paths total 89 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0742153 +eval/Actions Std 0.715015 +eval/Actions Max 0.998231 +eval/Actions Min -0.998075 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.90805e-06 +time/evaluation sampling (s) 3.07131 +time/exploration sampling (s) 3.91354 +time/logging (s) 0.00903195 +time/saving (s) 0.0151719 +time/training (s) 15.35 +time/epoch (s) 22.359 +time/total (s) 2061.42 +Epoch -912 +------------------------------ ---------------- +2022-05-15 18:37:10.208153 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -911 finished +------------------------------ ---------------- +epoch -911 +replay_buffer/size 999047 +trainer/num train calls 90000 +trainer/QF1 Loss 0.953878 +trainer/QF2 Loss 0.858036 +trainer/Policy Loss 75.3178 +trainer/Q1 Predictions Mean -68.9114 +trainer/Q1 Predictions Std 20.0517 +trainer/Q1 Predictions Max -1.64054 +trainer/Q1 Predictions Min -87.336 +trainer/Q2 Predictions Mean -68.9636 +trainer/Q2 Predictions Std 19.9979 +trainer/Q2 Predictions Max -2.05942 +trainer/Q2 Predictions Min -87.6242 +trainer/Q Targets Mean -69.4275 +trainer/Q Targets Std 19.7977 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7245 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0139781 +trainer/policy/mean Std 0.687421 +trainer/policy/mean Max 0.996652 +trainer/policy/mean Min -0.996848 +trainer/policy/std Mean 0.451928 +trainer/policy/std Std 0.024804 +trainer/policy/std Max 0.484396 +trainer/policy/std Min 0.413548 +trainer/Advantage Weights Mean 13.9055 +trainer/Advantage Weights Std 30.8209 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.35444e-08 +trainer/Advantage Score Mean -0.0645316 +trainer/Advantage Score Std 0.442097 +trainer/Advantage Score Max 1.34399 +trainer/Advantage Score Min -1.81173 +trainer/V1 Predictions Mean -69.1941 +trainer/V1 Predictions Std 19.7632 +trainer/V1 Predictions Max -1.97603 +trainer/V1 Predictions Min -87.6532 +trainer/VF Loss 0.0757623 +expl/num steps total 90000 +expl/num paths total 90 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0132196 +expl/Actions Std 0.803711 +expl/Actions Max 2.22491 +expl/Actions Min -2.39215 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 89701 +eval/num paths total 90 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0172875 +eval/Actions Std 0.710805 +eval/Actions Max 0.998311 +eval/Actions Min -0.997502 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.24462e-05 +time/evaluation sampling (s) 3.40603 +time/exploration sampling (s) 4.21763 +time/logging (s) 0.0109096 +time/saving (s) 0.0158559 +time/training (s) 15.3379 +time/epoch (s) 22.9884 +time/total (s) 2084.41 +Epoch -911 +------------------------------ ---------------- +2022-05-15 18:37:33.270161 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -910 finished +------------------------------ ---------------- +epoch -910 +replay_buffer/size 999047 +trainer/num train calls 91000 +trainer/QF1 Loss 0.959193 +trainer/QF2 Loss 0.924608 +trainer/Policy Loss 16.1608 +trainer/Q1 Predictions Mean -69.9666 +trainer/Q1 Predictions Std 18.6572 +trainer/Q1 Predictions Max -4.3592 +trainer/Q1 Predictions Min -87.2134 +trainer/Q2 Predictions Mean -69.9688 +trainer/Q2 Predictions Std 18.691 +trainer/Q2 Predictions Max -4.412 +trainer/Q2 Predictions Min -87.2316 +trainer/Q Targets Mean -69.8206 +trainer/Q Targets Std 18.8443 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1557 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0299581 +trainer/policy/mean Std 0.684715 +trainer/policy/mean Max 0.998507 +trainer/policy/mean Min -0.995676 +trainer/policy/std Mean 0.453182 +trainer/policy/std Std 0.0236967 +trainer/policy/std Max 0.485326 +trainer/policy/std Min 0.414629 +trainer/Advantage Weights Mean 3.6812 +trainer/Advantage Weights Std 13.6887 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.39943e-16 +trainer/Advantage Score Mean -0.322981 +trainer/Advantage Score Std 0.546445 +trainer/Advantage Score Max 1.37459 +trainer/Advantage Score Min -3.59661 +trainer/V1 Predictions Mean -69.6295 +trainer/V1 Predictions Std 19.0098 +trainer/V1 Predictions Max -4.10134 +trainer/V1 Predictions Min -87.081 +trainer/VF Loss 0.0585195 +expl/num steps total 91000 +expl/num paths total 91 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00333847 +expl/Actions Std 0.803211 +expl/Actions Max 2.48527 +expl/Actions Min -2.39714 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 90701 +eval/num paths total 91 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0205259 +eval/Actions Std 0.672791 +eval/Actions Max 0.999156 +eval/Actions Min -0.996205 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.8722e-06 +time/evaluation sampling (s) 3.18989 +time/exploration sampling (s) 4.45913 +time/logging (s) 0.00764099 +time/saving (s) 0.0158943 +time/training (s) 15.3774 +time/epoch (s) 23.05 +time/total (s) 2107.47 +Epoch -910 +------------------------------ ---------------- +2022-05-15 18:37:55.600913 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -909 finished +------------------------------ ---------------- +epoch -909 +replay_buffer/size 999047 +trainer/num train calls 92000 +trainer/QF1 Loss 0.994885 +trainer/QF2 Loss 1.0533 +trainer/Policy Loss 7.10994 +trainer/Q1 Predictions Mean -69.7524 +trainer/Q1 Predictions Std 19.3758 +trainer/Q1 Predictions Max -0.302261 +trainer/Q1 Predictions Min -88.034 +trainer/Q2 Predictions Mean -69.7436 +trainer/Q2 Predictions Std 19.3948 +trainer/Q2 Predictions Max -0.722338 +trainer/Q2 Predictions Min -88.1954 +trainer/Q Targets Mean -69.7124 +trainer/Q Targets Std 19.5635 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5551 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00415754 +trainer/policy/mean Std 0.701172 +trainer/policy/mean Max 0.996952 +trainer/policy/mean Min -0.995178 +trainer/policy/std Mean 0.453634 +trainer/policy/std Std 0.0231089 +trainer/policy/std Max 0.484946 +trainer/policy/std Min 0.41596 +trainer/Advantage Weights Mean 1.31563 +trainer/Advantage Weights Std 7.22718 +trainer/Advantage Weights Max 82.8094 +trainer/Advantage Weights Min 5.87559e-12 +trainer/Advantage Score Mean -0.468934 +trainer/Advantage Score Std 0.461905 +trainer/Advantage Score Max 0.441654 +trainer/Advantage Score Min -2.58602 +trainer/V1 Predictions Mean -69.424 +trainer/V1 Predictions Std 19.7193 +trainer/V1 Predictions Max 0.0702683 +trainer/V1 Predictions Min -87.8954 +trainer/VF Loss 0.0467303 +expl/num steps total 92000 +expl/num paths total 93 +expl/path length Mean 500 +expl/path length Std 420 +expl/path length Max 920 +expl/path length Min 80 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0122921 +expl/Actions Std 0.804226 +expl/Actions Max 2.36219 +expl/Actions Min -2.51598 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 91701 +eval/num paths total 92 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.181282 +eval/Actions Std 0.682915 +eval/Actions Max 0.998621 +eval/Actions Min -0.997528 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.00709e-05 +time/evaluation sampling (s) 3.21987 +time/exploration sampling (s) 4.24037 +time/logging (s) 0.00773498 +time/saving (s) 0.0127709 +time/training (s) 14.8443 +time/epoch (s) 22.3251 +time/total (s) 2129.8 +Epoch -909 +------------------------------ ---------------- +2022-05-15 18:38:17.970095 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -908 finished +------------------------------ ---------------- +epoch -908 +replay_buffer/size 999047 +trainer/num train calls 93000 +trainer/QF1 Loss 0.458402 +trainer/QF2 Loss 0.436766 +trainer/Policy Loss 11.4371 +trainer/Q1 Predictions Mean -70.6463 +trainer/Q1 Predictions Std 17.9024 +trainer/Q1 Predictions Max -2.70417 +trainer/Q1 Predictions Min -86.742 +trainer/Q2 Predictions Mean -70.6238 +trainer/Q2 Predictions Std 17.913 +trainer/Q2 Predictions Max -2.3054 +trainer/Q2 Predictions Min -86.9791 +trainer/Q Targets Mean -70.5282 +trainer/Q Targets Std 18.0565 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6042 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0122232 +trainer/policy/mean Std 0.692015 +trainer/policy/mean Max 0.995811 +trainer/policy/mean Min -0.996811 +trainer/policy/std Mean 0.454169 +trainer/policy/std Std 0.0251721 +trainer/policy/std Max 0.488351 +trainer/policy/std Min 0.413935 +trainer/Advantage Weights Mean 2.28133 +trainer/Advantage Weights Std 11.564 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.04589e-12 +trainer/Advantage Score Mean -0.463676 +trainer/Advantage Score Std 0.524989 +trainer/Advantage Score Max 0.674652 +trainer/Advantage Score Min -2.62333 +trainer/V1 Predictions Mean -70.1982 +trainer/V1 Predictions Std 18.2724 +trainer/V1 Predictions Max -1.59141 +trainer/V1 Predictions Min -86.4667 +trainer/VF Loss 0.0556244 +expl/num steps total 93000 +expl/num paths total 94 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0458615 +expl/Actions Std 0.801283 +expl/Actions Max 2.52601 +expl/Actions Min -2.35199 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 92701 +eval/num paths total 93 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.304311 +eval/Actions Std 0.635793 +eval/Actions Max 0.995964 +eval/Actions Min -0.996795 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95974e-06 +time/evaluation sampling (s) 3.14446 +time/exploration sampling (s) 3.89938 +time/logging (s) 0.00930543 +time/saving (s) 0.0117415 +time/training (s) 15.2993 +time/epoch (s) 22.3642 +time/total (s) 2152.17 +Epoch -908 +------------------------------ ---------------- +2022-05-15 18:38:40.667857 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -907 finished +------------------------------ ---------------- +epoch -907 +replay_buffer/size 999047 +trainer/num train calls 94000 +trainer/QF1 Loss 1.23026 +trainer/QF2 Loss 1.22265 +trainer/Policy Loss 13.2495 +trainer/Q1 Predictions Mean -70.4115 +trainer/Q1 Predictions Std 19.432 +trainer/Q1 Predictions Max -2.57334 +trainer/Q1 Predictions Min -87.107 +trainer/Q2 Predictions Mean -70.3967 +trainer/Q2 Predictions Std 19.43 +trainer/Q2 Predictions Max -2.81268 +trainer/Q2 Predictions Min -87.4487 +trainer/Q Targets Mean -70.0609 +trainer/Q Targets Std 19.3044 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6176 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00509138 +trainer/policy/mean Std 0.690579 +trainer/policy/mean Max 0.996861 +trainer/policy/mean Min -0.995489 +trainer/policy/std Mean 0.453375 +trainer/policy/std Std 0.0253502 +trainer/policy/std Max 0.48677 +trainer/policy/std Min 0.413553 +trainer/Advantage Weights Mean 2.52296 +trainer/Advantage Weights Std 13.745 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.34009e-10 +trainer/Advantage Score Mean -0.463844 +trainer/Advantage Score Std 0.398726 +trainer/Advantage Score Max 1.70725 +trainer/Advantage Score Min -2.27331 +trainer/V1 Predictions Mean -69.8291 +trainer/V1 Predictions Std 19.3273 +trainer/V1 Predictions Max -2.07412 +trainer/V1 Predictions Min -86.5234 +trainer/VF Loss 0.0544248 +expl/num steps total 94000 +expl/num paths total 95 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.020314 +expl/Actions Std 0.82563 +expl/Actions Max 2.37317 +expl/Actions Min -2.61732 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 93701 +eval/num paths total 94 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.303605 +eval/Actions Std 0.624595 +eval/Actions Max 0.996933 +eval/Actions Min -0.994453 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95741e-06 +time/evaluation sampling (s) 3.13192 +time/exploration sampling (s) 4.16076 +time/logging (s) 0.0118094 +time/saving (s) 0.0163687 +time/training (s) 15.3725 +time/epoch (s) 22.6934 +time/total (s) 2174.87 +Epoch -907 +------------------------------ ---------------- +2022-05-15 18:39:02.807060 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -906 finished +------------------------------ ---------------- +epoch -906 +replay_buffer/size 999047 +trainer/num train calls 95000 +trainer/QF1 Loss 1.02799 +trainer/QF2 Loss 0.992382 +trainer/Policy Loss 16.6726 +trainer/Q1 Predictions Mean -68.8229 +trainer/Q1 Predictions Std 21.1189 +trainer/Q1 Predictions Max -1.14641 +trainer/Q1 Predictions Min -86.8759 +trainer/Q2 Predictions Mean -68.912 +trainer/Q2 Predictions Std 21.0391 +trainer/Q2 Predictions Max -1.46857 +trainer/Q2 Predictions Min -86.6851 +trainer/Q Targets Mean -69.2231 +trainer/Q Targets Std 21.2112 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5332 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0170228 +trainer/policy/mean Std 0.706364 +trainer/policy/mean Max 0.998143 +trainer/policy/mean Min -0.998044 +trainer/policy/std Mean 0.452157 +trainer/policy/std Std 0.0234199 +trainer/policy/std Max 0.483495 +trainer/policy/std Min 0.416107 +trainer/Advantage Weights Mean 3.2063 +trainer/Advantage Weights Std 14.6339 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.58171e-12 +trainer/Advantage Score Mean -0.360616 +trainer/Advantage Score Std 0.486341 +trainer/Advantage Score Max 0.806856 +trainer/Advantage Score Min -2.57467 +trainer/V1 Predictions Mean -68.897 +trainer/V1 Predictions Std 21.4038 +trainer/V1 Predictions Max 0.282692 +trainer/V1 Predictions Min -86.6344 +trainer/VF Loss 0.0456244 +expl/num steps total 95000 +expl/num paths total 96 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.213173 +expl/Actions Std 0.817739 +expl/Actions Max 2.42696 +expl/Actions Min -2.59759 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 94701 +eval/num paths total 95 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.18687 +eval/Actions Std 0.718404 +eval/Actions Max 0.995888 +eval/Actions Min -0.99579 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.26009e-06 +time/evaluation sampling (s) 2.99138 +time/exploration sampling (s) 4.211 +time/logging (s) 0.010687 +time/saving (s) 0.0158322 +time/training (s) 14.9026 +time/epoch (s) 22.1315 +time/total (s) 2197 +Epoch -906 +------------------------------ ---------------- +2022-05-15 18:39:25.827619 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -905 finished +------------------------------ ---------------- +epoch -905 +replay_buffer/size 999047 +trainer/num train calls 96000 +trainer/QF1 Loss 22.0879 +trainer/QF2 Loss 22.0868 +trainer/Policy Loss 11.4648 +trainer/Q1 Predictions Mean -71.0069 +trainer/Q1 Predictions Std 18.368 +trainer/Q1 Predictions Max -4.3434 +trainer/Q1 Predictions Min -87.2823 +trainer/Q2 Predictions Mean -70.9868 +trainer/Q2 Predictions Std 18.3659 +trainer/Q2 Predictions Max -4.59391 +trainer/Q2 Predictions Min -87.4611 +trainer/Q Targets Mean -70.8231 +trainer/Q Targets Std 18.1045 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9789 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0240324 +trainer/policy/mean Std 0.702907 +trainer/policy/mean Max 0.997672 +trainer/policy/mean Min -0.998784 +trainer/policy/std Mean 0.452223 +trainer/policy/std Std 0.0243384 +trainer/policy/std Max 0.484247 +trainer/policy/std Min 0.413215 +trainer/Advantage Weights Mean 2.50951 +trainer/Advantage Weights Std 13.0654 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.80436e-15 +trainer/Advantage Score Mean -0.472577 +trainer/Advantage Score Std 0.498241 +trainer/Advantage Score Max 0.603626 +trainer/Advantage Score Min -3.23635 +trainer/V1 Predictions Mean -70.231 +trainer/V1 Predictions Std 18.6385 +trainer/V1 Predictions Max -1.46133 +trainer/V1 Predictions Min -86.8646 +trainer/VF Loss 0.0536287 +expl/num steps total 96000 +expl/num paths total 97 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0111581 +expl/Actions Std 0.826121 +expl/Actions Max 2.78765 +expl/Actions Min -2.59164 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 95701 +eval/num paths total 96 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.183019 +eval/Actions Std 0.576813 +eval/Actions Max 0.996456 +eval/Actions Min -0.995183 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.07433e-05 +time/evaluation sampling (s) 3.37289 +time/exploration sampling (s) 4.3158 +time/logging (s) 0.00735806 +time/saving (s) 0.0109668 +time/training (s) 15.3011 +time/epoch (s) 23.0082 +time/total (s) 2220.02 +Epoch -905 +------------------------------ ---------------- +2022-05-15 18:39:48.235687 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -904 finished +------------------------------ ---------------- +epoch -904 +replay_buffer/size 999047 +trainer/num train calls 97000 +trainer/QF1 Loss 2.0537 +trainer/QF2 Loss 2.063 +trainer/Policy Loss 63.9996 +trainer/Q1 Predictions Mean -69.3142 +trainer/Q1 Predictions Std 19.4434 +trainer/Q1 Predictions Max -1.75908 +trainer/Q1 Predictions Min -86.6048 +trainer/Q2 Predictions Mean -69.2367 +trainer/Q2 Predictions Std 19.4227 +trainer/Q2 Predictions Max -1.14457 +trainer/Q2 Predictions Min -86.8566 +trainer/Q Targets Mean -70.0608 +trainer/Q Targets Std 19.0961 +trainer/Q Targets Max -2.04525 +trainer/Q Targets Min -87.9102 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00132261 +trainer/policy/mean Std 0.694187 +trainer/policy/mean Max 0.999192 +trainer/policy/mean Min -0.995763 +trainer/policy/std Mean 0.450813 +trainer/policy/std Std 0.023118 +trainer/policy/std Max 0.480483 +trainer/policy/std Min 0.411711 +trainer/Advantage Weights Mean 11.9576 +trainer/Advantage Weights Std 29.378 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.43042e-10 +trainer/Advantage Score Mean -0.208783 +trainer/Advantage Score Std 0.529136 +trainer/Advantage Score Max 1.60526 +trainer/Advantage Score Min -2.26679 +trainer/V1 Predictions Mean -69.7446 +trainer/V1 Predictions Std 19.4487 +trainer/V1 Predictions Max -1.53568 +trainer/V1 Predictions Min -87.3171 +trainer/VF Loss 0.0887272 +expl/num steps total 97000 +expl/num paths total 98 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0191975 +expl/Actions Std 0.832304 +expl/Actions Max 2.79315 +expl/Actions Min -2.5824 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 96701 +eval/num paths total 97 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.270135 +eval/Actions Std 0.729795 +eval/Actions Max 0.995245 +eval/Actions Min -0.99751 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.12905e-06 +time/evaluation sampling (s) 3.34525 +time/exploration sampling (s) 4.03197 +time/logging (s) 0.0136889 +time/saving (s) 0.0124013 +time/training (s) 15.004 +time/epoch (s) 22.4074 +time/total (s) 2242.43 +Epoch -904 +------------------------------ ---------------- +2022-05-15 18:40:09.683817 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -903 finished +------------------------------ ---------------- +epoch -903 +replay_buffer/size 999047 +trainer/num train calls 98000 +trainer/QF1 Loss 1.39109 +trainer/QF2 Loss 1.36922 +trainer/Policy Loss 4.1568 +trainer/Q1 Predictions Mean -70.606 +trainer/Q1 Predictions Std 16.254 +trainer/Q1 Predictions Max -3.51689 +trainer/Q1 Predictions Min -86.985 +trainer/Q2 Predictions Mean -70.567 +trainer/Q2 Predictions Std 16.2901 +trainer/Q2 Predictions Max -2.99911 +trainer/Q2 Predictions Min -87.1138 +trainer/Q Targets Mean -70.4461 +trainer/Q Targets Std 16.4365 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1098 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00454138 +trainer/policy/mean Std 0.678947 +trainer/policy/mean Max 0.997638 +trainer/policy/mean Min -0.993794 +trainer/policy/std Mean 0.452139 +trainer/policy/std Std 0.0246753 +trainer/policy/std Max 0.484887 +trainer/policy/std Min 0.413395 +trainer/Advantage Weights Mean 0.912958 +trainer/Advantage Weights Std 6.74585 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.11567e-16 +trainer/Advantage Score Mean -0.574275 +trainer/Advantage Score Std 0.514569 +trainer/Advantage Score Max 0.681591 +trainer/Advantage Score Min -3.57049 +trainer/V1 Predictions Mean -70.0935 +trainer/V1 Predictions Std 16.6651 +trainer/V1 Predictions Max -0.272987 +trainer/V1 Predictions Min -86.0704 +trainer/VF Loss 0.0625253 +expl/num steps total 98000 +expl/num paths total 99 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0459272 +expl/Actions Std 0.77297 +expl/Actions Max 2.19964 +expl/Actions Min -2.38698 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 97701 +eval/num paths total 98 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.022042 +eval/Actions Std 0.660024 +eval/Actions Max 0.997277 +eval/Actions Min -0.996884 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.13111e-06 +time/evaluation sampling (s) 3.28704 +time/exploration sampling (s) 3.84021 +time/logging (s) 0.0119836 +time/saving (s) 0.0191988 +time/training (s) 14.2754 +time/epoch (s) 21.4338 +time/total (s) 2263.88 +Epoch -903 +------------------------------ ---------------- +2022-05-15 18:40:30.853251 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -902 finished +------------------------------ ---------------- +epoch -902 +replay_buffer/size 999047 +trainer/num train calls 99000 +trainer/QF1 Loss 0.514647 +trainer/QF2 Loss 0.596717 +trainer/Policy Loss 39.6116 +trainer/Q1 Predictions Mean -71.1147 +trainer/Q1 Predictions Std 17.8921 +trainer/Q1 Predictions Max -2.95766 +trainer/Q1 Predictions Min -86.2433 +trainer/Q2 Predictions Mean -71.2075 +trainer/Q2 Predictions Std 17.8495 +trainer/Q2 Predictions Max -3.6563 +trainer/Q2 Predictions Min -86.4912 +trainer/Q Targets Mean -71.364 +trainer/Q Targets Std 18.1034 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8505 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.012057 +trainer/policy/mean Std 0.692139 +trainer/policy/mean Max 0.99511 +trainer/policy/mean Min -0.997334 +trainer/policy/std Mean 0.45086 +trainer/policy/std Std 0.0228895 +trainer/policy/std Max 0.481748 +trainer/policy/std Min 0.41648 +trainer/Advantage Weights Mean 6.87235 +trainer/Advantage Weights Std 20.2824 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.74001e-09 +trainer/Advantage Score Mean -0.218511 +trainer/Advantage Score Std 0.478686 +trainer/Advantage Score Max 1.20288 +trainer/Advantage Score Min -2.01694 +trainer/V1 Predictions Mean -71.0494 +trainer/V1 Predictions Std 18.2672 +trainer/V1 Predictions Max -2.39517 +trainer/V1 Predictions Min -86.2084 +trainer/VF Loss 0.0537504 +expl/num steps total 99000 +expl/num paths total 100 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0155321 +expl/Actions Std 0.807614 +expl/Actions Max 2.36822 +expl/Actions Min -2.394 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 98701 +eval/num paths total 99 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0049326 +eval/Actions Std 0.700654 +eval/Actions Max 0.998161 +eval/Actions Min -0.998637 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.64402e-06 +time/evaluation sampling (s) 3.00122 +time/exploration sampling (s) 3.61779 +time/logging (s) 0.0101637 +time/saving (s) 0.0136909 +time/training (s) 14.5147 +time/epoch (s) 21.1576 +time/total (s) 2285.04 +Epoch -902 +------------------------------ ---------------- +2022-05-15 18:40:52.601845 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -901 finished +------------------------------ ---------------- +epoch -901 +replay_buffer/size 999047 +trainer/num train calls 100000 +trainer/QF1 Loss 0.843654 +trainer/QF2 Loss 0.892841 +trainer/Policy Loss 12.556 +trainer/Q1 Predictions Mean -71.6177 +trainer/Q1 Predictions Std 17.8345 +trainer/Q1 Predictions Max -0.746425 +trainer/Q1 Predictions Min -87.0949 +trainer/Q2 Predictions Mean -71.5197 +trainer/Q2 Predictions Std 17.8725 +trainer/Q2 Predictions Max -0.729252 +trainer/Q2 Predictions Min -87.8357 +trainer/Q Targets Mean -71.6893 +trainer/Q Targets Std 18.0154 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.639 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0234657 +trainer/policy/mean Std 0.700585 +trainer/policy/mean Max 0.998126 +trainer/policy/mean Min -0.996186 +trainer/policy/std Mean 0.449947 +trainer/policy/std Std 0.0240922 +trainer/policy/std Max 0.478838 +trainer/policy/std Min 0.410962 +trainer/Advantage Weights Mean 2.85239 +trainer/Advantage Weights Std 14.7269 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.11737e-13 +trainer/Advantage Score Mean -0.487805 +trainer/Advantage Score Std 0.475263 +trainer/Advantage Score Max 0.872475 +trainer/Advantage Score Min -2.87966 +trainer/V1 Predictions Mean -71.4099 +trainer/V1 Predictions Std 18.1242 +trainer/V1 Predictions Max -0.134964 +trainer/V1 Predictions Min -87.0635 +trainer/VF Loss 0.0543652 +expl/num steps total 100000 +expl/num paths total 101 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0141221 +expl/Actions Std 0.805555 +expl/Actions Max 2.41023 +expl/Actions Min -2.55178 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 99701 +eval/num paths total 100 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.21887 +eval/Actions Std 0.600767 +eval/Actions Max 0.99797 +eval/Actions Min -0.994885 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.65986e-06 +time/evaluation sampling (s) 3.27373 +time/exploration sampling (s) 3.74194 +time/logging (s) 0.00817313 +time/saving (s) 0.011468 +time/training (s) 14.7021 +time/epoch (s) 21.7374 +time/total (s) 2306.78 +Epoch -901 +------------------------------ ---------------- +2022-05-15 18:41:13.448933 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -900 finished +------------------------------ ---------------- +epoch -900 +replay_buffer/size 999047 +trainer/num train calls 101000 +trainer/QF1 Loss 0.498558 +trainer/QF2 Loss 0.524017 +trainer/Policy Loss 35.06 +trainer/Q1 Predictions Mean -72.8578 +trainer/Q1 Predictions Std 15.5264 +trainer/Q1 Predictions Max -0.830729 +trainer/Q1 Predictions Min -87.8781 +trainer/Q2 Predictions Mean -72.8051 +trainer/Q2 Predictions Std 15.5684 +trainer/Q2 Predictions Max -0.364495 +trainer/Q2 Predictions Min -86.6489 +trainer/Q Targets Mean -73.0933 +trainer/Q Targets Std 15.6245 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8331 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00389599 +trainer/policy/mean Std 0.691237 +trainer/policy/mean Max 0.9963 +trainer/policy/mean Min -0.996188 +trainer/policy/std Mean 0.449185 +trainer/policy/std Std 0.0237244 +trainer/policy/std Max 0.479545 +trainer/policy/std Min 0.410237 +trainer/Advantage Weights Mean 7.55769 +trainer/Advantage Weights Std 22.4745 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3675e-12 +trainer/Advantage Score Mean -0.215255 +trainer/Advantage Score Std 0.478733 +trainer/Advantage Score Max 1.41003 +trainer/Advantage Score Min -2.7318 +trainer/V1 Predictions Mean -72.7768 +trainer/V1 Predictions Std 15.7104 +trainer/V1 Predictions Max 0.18726 +trainer/V1 Predictions Min -86.6714 +trainer/VF Loss 0.0569495 +expl/num steps total 101000 +expl/num paths total 102 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.027867 +expl/Actions Std 0.802979 +expl/Actions Max 2.4384 +expl/Actions Min -2.34679 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 100701 +eval/num paths total 101 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0866406 +eval/Actions Std 0.628671 +eval/Actions Max 0.998332 +eval/Actions Min -0.992757 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.05101e-06 +time/evaluation sampling (s) 3.05472 +time/exploration sampling (s) 3.263 +time/logging (s) 0.00660358 +time/saving (s) 0.0179461 +time/training (s) 14.4969 +time/epoch (s) 20.8392 +time/total (s) 2327.63 +Epoch -900 +------------------------------ ---------------- +2022-05-15 18:41:34.365745 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -899 finished +------------------------------ ---------------- +epoch -899 +replay_buffer/size 999047 +trainer/num train calls 102000 +trainer/QF1 Loss 0.724777 +trainer/QF2 Loss 0.746475 +trainer/Policy Loss 27.3951 +trainer/Q1 Predictions Mean -71.9643 +trainer/Q1 Predictions Std 16.3181 +trainer/Q1 Predictions Max -3.27772 +trainer/Q1 Predictions Min -87.5982 +trainer/Q2 Predictions Mean -71.8863 +trainer/Q2 Predictions Std 16.3254 +trainer/Q2 Predictions Max -2.51159 +trainer/Q2 Predictions Min -87.9554 +trainer/Q Targets Mean -72.0458 +trainer/Q Targets Std 16.4014 +trainer/Q Targets Max -2.10408 +trainer/Q Targets Min -87.1641 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0299887 +trainer/policy/mean Std 0.682895 +trainer/policy/mean Max 0.994086 +trainer/policy/mean Min -0.995058 +trainer/policy/std Mean 0.448849 +trainer/policy/std Std 0.0239674 +trainer/policy/std Max 0.478594 +trainer/policy/std Min 0.410097 +trainer/Advantage Weights Mean 3.63125 +trainer/Advantage Weights Std 13.6752 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.17751e-14 +trainer/Advantage Score Mean -0.293314 +trainer/Advantage Score Std 0.471562 +trainer/Advantage Score Max 1.41692 +trainer/Advantage Score Min -3.10801 +trainer/V1 Predictions Mean -71.8365 +trainer/V1 Predictions Std 16.4039 +trainer/V1 Predictions Max -2.05971 +trainer/V1 Predictions Min -87.3592 +trainer/VF Loss 0.0480976 +expl/num steps total 102000 +expl/num paths total 103 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.104811 +expl/Actions Std 0.805317 +expl/Actions Max 2.46508 +expl/Actions Min -2.33512 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 101701 +eval/num paths total 102 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.11511 +eval/Actions Std 0.753466 +eval/Actions Max 0.998175 +eval/Actions Min -0.997965 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.01236e-06 +time/evaluation sampling (s) 2.94935 +time/exploration sampling (s) 3.34726 +time/logging (s) 0.0113169 +time/saving (s) 0.0141431 +time/training (s) 14.5938 +time/epoch (s) 20.9159 +time/total (s) 2348.55 +Epoch -899 +------------------------------ ---------------- +2022-05-15 18:41:54.955608 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -898 finished +------------------------------ ---------------- +epoch -898 +replay_buffer/size 999047 +trainer/num train calls 103000 +trainer/QF1 Loss 1.55093 +trainer/QF2 Loss 1.6554 +trainer/Policy Loss 32.9176 +trainer/Q1 Predictions Mean -71.0792 +trainer/Q1 Predictions Std 17.2586 +trainer/Q1 Predictions Max -1.04141 +trainer/Q1 Predictions Min -86.3187 +trainer/Q2 Predictions Mean -71.08 +trainer/Q2 Predictions Std 17.263 +trainer/Q2 Predictions Max -0.900551 +trainer/Q2 Predictions Min -86.2916 +trainer/Q Targets Mean -71.6662 +trainer/Q Targets Std 17.3075 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9232 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00325615 +trainer/policy/mean Std 0.69867 +trainer/policy/mean Max 0.997145 +trainer/policy/mean Min -0.997657 +trainer/policy/std Mean 0.449113 +trainer/policy/std Std 0.0232963 +trainer/policy/std Max 0.476058 +trainer/policy/std Min 0.411731 +trainer/Advantage Weights Mean 7.07871 +trainer/Advantage Weights Std 20.9962 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.03166e-10 +trainer/Advantage Score Mean -0.212385 +trainer/Advantage Score Std 0.470037 +trainer/Advantage Score Max 1.22619 +trainer/Advantage Score Min -2.12288 +trainer/V1 Predictions Mean -71.383 +trainer/V1 Predictions Std 17.3851 +trainer/V1 Predictions Max 0.160382 +trainer/V1 Predictions Min -86.9728 +trainer/VF Loss 0.0556036 +expl/num steps total 103000 +expl/num paths total 104 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0204568 +expl/Actions Std 0.81113 +expl/Actions Max 2.34197 +expl/Actions Min -2.46275 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 102701 +eval/num paths total 103 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.101466 +eval/Actions Std 0.701306 +eval/Actions Max 0.999817 +eval/Actions Min -0.997857 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.74274e-06 +time/evaluation sampling (s) 2.93214 +time/exploration sampling (s) 3.41926 +time/logging (s) 0.0136415 +time/saving (s) 0.0212861 +time/training (s) 14.1976 +time/epoch (s) 20.5839 +time/total (s) 2369.14 +Epoch -898 +------------------------------ ---------------- +2022-05-15 18:42:13.896726 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -897 finished +------------------------------ ---------------- +epoch -897 +replay_buffer/size 999047 +trainer/num train calls 104000 +trainer/QF1 Loss 0.372916 +trainer/QF2 Loss 0.460812 +trainer/Policy Loss 18.7399 +trainer/Q1 Predictions Mean -70.284 +trainer/Q1 Predictions Std 18.1626 +trainer/Q1 Predictions Max -1.29803 +trainer/Q1 Predictions Min -87.0311 +trainer/Q2 Predictions Mean -70.2158 +trainer/Q2 Predictions Std 18.1807 +trainer/Q2 Predictions Max -1.90418 +trainer/Q2 Predictions Min -87.5937 +trainer/Q Targets Mean -70.4182 +trainer/Q Targets Std 18.2363 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9281 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00475789 +trainer/policy/mean Std 0.686903 +trainer/policy/mean Max 0.996048 +trainer/policy/mean Min -0.995906 +trainer/policy/std Mean 0.449436 +trainer/policy/std Std 0.023618 +trainer/policy/std Max 0.47928 +trainer/policy/std Min 0.412063 +trainer/Advantage Weights Mean 3.24665 +trainer/Advantage Weights Std 12.3562 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.99598e-15 +trainer/Advantage Score Mean -0.304053 +trainer/Advantage Score Std 0.49592 +trainer/Advantage Score Max 0.826134 +trainer/Advantage Score Min -3.25934 +trainer/V1 Predictions Mean -70.1668 +trainer/V1 Predictions Std 18.3297 +trainer/V1 Predictions Max -1.0234 +trainer/V1 Predictions Min -86.828 +trainer/VF Loss 0.0442545 +expl/num steps total 104000 +expl/num paths total 105 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0263957 +expl/Actions Std 0.794613 +expl/Actions Max 2.4562 +expl/Actions Min -2.33128 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 103701 +eval/num paths total 104 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.239791 +eval/Actions Std 0.754135 +eval/Actions Max 0.997258 +eval/Actions Min -0.996547 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.74229e-06 +time/evaluation sampling (s) 2.67354 +time/exploration sampling (s) 3.0255 +time/logging (s) 0.0100661 +time/saving (s) 0.0151788 +time/training (s) 13.2048 +time/epoch (s) 18.9291 +time/total (s) 2388.07 +Epoch -897 +------------------------------ ---------------- +2022-05-15 18:42:32.934090 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -896 finished +------------------------------ ---------------- +epoch -896 +replay_buffer/size 999047 +trainer/num train calls 105000 +trainer/QF1 Loss 0.650046 +trainer/QF2 Loss 0.634111 +trainer/Policy Loss 9.55542 +trainer/Q1 Predictions Mean -72.7577 +trainer/Q1 Predictions Std 14.3154 +trainer/Q1 Predictions Max -4.70979 +trainer/Q1 Predictions Min -87.3781 +trainer/Q2 Predictions Mean -72.7771 +trainer/Q2 Predictions Std 14.3371 +trainer/Q2 Predictions Max -5.44135 +trainer/Q2 Predictions Min -87.5746 +trainer/Q Targets Mean -72.5607 +trainer/Q Targets Std 14.6559 +trainer/Q Targets Max -4.69129 +trainer/Q Targets Min -87.5238 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00209812 +trainer/policy/mean Std 0.697852 +trainer/policy/mean Max 0.998219 +trainer/policy/mean Min -0.994768 +trainer/policy/std Mean 0.44979 +trainer/policy/std Std 0.023852 +trainer/policy/std Max 0.479522 +trainer/policy/std Min 0.413009 +trainer/Advantage Weights Mean 1.59681 +trainer/Advantage Weights Std 9.39403 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.98019e-11 +trainer/Advantage Score Mean -0.428007 +trainer/Advantage Score Std 0.418191 +trainer/Advantage Score Max 1.07879 +trainer/Advantage Score Min -2.42365 +trainer/V1 Predictions Mean -72.3003 +trainer/V1 Predictions Std 14.7589 +trainer/V1 Predictions Max -3.28909 +trainer/V1 Predictions Min -87.3922 +trainer/VF Loss 0.0428014 +expl/num steps total 105000 +expl/num paths total 106 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00513108 +expl/Actions Std 0.812497 +expl/Actions Max 2.63751 +expl/Actions Min -2.5548 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 104701 +eval/num paths total 105 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0579146 +eval/Actions Std 0.692385 +eval/Actions Max 0.99827 +eval/Actions Min -0.997614 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.79583e-06 +time/evaluation sampling (s) 2.62181 +time/exploration sampling (s) 2.72602 +time/logging (s) 0.00734632 +time/saving (s) 0.0138278 +time/training (s) 13.6604 +time/epoch (s) 19.0294 +time/total (s) 2407.11 +Epoch -896 +------------------------------ ---------------- +2022-05-15 18:42:51.964779 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -895 finished +------------------------------ ---------------- +epoch -895 +replay_buffer/size 999047 +trainer/num train calls 106000 +trainer/QF1 Loss 20.8897 +trainer/QF2 Loss 21.0698 +trainer/Policy Loss 34.7895 +trainer/Q1 Predictions Mean -70.7415 +trainer/Q1 Predictions Std 18.817 +trainer/Q1 Predictions Max -1.46783 +trainer/Q1 Predictions Min -86.5927 +trainer/Q2 Predictions Mean -70.6287 +trainer/Q2 Predictions Std 18.8269 +trainer/Q2 Predictions Max -1.422 +trainer/Q2 Predictions Min -86.4429 +trainer/Q Targets Mean -71.2736 +trainer/Q Targets Std 18.2805 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.945 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00195084 +trainer/policy/mean Std 0.709146 +trainer/policy/mean Max 0.9985 +trainer/policy/mean Min -0.994702 +trainer/policy/std Mean 0.448933 +trainer/policy/std Std 0.0238754 +trainer/policy/std Max 0.475312 +trainer/policy/std Min 0.410214 +trainer/Advantage Weights Mean 6.26662 +trainer/Advantage Weights Std 19.9311 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.24821e-08 +trainer/Advantage Score Mean -0.236673 +trainer/Advantage Score Std 0.420292 +trainer/Advantage Score Max 1.07318 +trainer/Advantage Score Min -1.61963 +trainer/V1 Predictions Mean -70.7301 +trainer/V1 Predictions Std 18.7539 +trainer/V1 Predictions Max -1.99727 +trainer/V1 Predictions Min -86.6913 +trainer/VF Loss 0.0421775 +expl/num steps total 106000 +expl/num paths total 107 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00683813 +expl/Actions Std 0.808616 +expl/Actions Max 2.319 +expl/Actions Min -2.34251 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 105701 +eval/num paths total 106 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0855843 +eval/Actions Std 0.691857 +eval/Actions Max 0.997746 +eval/Actions Min -0.996751 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.17767e-06 +time/evaluation sampling (s) 2.53191 +time/exploration sampling (s) 2.72674 +time/logging (s) 0.00772336 +time/saving (s) 0.0108373 +time/training (s) 13.7477 +time/epoch (s) 19.0249 +time/total (s) 2426.14 +Epoch -895 +------------------------------ ---------------- +2022-05-15 18:43:11.137834 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -894 finished +------------------------------ ---------------- +epoch -894 +replay_buffer/size 999047 +trainer/num train calls 107000 +trainer/QF1 Loss 0.611949 +trainer/QF2 Loss 0.649236 +trainer/Policy Loss 20.3456 +trainer/Q1 Predictions Mean -71.0638 +trainer/Q1 Predictions Std 17.0122 +trainer/Q1 Predictions Max -1.61782 +trainer/Q1 Predictions Min -86.1005 +trainer/Q2 Predictions Mean -71.1258 +trainer/Q2 Predictions Std 16.9917 +trainer/Q2 Predictions Max -1.57082 +trainer/Q2 Predictions Min -85.8679 +trainer/Q Targets Mean -70.955 +trainer/Q Targets Std 17.3846 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1615 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0325046 +trainer/policy/mean Std 0.707299 +trainer/policy/mean Max 0.998332 +trainer/policy/mean Min -0.996822 +trainer/policy/std Mean 0.449026 +trainer/policy/std Std 0.0236902 +trainer/policy/std Max 0.477329 +trainer/policy/std Min 0.408863 +trainer/Advantage Weights Mean 4.50267 +trainer/Advantage Weights Std 17.8536 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.57961e-14 +trainer/Advantage Score Mean -0.341398 +trainer/Advantage Score Std 0.495985 +trainer/Advantage Score Max 0.955399 +trainer/Advantage Score Min -3.00868 +trainer/V1 Predictions Mean -70.6651 +trainer/V1 Predictions Std 17.4663 +trainer/V1 Predictions Max -0.76634 +trainer/V1 Predictions Min -86.1125 +trainer/VF Loss 0.0539274 +expl/num steps total 107000 +expl/num paths total 108 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00241215 +expl/Actions Std 0.75572 +expl/Actions Max 2.52136 +expl/Actions Min -2.48257 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 106701 +eval/num paths total 107 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0218447 +eval/Actions Std 0.709015 +eval/Actions Max 0.998637 +eval/Actions Min -0.996602 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99467e-06 +time/evaluation sampling (s) 2.59641 +time/exploration sampling (s) 2.85072 +time/logging (s) 0.00741504 +time/saving (s) 0.0111835 +time/training (s) 13.7009 +time/epoch (s) 19.1667 +time/total (s) 2445.31 +Epoch -894 +------------------------------ ---------------- +2022-05-15 18:43:30.223760 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -893 finished +------------------------------ ---------------- +epoch -893 +replay_buffer/size 999047 +trainer/num train calls 108000 +trainer/QF1 Loss 0.971336 +trainer/QF2 Loss 1.0543 +trainer/Policy Loss 1.83554 +trainer/Q1 Predictions Mean -71.2532 +trainer/Q1 Predictions Std 17.325 +trainer/Q1 Predictions Max -2.57141 +trainer/Q1 Predictions Min -85.8603 +trainer/Q2 Predictions Mean -71.3074 +trainer/Q2 Predictions Std 17.2909 +trainer/Q2 Predictions Max -2.68465 +trainer/Q2 Predictions Min -85.7611 +trainer/Q Targets Mean -71.0367 +trainer/Q Targets Std 17.7102 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.6162 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00167219 +trainer/policy/mean Std 0.691425 +trainer/policy/mean Max 0.993974 +trainer/policy/mean Min -0.998202 +trainer/policy/std Mean 0.450963 +trainer/policy/std Std 0.0242548 +trainer/policy/std Max 0.482071 +trainer/policy/std Min 0.40874 +trainer/Advantage Weights Mean 0.530941 +trainer/Advantage Weights Std 3.14911 +trainer/Advantage Weights Max 37.4201 +trainer/Advantage Weights Min 1.09216e-13 +trainer/Advantage Score Mean -0.533459 +trainer/Advantage Score Std 0.467191 +trainer/Advantage Score Max 0.362221 +trainer/Advantage Score Min -2.98454 +trainer/V1 Predictions Mean -70.7915 +trainer/V1 Predictions Std 17.7537 +trainer/V1 Predictions Max 0.426674 +trainer/V1 Predictions Min -85.4715 +trainer/VF Loss 0.0517307 +expl/num steps total 108000 +expl/num paths total 109 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0667063 +expl/Actions Std 0.790787 +expl/Actions Max 2.39502 +expl/Actions Min -2.57705 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 107701 +eval/num paths total 108 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0146067 +eval/Actions Std 0.645216 +eval/Actions Max 0.996084 +eval/Actions Min -0.998236 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.94695e-06 +time/evaluation sampling (s) 2.50343 +time/exploration sampling (s) 2.89635 +time/logging (s) 0.0066922 +time/saving (s) 0.0139764 +time/training (s) 13.6587 +time/epoch (s) 19.0791 +time/total (s) 2464.39 +Epoch -893 +------------------------------ ---------------- +2022-05-15 18:43:49.426441 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -892 finished +------------------------------ ---------------- +epoch -892 +replay_buffer/size 999047 +trainer/num train calls 109000 +trainer/QF1 Loss 1.35541 +trainer/QF2 Loss 1.50223 +trainer/Policy Loss 13.8255 +trainer/Q1 Predictions Mean -70.5419 +trainer/Q1 Predictions Std 21.0153 +trainer/Q1 Predictions Max -1.72336 +trainer/Q1 Predictions Min -86.615 +trainer/Q2 Predictions Mean -70.559 +trainer/Q2 Predictions Std 21.1047 +trainer/Q2 Predictions Max -2.11418 +trainer/Q2 Predictions Min -86.7962 +trainer/Q Targets Mean -70.0273 +trainer/Q Targets Std 21.2507 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0581 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0148744 +trainer/policy/mean Std 0.694325 +trainer/policy/mean Max 0.99624 +trainer/policy/mean Min -0.991839 +trainer/policy/std Mean 0.44897 +trainer/policy/std Std 0.024725 +trainer/policy/std Max 0.479811 +trainer/policy/std Min 0.406832 +trainer/Advantage Weights Mean 2.73629 +trainer/Advantage Weights Std 13.5375 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.64143e-12 +trainer/Advantage Score Mean -0.448306 +trainer/Advantage Score Std 0.484289 +trainer/Advantage Score Max 1.10834 +trainer/Advantage Score Min -2.5365 +trainer/V1 Predictions Mean -69.8531 +trainer/V1 Predictions Std 21.1408 +trainer/V1 Predictions Max -1.33114 +trainer/V1 Predictions Min -86.0902 +trainer/VF Loss 0.0540176 +expl/num steps total 109000 +expl/num paths total 110 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0289281 +expl/Actions Std 0.783539 +expl/Actions Max 2.28774 +expl/Actions Min -2.21746 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 108701 +eval/num paths total 109 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0657 +eval/Actions Std 0.686076 +eval/Actions Max 0.999589 +eval/Actions Min -0.999621 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.03704e-06 +time/evaluation sampling (s) 2.40996 +time/exploration sampling (s) 2.86158 +time/logging (s) 0.00723965 +time/saving (s) 0.0111673 +time/training (s) 13.9074 +time/epoch (s) 19.1973 +time/total (s) 2483.59 +Epoch -892 +------------------------------ ---------------- +2022-05-15 18:44:08.427365 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -891 finished +------------------------------ ---------------- +epoch -891 +replay_buffer/size 999047 +trainer/num train calls 110000 +trainer/QF1 Loss 1.17256 +trainer/QF2 Loss 1.21771 +trainer/Policy Loss 12.0365 +trainer/Q1 Predictions Mean -71.9162 +trainer/Q1 Predictions Std 18.1238 +trainer/Q1 Predictions Max -0.610809 +trainer/Q1 Predictions Min -86.4165 +trainer/Q2 Predictions Mean -71.9656 +trainer/Q2 Predictions Std 18.085 +trainer/Q2 Predictions Max -0.751857 +trainer/Q2 Predictions Min -86.251 +trainer/Q Targets Mean -71.4378 +trainer/Q Targets Std 18.0935 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.326 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.01515 +trainer/policy/mean Std 0.68145 +trainer/policy/mean Max 0.994159 +trainer/policy/mean Min -0.995436 +trainer/policy/std Mean 0.448861 +trainer/policy/std Std 0.0257248 +trainer/policy/std Max 0.478683 +trainer/policy/std Min 0.40718 +trainer/Advantage Weights Mean 1.84769 +trainer/Advantage Weights Std 9.88499 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.53423e-13 +trainer/Advantage Score Mean -0.503413 +trainer/Advantage Score Std 0.512992 +trainer/Advantage Score Max 0.827522 +trainer/Advantage Score Min -2.95056 +trainer/V1 Predictions Mean -71.139 +trainer/V1 Predictions Std 18.2666 +trainer/V1 Predictions Max 0.0279278 +trainer/V1 Predictions Min -86.1863 +trainer/VF Loss 0.0584582 +expl/num steps total 110000 +expl/num paths total 111 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.02331 +expl/Actions Std 0.775646 +expl/Actions Max 2.56248 +expl/Actions Min -2.52778 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 109701 +eval/num paths total 110 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0463798 +eval/Actions Std 0.691084 +eval/Actions Max 0.996985 +eval/Actions Min -0.998052 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.03891e-06 +time/evaluation sampling (s) 2.50378 +time/exploration sampling (s) 2.89638 +time/logging (s) 0.00658533 +time/saving (s) 0.0102296 +time/training (s) 13.5761 +time/epoch (s) 18.9931 +time/total (s) 2502.59 +Epoch -891 +------------------------------ ---------------- +2022-05-15 18:44:27.558626 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -890 finished +------------------------------ ---------------- +epoch -890 +replay_buffer/size 999047 +trainer/num train calls 111000 +trainer/QF1 Loss 1.4342 +trainer/QF2 Loss 1.42807 +trainer/Policy Loss 7.74501 +trainer/Q1 Predictions Mean -69.1498 +trainer/Q1 Predictions Std 20.7747 +trainer/Q1 Predictions Max -1.42419 +trainer/Q1 Predictions Min -86.6299 +trainer/Q2 Predictions Mean -69.1197 +trainer/Q2 Predictions Std 20.8027 +trainer/Q2 Predictions Max -1.43078 +trainer/Q2 Predictions Min -86.7542 +trainer/Q Targets Mean -68.7259 +trainer/Q Targets Std 20.6497 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4001 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0146578 +trainer/policy/mean Std 0.704808 +trainer/policy/mean Max 0.99641 +trainer/policy/mean Min -0.995458 +trainer/policy/std Mean 0.448588 +trainer/policy/std Std 0.0236506 +trainer/policy/std Max 0.479295 +trainer/policy/std Min 0.412211 +trainer/Advantage Weights Mean 1.43729 +trainer/Advantage Weights Std 9.0649 +trainer/Advantage Weights Max 97.3733 +trainer/Advantage Weights Min 7.50073e-13 +trainer/Advantage Score Mean -0.620243 +trainer/Advantage Score Std 0.584677 +trainer/Advantage Score Max 0.457855 +trainer/Advantage Score Min -2.79186 +trainer/V1 Predictions Mean -68.386 +trainer/V1 Predictions Std 21.1151 +trainer/V1 Predictions Max -0.397685 +trainer/V1 Predictions Min -86.2607 +trainer/VF Loss 0.0758709 +expl/num steps total 111000 +expl/num paths total 112 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0407318 +expl/Actions Std 0.773664 +expl/Actions Max 2.39651 +expl/Actions Min -2.29942 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 110701 +eval/num paths total 111 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.137858 +eval/Actions Std 0.701688 +eval/Actions Max 0.994192 +eval/Actions Min -0.993422 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.21912e-06 +time/evaluation sampling (s) 2.48708 +time/exploration sampling (s) 2.809 +time/logging (s) 0.00815301 +time/saving (s) 0.0112682 +time/training (s) 13.8112 +time/epoch (s) 19.1267 +time/total (s) 2521.72 +Epoch -890 +------------------------------ ---------------- +2022-05-15 18:44:46.447655 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -889 finished +------------------------------ ---------------- +epoch -889 +replay_buffer/size 999047 +trainer/num train calls 112000 +trainer/QF1 Loss 0.536333 +trainer/QF2 Loss 0.528441 +trainer/Policy Loss 25.6391 +trainer/Q1 Predictions Mean -72.0546 +trainer/Q1 Predictions Std 17.6921 +trainer/Q1 Predictions Max -1.05047 +trainer/Q1 Predictions Min -87.0015 +trainer/Q2 Predictions Mean -72.0289 +trainer/Q2 Predictions Std 17.6763 +trainer/Q2 Predictions Max -1.13715 +trainer/Q2 Predictions Min -87.0341 +trainer/Q Targets Mean -71.952 +trainer/Q Targets Std 17.5568 +trainer/Q Targets Max -0.90388 +trainer/Q Targets Min -86.3411 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00775755 +trainer/policy/mean Std 0.694633 +trainer/policy/mean Max 0.998873 +trainer/policy/mean Min -0.996478 +trainer/policy/std Mean 0.448553 +trainer/policy/std Std 0.0241734 +trainer/policy/std Max 0.481497 +trainer/policy/std Min 0.411806 +trainer/Advantage Weights Mean 3.11654 +trainer/Advantage Weights Std 15.3177 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.1189e-11 +trainer/Advantage Score Mean -0.312502 +trainer/Advantage Score Std 0.375241 +trainer/Advantage Score Max 0.827202 +trainer/Advantage Score Min -2.39128 +trainer/V1 Predictions Mean -71.7915 +trainer/V1 Predictions Std 17.5393 +trainer/V1 Predictions Max -0.32921 +trainer/V1 Predictions Min -86.1427 +trainer/VF Loss 0.0341631 +expl/num steps total 112000 +expl/num paths total 113 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.059925 +expl/Actions Std 0.804344 +expl/Actions Max 2.26901 +expl/Actions Min -2.47484 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 111701 +eval/num paths total 112 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.2392 +eval/Actions Std 0.658039 +eval/Actions Max 0.997601 +eval/Actions Min -0.998237 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 5.18793e-06 +time/evaluation sampling (s) 2.34658 +time/exploration sampling (s) 2.9199 +time/logging (s) 0.0101418 +time/saving (s) 0.0138509 +time/training (s) 13.5949 +time/epoch (s) 18.8854 +time/total (s) 2540.61 +Epoch -889 +------------------------------ ---------------- +2022-05-15 18:45:05.929922 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -888 finished +------------------------------ ---------------- +epoch -888 +replay_buffer/size 999047 +trainer/num train calls 113000 +trainer/QF1 Loss 1.01671 +trainer/QF2 Loss 1.07575 +trainer/Policy Loss 3.06394 +trainer/Q1 Predictions Mean -69.9277 +trainer/Q1 Predictions Std 19.8826 +trainer/Q1 Predictions Max -1.27976 +trainer/Q1 Predictions Min -87.45 +trainer/Q2 Predictions Mean -69.8662 +trainer/Q2 Predictions Std 19.9689 +trainer/Q2 Predictions Max -1.22398 +trainer/Q2 Predictions Min -87.8375 +trainer/Q Targets Mean -70.0397 +trainer/Q Targets Std 19.614 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6392 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0171645 +trainer/policy/mean Std 0.69119 +trainer/policy/mean Max 0.997005 +trainer/policy/mean Min -0.999255 +trainer/policy/std Mean 0.448157 +trainer/policy/std Std 0.024605 +trainer/policy/std Max 0.480376 +trainer/policy/std Min 0.409855 +trainer/Advantage Weights Mean 0.754434 +trainer/Advantage Weights Std 4.00386 +trainer/Advantage Weights Max 50.3842 +trainer/Advantage Weights Min 8.82662e-11 +trainer/Advantage Score Mean -0.506293 +trainer/Advantage Score Std 0.432866 +trainer/Advantage Score Max 0.391968 +trainer/Advantage Score Min -2.31507 +trainer/V1 Predictions Mean -69.7501 +trainer/V1 Predictions Std 19.7192 +trainer/V1 Predictions Max -1.11263 +trainer/V1 Predictions Min -87.4002 +trainer/VF Loss 0.0466265 +expl/num steps total 113000 +expl/num paths total 114 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.022883 +expl/Actions Std 0.831412 +expl/Actions Max 2.3562 +expl/Actions Min -2.30563 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 112701 +eval/num paths total 113 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.466848 +eval/Actions Std 0.665859 +eval/Actions Max 0.997635 +eval/Actions Min -0.997658 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.05194e-06 +time/evaluation sampling (s) 2.66941 +time/exploration sampling (s) 3.10849 +time/logging (s) 0.0106977 +time/saving (s) 0.0146345 +time/training (s) 13.6709 +time/epoch (s) 19.4741 +time/total (s) 2560.09 +Epoch -888 +------------------------------ ---------------- +2022-05-15 18:45:24.917892 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -887 finished +------------------------------ ---------------- +epoch -887 +replay_buffer/size 999047 +trainer/num train calls 114000 +trainer/QF1 Loss 0.879437 +trainer/QF2 Loss 0.882759 +trainer/Policy Loss 25.6971 +trainer/Q1 Predictions Mean -70.6065 +trainer/Q1 Predictions Std 19.457 +trainer/Q1 Predictions Max -2.26888 +trainer/Q1 Predictions Min -87.2819 +trainer/Q2 Predictions Mean -70.6555 +trainer/Q2 Predictions Std 19.3698 +trainer/Q2 Predictions Max -2.43519 +trainer/Q2 Predictions Min -87.153 +trainer/Q Targets Mean -70.7052 +trainer/Q Targets Std 19.5081 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7038 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0232813 +trainer/policy/mean Std 0.69838 +trainer/policy/mean Max 0.999599 +trainer/policy/mean Min -0.997741 +trainer/policy/std Mean 0.448156 +trainer/policy/std Std 0.0238248 +trainer/policy/std Max 0.47799 +trainer/policy/std Min 0.414697 +trainer/Advantage Weights Mean 5.92194 +trainer/Advantage Weights Std 20.9031 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.39793e-11 +trainer/Advantage Score Mean -0.277576 +trainer/Advantage Score Std 0.488433 +trainer/Advantage Score Max 2.37049 +trainer/Advantage Score Min -2.44538 +trainer/V1 Predictions Mean -70.4416 +trainer/V1 Predictions Std 19.4732 +trainer/V1 Predictions Max -2.49146 +trainer/V1 Predictions Min -86.6702 +trainer/VF Loss 0.0676005 +expl/num steps total 114000 +expl/num paths total 115 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0393426 +expl/Actions Std 0.772913 +expl/Actions Max 2.29876 +expl/Actions Min -2.38653 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 113701 +eval/num paths total 114 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0730238 +eval/Actions Std 0.66808 +eval/Actions Max 0.99665 +eval/Actions Min -0.995239 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.98116e-06 +time/evaluation sampling (s) 2.41074 +time/exploration sampling (s) 2.99013 +time/logging (s) 0.0117141 +time/saving (s) 0.0153469 +time/training (s) 13.552 +time/epoch (s) 18.98 +time/total (s) 2579.08 +Epoch -887 +------------------------------ ---------------- +2022-05-15 18:45:43.912485 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -886 finished +------------------------------ ---------------- +epoch -886 +replay_buffer/size 999047 +trainer/num train calls 115000 +trainer/QF1 Loss 13.5779 +trainer/QF2 Loss 13.6956 +trainer/Policy Loss 4.59734 +trainer/Q1 Predictions Mean -72.049 +trainer/Q1 Predictions Std 17.2047 +trainer/Q1 Predictions Max -2.16731 +trainer/Q1 Predictions Min -86.8329 +trainer/Q2 Predictions Mean -72.0333 +trainer/Q2 Predictions Std 17.1657 +trainer/Q2 Predictions Max -1.69204 +trainer/Q2 Predictions Min -86.9181 +trainer/Q Targets Mean -71.4813 +trainer/Q Targets Std 17.5427 +trainer/Q Targets Max -1.75209 +trainer/Q Targets Min -86.8272 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0101778 +trainer/policy/mean Std 0.696056 +trainer/policy/mean Max 0.999783 +trainer/policy/mean Min -0.998444 +trainer/policy/std Mean 0.447528 +trainer/policy/std Std 0.0249565 +trainer/policy/std Max 0.479302 +trainer/policy/std Min 0.409361 +trainer/Advantage Weights Mean 1.10467 +trainer/Advantage Weights Std 8.8369 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.00701e-13 +trainer/Advantage Score Mean -0.518395 +trainer/Advantage Score Std 0.487642 +trainer/Advantage Score Max 0.520012 +trainer/Advantage Score Min -2.99266 +trainer/V1 Predictions Mean -71.3378 +trainer/V1 Predictions Std 17.564 +trainer/V1 Predictions Max -0.478992 +trainer/V1 Predictions Min -86.6962 +trainer/VF Loss 0.0531973 +expl/num steps total 115000 +expl/num paths total 116 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.000438396 +expl/Actions Std 0.802996 +expl/Actions Max 2.22386 +expl/Actions Min -2.6039 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 114701 +eval/num paths total 115 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0270455 +eval/Actions Std 0.725259 +eval/Actions Max 0.999172 +eval/Actions Min -0.996033 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.70223e-06 +time/evaluation sampling (s) 2.55459 +time/exploration sampling (s) 3.09772 +time/logging (s) 0.0073758 +time/saving (s) 0.0111958 +time/training (s) 13.3127 +time/epoch (s) 18.9835 +time/total (s) 2598.07 +Epoch -886 +------------------------------ ---------------- +2022-05-15 18:46:03.049285 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -885 finished +------------------------------ ---------------- +epoch -885 +replay_buffer/size 999047 +trainer/num train calls 116000 +trainer/QF1 Loss 0.742037 +trainer/QF2 Loss 0.778855 +trainer/Policy Loss 69.4425 +trainer/Q1 Predictions Mean -72.1784 +trainer/Q1 Predictions Std 17.7407 +trainer/Q1 Predictions Max -1.01318 +trainer/Q1 Predictions Min -87.4714 +trainer/Q2 Predictions Mean -72.1455 +trainer/Q2 Predictions Std 17.7104 +trainer/Q2 Predictions Max -0.926343 +trainer/Q2 Predictions Min -87.4026 +trainer/Q Targets Mean -72.7338 +trainer/Q Targets Std 17.695 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5757 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0163233 +trainer/policy/mean Std 0.71794 +trainer/policy/mean Max 0.999066 +trainer/policy/mean Min -0.996252 +trainer/policy/std Mean 0.446941 +trainer/policy/std Std 0.0233917 +trainer/policy/std Max 0.476593 +trainer/policy/std Min 0.411638 +trainer/Advantage Weights Mean 14.4199 +trainer/Advantage Weights Std 29.5597 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.23833e-11 +trainer/Advantage Score Mean -0.0670768 +trainer/Advantage Score Std 0.484241 +trainer/Advantage Score Max 1.43496 +trainer/Advantage Score Min -2.45227 +trainer/V1 Predictions Mean -72.5259 +trainer/V1 Predictions Std 17.7449 +trainer/V1 Predictions Max -1.65981 +trainer/V1 Predictions Min -87.2147 +trainer/VF Loss 0.0795139 +expl/num steps total 116000 +expl/num paths total 117 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0464823 +expl/Actions Std 0.76454 +expl/Actions Max 2.44073 +expl/Actions Min -2.54299 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 115701 +eval/num paths total 116 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0414016 +eval/Actions Std 0.762296 +eval/Actions Max 0.997455 +eval/Actions Min -0.999002 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.09898e-06 +time/evaluation sampling (s) 2.66027 +time/exploration sampling (s) 3.09912 +time/logging (s) 0.00915716 +time/saving (s) 0.0147885 +time/training (s) 13.3482 +time/epoch (s) 19.1315 +time/total (s) 2617.2 +Epoch -885 +------------------------------ ---------------- +2022-05-15 18:46:22.005075 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -884 finished +------------------------------ ---------------- +epoch -884 +replay_buffer/size 999047 +trainer/num train calls 117000 +trainer/QF1 Loss 0.711951 +trainer/QF2 Loss 0.770249 +trainer/Policy Loss 0.134513 +trainer/Q1 Predictions Mean -69.8283 +trainer/Q1 Predictions Std 19.556 +trainer/Q1 Predictions Max -0.66803 +trainer/Q1 Predictions Min -88.0854 +trainer/Q2 Predictions Mean -69.7139 +trainer/Q2 Predictions Std 19.6023 +trainer/Q2 Predictions Max -0.412796 +trainer/Q2 Predictions Min -88.4278 +trainer/Q Targets Mean -69.67 +trainer/Q Targets Std 19.3213 +trainer/Q Targets Max 0.584364 +trainer/Q Targets Min -87.2116 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0328695 +trainer/policy/mean Std 0.699907 +trainer/policy/mean Max 0.998985 +trainer/policy/mean Min -0.992628 +trainer/policy/std Mean 0.445526 +trainer/policy/std Std 0.0239732 +trainer/policy/std Max 0.477149 +trainer/policy/std Min 0.407515 +trainer/Advantage Weights Mean 0.0327347 +trainer/Advantage Weights Std 0.206538 +trainer/Advantage Weights Max 2.26481 +trainer/Advantage Weights Min 1.89306e-15 +trainer/Advantage Score Mean -0.977424 +trainer/Advantage Score Std 0.518139 +trainer/Advantage Score Max 0.081749 +trainer/Advantage Score Min -3.39006 +trainer/V1 Predictions Mean -69.3171 +trainer/V1 Predictions Std 19.5734 +trainer/V1 Predictions Max 1.70387 +trainer/V1 Predictions Min -87.3586 +trainer/VF Loss 0.122419 +expl/num steps total 117000 +expl/num paths total 118 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0489213 +expl/Actions Std 0.825174 +expl/Actions Max 2.49257 +expl/Actions Min -2.3409 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 116701 +eval/num paths total 117 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.196572 +eval/Actions Std 0.649747 +eval/Actions Max 0.998252 +eval/Actions Min -0.992949 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.26429e-06 +time/evaluation sampling (s) 2.42431 +time/exploration sampling (s) 2.94894 +time/logging (s) 0.0073222 +time/saving (s) 0.0108842 +time/training (s) 13.5532 +time/epoch (s) 18.9447 +time/total (s) 2636.16 +Epoch -884 +------------------------------ ---------------- +2022-05-15 18:46:41.044950 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -883 finished +------------------------------ ---------------- +epoch -883 +replay_buffer/size 999047 +trainer/num train calls 118000 +trainer/QF1 Loss 0.641067 +trainer/QF2 Loss 0.653199 +trainer/Policy Loss 12.8085 +trainer/Q1 Predictions Mean -72.4311 +trainer/Q1 Predictions Std 17.0587 +trainer/Q1 Predictions Max -1.12301 +trainer/Q1 Predictions Min -86.4212 +trainer/Q2 Predictions Mean -72.4631 +trainer/Q2 Predictions Std 17.1059 +trainer/Q2 Predictions Max -0.919888 +trainer/Q2 Predictions Min -86.5538 +trainer/Q Targets Mean -72.0922 +trainer/Q Targets Std 17.1632 +trainer/Q Targets Max -1.4198 +trainer/Q Targets Min -86.3127 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0028856 +trainer/policy/mean Std 0.685408 +trainer/policy/mean Max 0.999548 +trainer/policy/mean Min -0.991618 +trainer/policy/std Mean 0.444911 +trainer/policy/std Std 0.0246515 +trainer/policy/std Max 0.475954 +trainer/policy/std Min 0.403247 +trainer/Advantage Weights Mean 2.78307 +trainer/Advantage Weights Std 14.325 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.76165e-15 +trainer/Advantage Score Mean -0.452685 +trainer/Advantage Score Std 0.475704 +trainer/Advantage Score Max 0.900253 +trainer/Advantage Score Min -3.27876 +trainer/V1 Predictions Mean -71.8471 +trainer/V1 Predictions Std 17.3254 +trainer/V1 Predictions Max -0.850237 +trainer/V1 Predictions Min -86.2517 +trainer/VF Loss 0.0522453 +expl/num steps total 118000 +expl/num paths total 119 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0728876 +expl/Actions Std 0.80507 +expl/Actions Max 2.15126 +expl/Actions Min -2.3895 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 117701 +eval/num paths total 118 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0989051 +eval/Actions Std 0.682377 +eval/Actions Max 0.99703 +eval/Actions Min -0.997466 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.78465e-06 +time/evaluation sampling (s) 2.50553 +time/exploration sampling (s) 2.97763 +time/logging (s) 0.0105273 +time/saving (s) 0.0151205 +time/training (s) 13.5272 +time/epoch (s) 19.036 +time/total (s) 2655.2 +Epoch -883 +------------------------------ ---------------- +2022-05-15 18:47:00.105161 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -882 finished +------------------------------ ---------------- +epoch -882 +replay_buffer/size 999047 +trainer/num train calls 119000 +trainer/QF1 Loss 0.704318 +trainer/QF2 Loss 0.705008 +trainer/Policy Loss 9.00841 +trainer/Q1 Predictions Mean -70.7463 +trainer/Q1 Predictions Std 18.6469 +trainer/Q1 Predictions Max -1.52056 +trainer/Q1 Predictions Min -88.1165 +trainer/Q2 Predictions Mean -70.626 +trainer/Q2 Predictions Std 18.6108 +trainer/Q2 Predictions Max -1.73628 +trainer/Q2 Predictions Min -88.0079 +trainer/Q Targets Mean -70.4174 +trainer/Q Targets Std 18.6088 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.654 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0249914 +trainer/policy/mean Std 0.697522 +trainer/policy/mean Max 0.998595 +trainer/policy/mean Min -0.997193 +trainer/policy/std Mean 0.444966 +trainer/policy/std Std 0.0247627 +trainer/policy/std Max 0.47718 +trainer/policy/std Min 0.405588 +trainer/Advantage Weights Mean 1.74407 +trainer/Advantage Weights Std 12.412 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.0861e-14 +trainer/Advantage Score Mean -0.552456 +trainer/Advantage Score Std 0.429909 +trainer/Advantage Score Max 0.812811 +trainer/Advantage Score Min -3.08286 +trainer/V1 Predictions Mean -70.1337 +trainer/V1 Predictions Std 18.6399 +trainer/V1 Predictions Max -0.905988 +trainer/V1 Predictions Min -87.8564 +trainer/VF Loss 0.0546374 +expl/num steps total 119000 +expl/num paths total 121 +expl/path length Mean 500 +expl/path length Std 462 +expl/path length Max 962 +expl/path length Min 38 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00768544 +expl/Actions Std 0.824965 +expl/Actions Max 2.3352 +expl/Actions Min -2.356 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 118701 +eval/num paths total 119 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00746766 +eval/Actions Std 0.723434 +eval/Actions Max 0.998825 +eval/Actions Min -0.998626 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27158e-05 +time/evaluation sampling (s) 2.36434 +time/exploration sampling (s) 2.95062 +time/logging (s) 0.00719962 +time/saving (s) 0.0102345 +time/training (s) 13.7179 +time/epoch (s) 19.0503 +time/total (s) 2674.25 +Epoch -882 +------------------------------ ---------------- +2022-05-15 18:47:18.912242 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -881 finished +------------------------------ ---------------- +epoch -881 +replay_buffer/size 999047 +trainer/num train calls 120000 +trainer/QF1 Loss 0.853936 +trainer/QF2 Loss 0.851458 +trainer/Policy Loss 32.7131 +trainer/Q1 Predictions Mean -73.7365 +trainer/Q1 Predictions Std 15.9946 +trainer/Q1 Predictions Max -3.01973 +trainer/Q1 Predictions Min -87.3143 +trainer/Q2 Predictions Mean -73.6982 +trainer/Q2 Predictions Std 15.9884 +trainer/Q2 Predictions Max -3.1924 +trainer/Q2 Predictions Min -86.9071 +trainer/Q Targets Mean -73.5386 +trainer/Q Targets Std 16.4743 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7993 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000750023 +trainer/policy/mean Std 0.700181 +trainer/policy/mean Max 0.99842 +trainer/policy/mean Min -0.995686 +trainer/policy/std Mean 0.444596 +trainer/policy/std Std 0.023065 +trainer/policy/std Max 0.472014 +trainer/policy/std Min 0.407599 +trainer/Advantage Weights Mean 6.13288 +trainer/Advantage Weights Std 20.5892 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.52455e-12 +trainer/Advantage Score Mean -0.221871 +trainer/Advantage Score Std 0.443748 +trainer/Advantage Score Max 1.2376 +trainer/Advantage Score Min -2.54881 +trainer/V1 Predictions Mean -73.3141 +trainer/V1 Predictions Std 16.4854 +trainer/V1 Predictions Max -1.75347 +trainer/V1 Predictions Min -86.7558 +trainer/VF Loss 0.0469103 +expl/num steps total 120000 +expl/num paths total 122 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.105883 +expl/Actions Std 0.898127 +expl/Actions Max 2.24086 +expl/Actions Min -2.62513 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 119482 +eval/num paths total 120 +eval/path length Mean 781 +eval/path length Std 0 +eval/path length Max 781 +eval/path length Min 781 +eval/Rewards Mean 0.00128041 +eval/Rewards Std 0.0357599 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0326207 +eval/Actions Std 0.705964 +eval/Actions Max 0.997968 +eval/Actions Min -0.997902 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.71201e-06 +time/evaluation sampling (s) 2.43299 +time/exploration sampling (s) 2.9405 +time/logging (s) 0.0104629 +time/saving (s) 0.0146797 +time/training (s) 13.4054 +time/epoch (s) 18.804 +time/total (s) 2693.06 +Epoch -881 +------------------------------ ---------------- +2022-05-15 18:47:38.099109 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -880 finished +------------------------------ ---------------- +epoch -880 +replay_buffer/size 999047 +trainer/num train calls 121000 +trainer/QF1 Loss 2.52626 +trainer/QF2 Loss 2.38228 +trainer/Policy Loss 19.4904 +trainer/Q1 Predictions Mean -71.1772 +trainer/Q1 Predictions Std 19.4784 +trainer/Q1 Predictions Max -4.0948 +trainer/Q1 Predictions Min -87.2888 +trainer/Q2 Predictions Mean -71.1201 +trainer/Q2 Predictions Std 19.4654 +trainer/Q2 Predictions Max -3.86493 +trainer/Q2 Predictions Min -87.3218 +trainer/Q Targets Mean -70.8564 +trainer/Q Targets Std 19.1581 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9576 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0262481 +trainer/policy/mean Std 0.698061 +trainer/policy/mean Max 0.999538 +trainer/policy/mean Min -0.998151 +trainer/policy/std Mean 0.442984 +trainer/policy/std Std 0.0230137 +trainer/policy/std Max 0.469681 +trainer/policy/std Min 0.404643 +trainer/Advantage Weights Mean 3.84599 +trainer/Advantage Weights Std 16.6196 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.55187e-11 +trainer/Advantage Score Mean -0.389924 +trainer/Advantage Score Std 0.428667 +trainer/Advantage Score Max 0.862728 +trainer/Advantage Score Min -2.36143 +trainer/V1 Predictions Mean -70.6587 +trainer/V1 Predictions Std 19.2623 +trainer/V1 Predictions Max -3.61134 +trainer/V1 Predictions Min -86.8248 +trainer/VF Loss 0.046084 +expl/num steps total 121000 +expl/num paths total 124 +expl/path length Mean 500 +expl/path length Std 471 +expl/path length Max 971 +expl/path length Min 29 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0303275 +expl/Actions Std 0.824195 +expl/Actions Max 2.24286 +expl/Actions Min -2.35348 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 120482 +eval/num paths total 121 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0311755 +eval/Actions Std 0.741026 +eval/Actions Max 0.999197 +eval/Actions Min -0.998833 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.39541e-06 +time/evaluation sampling (s) 2.45295 +time/exploration sampling (s) 2.89133 +time/logging (s) 0.0113839 +time/saving (s) 0.0164883 +time/training (s) 13.8078 +time/epoch (s) 19.18 +time/total (s) 2712.25 +Epoch -880 +------------------------------ ---------------- +2022-05-15 18:47:57.355443 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -879 finished +------------------------------ ---------------- +epoch -879 +replay_buffer/size 999047 +trainer/num train calls 122000 +trainer/QF1 Loss 1.02569 +trainer/QF2 Loss 0.954265 +trainer/Policy Loss 23.8313 +trainer/Q1 Predictions Mean -72.1332 +trainer/Q1 Predictions Std 17.9344 +trainer/Q1 Predictions Max -1.69101 +trainer/Q1 Predictions Min -87.5734 +trainer/Q2 Predictions Mean -72.1371 +trainer/Q2 Predictions Std 18.0178 +trainer/Q2 Predictions Max -1.60781 +trainer/Q2 Predictions Min -87.361 +trainer/Q Targets Mean -72.1028 +trainer/Q Targets Std 18.1618 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5781 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0203626 +trainer/policy/mean Std 0.696381 +trainer/policy/mean Max 0.998117 +trainer/policy/mean Min -0.996224 +trainer/policy/std Mean 0.443861 +trainer/policy/std Std 0.0239165 +trainer/policy/std Max 0.474965 +trainer/policy/std Min 0.406065 +trainer/Advantage Weights Mean 5.74081 +trainer/Advantage Weights Std 18.9184 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.3687e-07 +trainer/Advantage Score Mean -0.201433 +trainer/Advantage Score Std 0.374226 +trainer/Advantage Score Max 0.928635 +trainer/Advantage Score Min -1.46436 +trainer/V1 Predictions Mean -71.9611 +trainer/V1 Predictions Std 18.005 +trainer/V1 Predictions Max -1.42711 +trainer/V1 Predictions Min -87.5023 +trainer/VF Loss 0.0369156 +expl/num steps total 122000 +expl/num paths total 125 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.178684 +expl/Actions Std 0.894572 +expl/Actions Max 2.59171 +expl/Actions Min -2.51625 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 121482 +eval/num paths total 122 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0413422 +eval/Actions Std 0.654109 +eval/Actions Max 0.999294 +eval/Actions Min -0.998641 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.92016e-06 +time/evaluation sampling (s) 2.60169 +time/exploration sampling (s) 3.01583 +time/logging (s) 0.00801376 +time/saving (s) 0.0137972 +time/training (s) 13.6043 +time/epoch (s) 19.2436 +time/total (s) 2731.5 +Epoch -879 +------------------------------ ---------------- +2022-05-15 18:48:16.445581 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -878 finished +------------------------------ ---------------- +epoch -878 +replay_buffer/size 999047 +trainer/num train calls 123000 +trainer/QF1 Loss 0.738096 +trainer/QF2 Loss 0.733429 +trainer/Policy Loss 14.3526 +trainer/Q1 Predictions Mean -71.6594 +trainer/Q1 Predictions Std 19.0615 +trainer/Q1 Predictions Max -1.75922 +trainer/Q1 Predictions Min -86.8289 +trainer/Q2 Predictions Mean -71.7227 +trainer/Q2 Predictions Std 18.9746 +trainer/Q2 Predictions Max -2.58369 +trainer/Q2 Predictions Min -86.7271 +trainer/Q Targets Mean -71.4791 +trainer/Q Targets Std 19.233 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.028 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00570697 +trainer/policy/mean Std 0.69142 +trainer/policy/mean Max 0.998393 +trainer/policy/mean Min -0.997572 +trainer/policy/std Mean 0.445108 +trainer/policy/std Std 0.0230351 +trainer/policy/std Max 0.473603 +trainer/policy/std Min 0.406813 +trainer/Advantage Weights Mean 3.16711 +trainer/Advantage Weights Std 14.6784 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.33657e-09 +trainer/Advantage Score Mean -0.317017 +trainer/Advantage Score Std 0.422655 +trainer/Advantage Score Max 1.41798 +trainer/Advantage Score Min -2.04332 +trainer/V1 Predictions Mean -71.2073 +trainer/V1 Predictions Std 19.4219 +trainer/V1 Predictions Max -0.868727 +trainer/V1 Predictions Min -86.9721 +trainer/VF Loss 0.0422106 +expl/num steps total 123000 +expl/num paths total 126 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0713138 +expl/Actions Std 0.83445 +expl/Actions Max 2.38601 +expl/Actions Min -2.6216 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 122482 +eval/num paths total 123 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0747648 +eval/Actions Std 0.709003 +eval/Actions Max 0.998995 +eval/Actions Min -0.998123 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.72622e-06 +time/evaluation sampling (s) 2.50905 +time/exploration sampling (s) 2.96849 +time/logging (s) 0.00810765 +time/saving (s) 0.0114394 +time/training (s) 13.5843 +time/epoch (s) 19.0814 +time/total (s) 2750.58 +Epoch -878 +------------------------------ ---------------- +2022-05-15 18:48:35.431312 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -877 finished +------------------------------ ---------------- +epoch -877 +replay_buffer/size 999047 +trainer/num train calls 124000 +trainer/QF1 Loss 0.844891 +trainer/QF2 Loss 0.821652 +trainer/Policy Loss 8.21392 +trainer/Q1 Predictions Mean -71.3264 +trainer/Q1 Predictions Std 16.8852 +trainer/Q1 Predictions Max -4.4559 +trainer/Q1 Predictions Min -85.9047 +trainer/Q2 Predictions Mean -71.3638 +trainer/Q2 Predictions Std 16.9116 +trainer/Q2 Predictions Max -4.36009 +trainer/Q2 Predictions Min -85.7583 +trainer/Q Targets Mean -71.5416 +trainer/Q Targets Std 16.9183 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0863 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00612816 +trainer/policy/mean Std 0.699242 +trainer/policy/mean Max 0.998105 +trainer/policy/mean Min -0.999365 +trainer/policy/std Mean 0.44544 +trainer/policy/std Std 0.0222966 +trainer/policy/std Max 0.4725 +trainer/policy/std Min 0.410985 +trainer/Advantage Weights Mean 1.11143 +trainer/Advantage Weights Std 6.99441 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.45287e-14 +trainer/Advantage Score Mean -0.48426 +trainer/Advantage Score Std 0.488901 +trainer/Advantage Score Max 0.558468 +trainer/Advantage Score Min -3.18627 +trainer/V1 Predictions Mean -71.1887 +trainer/V1 Predictions Std 17.1379 +trainer/V1 Predictions Max -2.54493 +trainer/V1 Predictions Min -85.9461 +trainer/VF Loss 0.050526 +expl/num steps total 124000 +expl/num paths total 127 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.070796 +expl/Actions Std 0.811634 +expl/Actions Max 2.58658 +expl/Actions Min -2.38929 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 123482 +eval/num paths total 124 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.270205 +eval/Actions Std 0.698324 +eval/Actions Max 0.996162 +eval/Actions Min -0.993285 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93599e-06 +time/evaluation sampling (s) 2.51788 +time/exploration sampling (s) 2.82111 +time/logging (s) 0.0101086 +time/saving (s) 0.0135583 +time/training (s) 13.6193 +time/epoch (s) 18.982 +time/total (s) 2769.57 +Epoch -877 +------------------------------ ---------------- +2022-05-15 18:48:53.999178 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -876 finished +------------------------------ ---------------- +epoch -876 +replay_buffer/size 999047 +trainer/num train calls 125000 +trainer/QF1 Loss 0.848837 +trainer/QF2 Loss 0.75142 +trainer/Policy Loss 36.3809 +trainer/Q1 Predictions Mean -70.6577 +trainer/Q1 Predictions Std 18.0319 +trainer/Q1 Predictions Max -1.16481 +trainer/Q1 Predictions Min -86.9549 +trainer/Q2 Predictions Mean -70.72 +trainer/Q2 Predictions Std 18.009 +trainer/Q2 Predictions Max -1.31736 +trainer/Q2 Predictions Min -87.0533 +trainer/Q Targets Mean -70.9249 +trainer/Q Targets Std 18.1757 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.163 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0225786 +trainer/policy/mean Std 0.697728 +trainer/policy/mean Max 0.998002 +trainer/policy/mean Min -0.997528 +trainer/policy/std Mean 0.443225 +trainer/policy/std Std 0.0230313 +trainer/policy/std Max 0.473502 +trainer/policy/std Min 0.407066 +trainer/Advantage Weights Mean 5.61667 +trainer/Advantage Weights Std 20.4178 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.7301e-12 +trainer/Advantage Score Mean -0.244045 +trainer/Advantage Score Std 0.431423 +trainer/Advantage Score Max 1.17721 +trainer/Advantage Score Min -2.53558 +trainer/V1 Predictions Mean -70.667 +trainer/V1 Predictions Std 18.3069 +trainer/V1 Predictions Max 0.041381 +trainer/V1 Predictions Min -87.0298 +trainer/VF Loss 0.0471102 +expl/num steps total 125000 +expl/num paths total 128 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0461487 +expl/Actions Std 0.812323 +expl/Actions Max 2.37958 +expl/Actions Min -2.35928 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 124482 +eval/num paths total 125 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.135704 +eval/Actions Std 0.625245 +eval/Actions Max 0.999148 +eval/Actions Min -0.998952 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93413e-06 +time/evaluation sampling (s) 2.58876 +time/exploration sampling (s) 2.90957 +time/logging (s) 0.00708146 +time/saving (s) 0.0103488 +time/training (s) 13.0395 +time/epoch (s) 18.5552 +time/total (s) 2788.13 +Epoch -876 +------------------------------ ---------------- +2022-05-15 18:49:13.458371 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -875 finished +------------------------------ ---------------- +epoch -875 +replay_buffer/size 999047 +trainer/num train calls 126000 +trainer/QF1 Loss 0.575784 +trainer/QF2 Loss 0.632023 +trainer/Policy Loss 4.13137 +trainer/Q1 Predictions Mean -72.6625 +trainer/Q1 Predictions Std 16.6252 +trainer/Q1 Predictions Max -3.95731 +trainer/Q1 Predictions Min -86.9794 +trainer/Q2 Predictions Mean -72.7031 +trainer/Q2 Predictions Std 16.615 +trainer/Q2 Predictions Max -3.77058 +trainer/Q2 Predictions Min -87.0491 +trainer/Q Targets Mean -72.8217 +trainer/Q Targets Std 16.378 +trainer/Q Targets Max -3.16775 +trainer/Q Targets Min -87.4986 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0064452 +trainer/policy/mean Std 0.695765 +trainer/policy/mean Max 0.99836 +trainer/policy/mean Min -0.995627 +trainer/policy/std Mean 0.443313 +trainer/policy/std Std 0.0224103 +trainer/policy/std Max 0.470666 +trainer/policy/std Min 0.406616 +trainer/Advantage Weights Mean 0.76714 +trainer/Advantage Weights Std 6.47638 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.23242e-15 +trainer/Advantage Score Mean -0.606245 +trainer/Advantage Score Std 0.515534 +trainer/Advantage Score Max 0.700249 +trainer/Advantage Score Min -3.43298 +trainer/V1 Predictions Mean -72.5178 +trainer/V1 Predictions Std 16.6265 +trainer/V1 Predictions Max -3.30869 +trainer/V1 Predictions Min -87.255 +trainer/VF Loss 0.0659635 +expl/num steps total 126000 +expl/num paths total 129 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0379031 +expl/Actions Std 0.832302 +expl/Actions Max 2.35161 +expl/Actions Min -2.6073 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 125482 +eval/num paths total 126 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0613289 +eval/Actions Std 0.715083 +eval/Actions Max 0.996206 +eval/Actions Min -0.99796 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.56579e-06 +time/evaluation sampling (s) 2.45143 +time/exploration sampling (s) 3.01887 +time/logging (s) 0.00988599 +time/saving (s) 0.0147632 +time/training (s) 13.9609 +time/epoch (s) 19.4558 +time/total (s) 2807.59 +Epoch -875 +------------------------------ ---------------- +2022-05-15 18:49:32.944062 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -874 finished +------------------------------ ---------------- +epoch -874 +replay_buffer/size 999047 +trainer/num train calls 127000 +trainer/QF1 Loss 1.15933 +trainer/QF2 Loss 0.974494 +trainer/Policy Loss 36.4361 +trainer/Q1 Predictions Mean -72.7913 +trainer/Q1 Predictions Std 15.9363 +trainer/Q1 Predictions Max -1.01841 +trainer/Q1 Predictions Min -86.3981 +trainer/Q2 Predictions Mean -72.9368 +trainer/Q2 Predictions Std 15.9573 +trainer/Q2 Predictions Max -1.47348 +trainer/Q2 Predictions Min -86.7297 +trainer/Q Targets Mean -73.6263 +trainer/Q Targets Std 15.7674 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0633 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0149617 +trainer/policy/mean Std 0.697754 +trainer/policy/mean Max 0.997361 +trainer/policy/mean Min -0.998188 +trainer/policy/std Mean 0.443774 +trainer/policy/std Std 0.0233399 +trainer/policy/std Max 0.469372 +trainer/policy/std Min 0.405405 +trainer/Advantage Weights Mean 7.26813 +trainer/Advantage Weights Std 22.9414 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.62291e-07 +trainer/Advantage Score Mean -0.14868 +trainer/Advantage Score Std 0.382179 +trainer/Advantage Score Max 1.63905 +trainer/Advantage Score Min -1.48308 +trainer/V1 Predictions Mean -73.336 +trainer/V1 Predictions Std 15.941 +trainer/V1 Predictions Max -2.19565 +trainer/V1 Predictions Min -86.8054 +trainer/VF Loss 0.049952 +expl/num steps total 127000 +expl/num paths total 130 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00613137 +expl/Actions Std 0.807562 +expl/Actions Max 2.77285 +expl/Actions Min -2.45094 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 126482 +eval/num paths total 127 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0680524 +eval/Actions Std 0.685456 +eval/Actions Max 0.996282 +eval/Actions Min -0.998349 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.91597e-06 +time/evaluation sampling (s) 2.66879 +time/exploration sampling (s) 2.96173 +time/logging (s) 0.0116692 +time/saving (s) 0.0164283 +time/training (s) 13.8206 +time/epoch (s) 19.4792 +time/total (s) 2827.08 +Epoch -874 +------------------------------ ---------------- +2022-05-15 18:49:52.255121 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -873 finished +------------------------------ ---------------- +epoch -873 +replay_buffer/size 999047 +trainer/num train calls 128000 +trainer/QF1 Loss 0.632544 +trainer/QF2 Loss 0.79893 +trainer/Policy Loss 21.9567 +trainer/Q1 Predictions Mean -72.5551 +trainer/Q1 Predictions Std 16.993 +trainer/Q1 Predictions Max -1.41604 +trainer/Q1 Predictions Min -87.8312 +trainer/Q2 Predictions Mean -72.4362 +trainer/Q2 Predictions Std 17.011 +trainer/Q2 Predictions Max -1.35861 +trainer/Q2 Predictions Min -87.9201 +trainer/Q Targets Mean -72.9924 +trainer/Q Targets Std 17.0283 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4369 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.045188 +trainer/policy/mean Std 0.697933 +trainer/policy/mean Max 0.999048 +trainer/policy/mean Min -0.99584 +trainer/policy/std Mean 0.445596 +trainer/policy/std Std 0.0218413 +trainer/policy/std Max 0.472537 +trainer/policy/std Min 0.410417 +trainer/Advantage Weights Mean 6.11941 +trainer/Advantage Weights Std 20.5656 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.77236e-08 +trainer/Advantage Score Mean -0.208391 +trainer/Advantage Score Std 0.439166 +trainer/Advantage Score Max 1.9846 +trainer/Advantage Score Min -1.7093 +trainer/V1 Predictions Mean -72.7442 +trainer/V1 Predictions Std 17.0018 +trainer/V1 Predictions Max -1.36544 +trainer/V1 Predictions Min -87.3018 +trainer/VF Loss 0.0568403 +expl/num steps total 128000 +expl/num paths total 131 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0509607 +expl/Actions Std 0.793922 +expl/Actions Max 2.33681 +expl/Actions Min -2.31439 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 127482 +eval/num paths total 128 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.20794 +eval/Actions Std 0.697727 +eval/Actions Max 0.999312 +eval/Actions Min -0.997873 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.06711e-05 +time/evaluation sampling (s) 2.56498 +time/exploration sampling (s) 2.75708 +time/logging (s) 0.00741034 +time/saving (s) 0.010597 +time/training (s) 13.9588 +time/epoch (s) 19.2989 +time/total (s) 2846.38 +Epoch -873 +------------------------------ ---------------- +2022-05-15 18:50:11.760535 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -872 finished +------------------------------ ---------------- +epoch -872 +replay_buffer/size 999047 +trainer/num train calls 129000 +trainer/QF1 Loss 0.643185 +trainer/QF2 Loss 0.662259 +trainer/Policy Loss 12.6875 +trainer/Q1 Predictions Mean -71.7906 +trainer/Q1 Predictions Std 18.4241 +trainer/Q1 Predictions Max -3.72932 +trainer/Q1 Predictions Min -88.7989 +trainer/Q2 Predictions Mean -71.6312 +trainer/Q2 Predictions Std 18.4471 +trainer/Q2 Predictions Max -3.67196 +trainer/Q2 Predictions Min -87.9681 +trainer/Q Targets Mean -71.6399 +trainer/Q Targets Std 18.6838 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7639 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0111402 +trainer/policy/mean Std 0.700577 +trainer/policy/mean Max 0.99695 +trainer/policy/mean Min -0.995415 +trainer/policy/std Mean 0.446126 +trainer/policy/std Std 0.0227001 +trainer/policy/std Max 0.47588 +trainer/policy/std Min 0.410087 +trainer/Advantage Weights Mean 2.88215 +trainer/Advantage Weights Std 13.9988 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.65726e-12 +trainer/Advantage Score Mean -0.385507 +trainer/Advantage Score Std 0.486379 +trainer/Advantage Score Max 0.775238 +trainer/Advantage Score Min -2.71259 +trainer/V1 Predictions Mean -71.3477 +trainer/V1 Predictions Std 18.7387 +trainer/V1 Predictions Max -3.96832 +trainer/V1 Predictions Min -87.7229 +trainer/VF Loss 0.0464545 +expl/num steps total 129000 +expl/num paths total 132 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0561531 +expl/Actions Std 0.848255 +expl/Actions Max 2.41255 +expl/Actions Min -2.66742 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 128482 +eval/num paths total 129 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.104578 +eval/Actions Std 0.754469 +eval/Actions Max 0.998326 +eval/Actions Min -0.999483 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.68733e-06 +time/evaluation sampling (s) 2.79255 +time/exploration sampling (s) 2.84316 +time/logging (s) 0.00990087 +time/saving (s) 0.0153176 +time/training (s) 13.8406 +time/epoch (s) 19.5015 +time/total (s) 2865.89 +Epoch -872 +------------------------------ ---------------- +2022-05-15 18:50:30.936580 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -871 finished +------------------------------ ---------------- +epoch -871 +replay_buffer/size 999047 +trainer/num train calls 130000 +trainer/QF1 Loss 1.58547 +trainer/QF2 Loss 1.74435 +trainer/Policy Loss 7.16979 +trainer/Q1 Predictions Mean -72.6689 +trainer/Q1 Predictions Std 18.0994 +trainer/Q1 Predictions Max -1.38979 +trainer/Q1 Predictions Min -87.9196 +trainer/Q2 Predictions Mean -72.7034 +trainer/Q2 Predictions Std 18.108 +trainer/Q2 Predictions Max -1.49368 +trainer/Q2 Predictions Min -88.326 +trainer/Q Targets Mean -72.2502 +trainer/Q Targets Std 18.2654 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7754 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 4.68006e-05 +trainer/policy/mean Std 0.694955 +trainer/policy/mean Max 0.996097 +trainer/policy/mean Min -0.996987 +trainer/policy/std Mean 0.443929 +trainer/policy/std Std 0.0227525 +trainer/policy/std Max 0.474014 +trainer/policy/std Min 0.409062 +trainer/Advantage Weights Mean 1.76679 +trainer/Advantage Weights Std 10.5205 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.23317e-19 +trainer/Advantage Score Mean -0.436412 +trainer/Advantage Score Std 0.470802 +trainer/Advantage Score Max 0.55706 +trainer/Advantage Score Min -4.17704 +trainer/V1 Predictions Mean -72.0299 +trainer/V1 Predictions Std 18.2192 +trainer/V1 Predictions Max -1.03395 +trainer/V1 Predictions Min -87.8227 +trainer/VF Loss 0.0453348 +expl/num steps total 130000 +expl/num paths total 133 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0359831 +expl/Actions Std 0.812205 +expl/Actions Max 2.52912 +expl/Actions Min -2.63396 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 129482 +eval/num paths total 130 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0401972 +eval/Actions Std 0.679226 +eval/Actions Max 0.998137 +eval/Actions Min -0.997598 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18233e-06 +time/evaluation sampling (s) 2.63473 +time/exploration sampling (s) 2.78817 +time/logging (s) 0.0069112 +time/saving (s) 0.00971526 +time/training (s) 13.7265 +time/epoch (s) 19.166 +time/total (s) 2885.06 +Epoch -871 +------------------------------ ---------------- +2022-05-15 18:50:49.973680 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -870 finished +------------------------------ ---------------- +epoch -870 +replay_buffer/size 999047 +trainer/num train calls 131000 +trainer/QF1 Loss 0.633156 +trainer/QF2 Loss 0.666855 +trainer/Policy Loss 27.6625 +trainer/Q1 Predictions Mean -72.2219 +trainer/Q1 Predictions Std 18.1041 +trainer/Q1 Predictions Max -1.11776 +trainer/Q1 Predictions Min -87.5524 +trainer/Q2 Predictions Mean -72.2217 +trainer/Q2 Predictions Std 18.0983 +trainer/Q2 Predictions Max -1.48578 +trainer/Q2 Predictions Min -87.5915 +trainer/Q Targets Mean -72.2654 +trainer/Q Targets Std 17.9105 +trainer/Q Targets Max -2.02857 +trainer/Q Targets Min -87.6369 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0148421 +trainer/policy/mean Std 0.698041 +trainer/policy/mean Max 0.999837 +trainer/policy/mean Min -0.998345 +trainer/policy/std Mean 0.443998 +trainer/policy/std Std 0.0222132 +trainer/policy/std Max 0.473406 +trainer/policy/std Min 0.408888 +trainer/Advantage Weights Mean 4.63166 +trainer/Advantage Weights Std 17.4998 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.06839e-15 +trainer/Advantage Score Mean -0.399086 +trainer/Advantage Score Std 0.508843 +trainer/Advantage Score Max 1.16658 +trainer/Advantage Score Min -3.3812 +trainer/V1 Predictions Mean -72.0004 +trainer/V1 Predictions Std 18.061 +trainer/V1 Predictions Max -2.69989 +trainer/V1 Predictions Min -87.4756 +trainer/VF Loss 0.0591544 +expl/num steps total 131000 +expl/num paths total 134 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0229065 +expl/Actions Std 0.828733 +expl/Actions Max 2.39601 +expl/Actions Min -2.3671 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 130482 +eval/num paths total 131 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0932676 +eval/Actions Std 0.725528 +eval/Actions Max 0.998596 +eval/Actions Min -0.99919 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94996e-06 +time/evaluation sampling (s) 2.5154 +time/exploration sampling (s) 2.82197 +time/logging (s) 0.0118069 +time/saving (s) 0.0163882 +time/training (s) 13.6707 +time/epoch (s) 19.0363 +time/total (s) 2904.1 +Epoch -870 +------------------------------ ---------------- +2022-05-15 18:51:08.515204 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -869 finished +------------------------------ ---------------- +epoch -869 +replay_buffer/size 999047 +trainer/num train calls 132000 +trainer/QF1 Loss 0.436027 +trainer/QF2 Loss 0.406435 +trainer/Policy Loss 14.0185 +trainer/Q1 Predictions Mean -72.8775 +trainer/Q1 Predictions Std 16.3901 +trainer/Q1 Predictions Max -2.65941 +trainer/Q1 Predictions Min -86.2875 +trainer/Q2 Predictions Mean -72.888 +trainer/Q2 Predictions Std 16.3893 +trainer/Q2 Predictions Max -2.79383 +trainer/Q2 Predictions Min -86.5352 +trainer/Q Targets Mean -72.9394 +trainer/Q Targets Std 16.5273 +trainer/Q Targets Max -1.94958 +trainer/Q Targets Min -86.9422 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00226196 +trainer/policy/mean Std 0.694204 +trainer/policy/mean Max 0.996019 +trainer/policy/mean Min -0.994579 +trainer/policy/std Mean 0.443717 +trainer/policy/std Std 0.0235266 +trainer/policy/std Max 0.472607 +trainer/policy/std Min 0.405451 +trainer/Advantage Weights Mean 3.20106 +trainer/Advantage Weights Std 13.4158 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.83098e-13 +trainer/Advantage Score Mean -0.387395 +trainer/Advantage Score Std 0.489237 +trainer/Advantage Score Max 0.512268 +trainer/Advantage Score Min -2.78755 +trainer/V1 Predictions Mean -72.7271 +trainer/V1 Predictions Std 16.5999 +trainer/V1 Predictions Max -2.6531 +trainer/V1 Predictions Min -86.7824 +trainer/VF Loss 0.0465863 +expl/num steps total 132000 +expl/num paths total 135 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.206943 +expl/Actions Std 0.820022 +expl/Actions Max 2.50159 +expl/Actions Min -2.57068 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 131482 +eval/num paths total 132 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0445098 +eval/Actions Std 0.701407 +eval/Actions Max 0.997378 +eval/Actions Min -0.997472 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.01388e-05 +time/evaluation sampling (s) 2.69722 +time/exploration sampling (s) 2.77639 +time/logging (s) 0.00857789 +time/saving (s) 0.0133883 +time/training (s) 13.0344 +time/epoch (s) 18.53 +time/total (s) 2922.63 +Epoch -869 +------------------------------ ---------------- +2022-05-15 18:51:27.286374 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -868 finished +------------------------------ ---------------- +epoch -868 +replay_buffer/size 999047 +trainer/num train calls 133000 +trainer/QF1 Loss 1.20841 +trainer/QF2 Loss 1.10373 +trainer/Policy Loss 10.5083 +trainer/Q1 Predictions Mean -72.8963 +trainer/Q1 Predictions Std 16.4228 +trainer/Q1 Predictions Max -3.90658 +trainer/Q1 Predictions Min -87.1146 +trainer/Q2 Predictions Mean -72.9144 +trainer/Q2 Predictions Std 16.4904 +trainer/Q2 Predictions Max -4.39918 +trainer/Q2 Predictions Min -87.005 +trainer/Q Targets Mean -72.7672 +trainer/Q Targets Std 16.8604 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3532 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00934003 +trainer/policy/mean Std 0.699927 +trainer/policy/mean Max 0.995055 +trainer/policy/mean Min -0.996841 +trainer/policy/std Mean 0.442947 +trainer/policy/std Std 0.0232674 +trainer/policy/std Max 0.468976 +trainer/policy/std Min 0.404851 +trainer/Advantage Weights Mean 2.08078 +trainer/Advantage Weights Std 11.6424 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.23685e-11 +trainer/Advantage Score Mean -0.428674 +trainer/Advantage Score Std 0.505928 +trainer/Advantage Score Max 0.595291 +trainer/Advantage Score Min -2.51159 +trainer/V1 Predictions Mean -72.6175 +trainer/V1 Predictions Std 16.7144 +trainer/V1 Predictions Max -4.56874 +trainer/V1 Predictions Min -87.2041 +trainer/VF Loss 0.0492846 +expl/num steps total 133000 +expl/num paths total 136 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0608536 +expl/Actions Std 0.834124 +expl/Actions Max 2.53048 +expl/Actions Min -2.72391 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 132482 +eval/num paths total 133 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.110048 +eval/Actions Std 0.700771 +eval/Actions Max 0.99836 +eval/Actions Min -0.99703 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.05897e-06 +time/evaluation sampling (s) 2.5317 +time/exploration sampling (s) 2.69576 +time/logging (s) 0.0115901 +time/saving (s) 0.0157294 +time/training (s) 13.5134 +time/epoch (s) 18.7682 +time/total (s) 2941.41 +Epoch -868 +------------------------------ ---------------- +2022-05-15 18:51:46.379358 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -867 finished +------------------------------ ---------------- +epoch -867 +replay_buffer/size 999047 +trainer/num train calls 134000 +trainer/QF1 Loss 1.02903 +trainer/QF2 Loss 1.09797 +trainer/Policy Loss 28.2689 +trainer/Q1 Predictions Mean -71.4344 +trainer/Q1 Predictions Std 19.419 +trainer/Q1 Predictions Max -0.468648 +trainer/Q1 Predictions Min -87.8779 +trainer/Q2 Predictions Mean -71.3958 +trainer/Q2 Predictions Std 19.4667 +trainer/Q2 Predictions Max -0.509686 +trainer/Q2 Predictions Min -87.1377 +trainer/Q Targets Mean -71.6172 +trainer/Q Targets Std 19.1009 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6469 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0170032 +trainer/policy/mean Std 0.705144 +trainer/policy/mean Max 0.996703 +trainer/policy/mean Min -0.994238 +trainer/policy/std Mean 0.444211 +trainer/policy/std Std 0.024391 +trainer/policy/std Max 0.472831 +trainer/policy/std Min 0.406042 +trainer/Advantage Weights Mean 6.21459 +trainer/Advantage Weights Std 18.0941 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.38738e-12 +trainer/Advantage Score Mean -0.211927 +trainer/Advantage Score Std 0.486408 +trainer/Advantage Score Max 0.833149 +trainer/Advantage Score Min -2.67608 +trainer/V1 Predictions Mean -71.1917 +trainer/V1 Predictions Std 19.6111 +trainer/V1 Predictions Max 0.338415 +trainer/V1 Predictions Min -87.4415 +trainer/VF Loss 0.048201 +expl/num steps total 134000 +expl/num paths total 137 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0404601 +expl/Actions Std 0.786515 +expl/Actions Max 2.2655 +expl/Actions Min -2.32974 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 133482 +eval/num paths total 134 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.347756 +eval/Actions Std 0.585094 +eval/Actions Max 0.995234 +eval/Actions Min -0.991154 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.76673e-06 +time/evaluation sampling (s) 2.84888 +time/exploration sampling (s) 2.83881 +time/logging (s) 0.0115363 +time/saving (s) 0.0155264 +time/training (s) 13.3695 +time/epoch (s) 19.0842 +time/total (s) 2960.5 +Epoch -867 +------------------------------ ---------------- +2022-05-15 18:52:05.234484 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -866 finished +------------------------------ ---------------- +epoch -866 +replay_buffer/size 999047 +trainer/num train calls 135000 +trainer/QF1 Loss 0.825435 +trainer/QF2 Loss 0.726655 +trainer/Policy Loss 13.002 +trainer/Q1 Predictions Mean -72.9555 +trainer/Q1 Predictions Std 17.0237 +trainer/Q1 Predictions Max -4.68439 +trainer/Q1 Predictions Min -87.3021 +trainer/Q2 Predictions Mean -72.8944 +trainer/Q2 Predictions Std 16.9672 +trainer/Q2 Predictions Max -4.722 +trainer/Q2 Predictions Min -87.1569 +trainer/Q Targets Mean -72.527 +trainer/Q Targets Std 17.0107 +trainer/Q Targets Max -2.45724 +trainer/Q Targets Min -87.0146 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00242031 +trainer/policy/mean Std 0.701839 +trainer/policy/mean Max 0.999049 +trainer/policy/mean Min -0.997627 +trainer/policy/std Mean 0.444351 +trainer/policy/std Std 0.023136 +trainer/policy/std Max 0.474707 +trainer/policy/std Min 0.410412 +trainer/Advantage Weights Mean 3.09434 +trainer/Advantage Weights Std 13.5395 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.8509e-11 +trainer/Advantage Score Mean -0.335477 +trainer/Advantage Score Std 0.4227 +trainer/Advantage Score Max 1.25481 +trainer/Advantage Score Min -2.35618 +trainer/V1 Predictions Mean -72.3097 +trainer/V1 Predictions Std 17.0928 +trainer/V1 Predictions Max -4.3014 +trainer/V1 Predictions Min -86.8847 +trainer/VF Loss 0.0408225 +expl/num steps total 135000 +expl/num paths total 138 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.030077 +expl/Actions Std 0.801707 +expl/Actions Max 2.28108 +expl/Actions Min -2.3471 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 134482 +eval/num paths total 135 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0366651 +eval/Actions Std 0.734684 +eval/Actions Max 0.999082 +eval/Actions Min -0.996912 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.85637e-06 +time/evaluation sampling (s) 2.57375 +time/exploration sampling (s) 2.72702 +time/logging (s) 0.00777658 +time/saving (s) 0.010837 +time/training (s) 13.5227 +time/epoch (s) 18.8421 +time/total (s) 2979.35 +Epoch -866 +------------------------------ ---------------- +2022-05-15 18:52:24.340383 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -865 finished +------------------------------ ---------------- +epoch -865 +replay_buffer/size 999047 +trainer/num train calls 136000 +trainer/QF1 Loss 1.09788 +trainer/QF2 Loss 1.06857 +trainer/Policy Loss 5.56099 +trainer/Q1 Predictions Mean -70.7029 +trainer/Q1 Predictions Std 19.2379 +trainer/Q1 Predictions Max -0.461307 +trainer/Q1 Predictions Min -86.3553 +trainer/Q2 Predictions Mean -70.6901 +trainer/Q2 Predictions Std 19.2333 +trainer/Q2 Predictions Max -0.605653 +trainer/Q2 Predictions Min -86.3999 +trainer/Q Targets Mean -70.8828 +trainer/Q Targets Std 19.3315 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.73 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00557507 +trainer/policy/mean Std 0.699718 +trainer/policy/mean Max 0.996744 +trainer/policy/mean Min -0.994942 +trainer/policy/std Mean 0.44372 +trainer/policy/std Std 0.024125 +trainer/policy/std Max 0.47208 +trainer/policy/std Min 0.406202 +trainer/Advantage Weights Mean 1.79561 +trainer/Advantage Weights Std 9.7154 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.68419e-17 +trainer/Advantage Score Mean -0.537776 +trainer/Advantage Score Std 0.631717 +trainer/Advantage Score Max 0.566984 +trainer/Advantage Score Min -3.75998 +trainer/V1 Predictions Mean -70.6088 +trainer/V1 Predictions Std 19.3945 +trainer/V1 Predictions Max 1.17439 +trainer/V1 Predictions Min -86.4715 +trainer/VF Loss 0.073311 +expl/num steps total 136000 +expl/num paths total 139 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0297061 +expl/Actions Std 0.791785 +expl/Actions Max 2.38413 +expl/Actions Min -2.40746 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 135482 +eval/num paths total 136 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.124597 +eval/Actions Std 0.775216 +eval/Actions Max 0.998615 +eval/Actions Min -0.99747 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.46712e-06 +time/evaluation sampling (s) 2.66548 +time/exploration sampling (s) 2.82315 +time/logging (s) 0.0107813 +time/saving (s) 0.0159574 +time/training (s) 13.5864 +time/epoch (s) 19.1017 +time/total (s) 2998.45 +Epoch -865 +------------------------------ ---------------- +2022-05-15 18:52:42.799767 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -864 finished +------------------------------ ---------------- +epoch -864 +replay_buffer/size 999047 +trainer/num train calls 137000 +trainer/QF1 Loss 0.670633 +trainer/QF2 Loss 0.664274 +trainer/Policy Loss 68.2876 +trainer/Q1 Predictions Mean -72.2939 +trainer/Q1 Predictions Std 16.6393 +trainer/Q1 Predictions Max -2.0305 +trainer/Q1 Predictions Min -87.5018 +trainer/Q2 Predictions Mean -72.29 +trainer/Q2 Predictions Std 16.637 +trainer/Q2 Predictions Max -2.73147 +trainer/Q2 Predictions Min -87.6032 +trainer/Q Targets Mean -72.7016 +trainer/Q Targets Std 16.6869 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.0293 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0157525 +trainer/policy/mean Std 0.699253 +trainer/policy/mean Max 0.99687 +trainer/policy/mean Min -0.995621 +trainer/policy/std Mean 0.444413 +trainer/policy/std Std 0.0235664 +trainer/policy/std Max 0.472282 +trainer/policy/std Min 0.403821 +trainer/Advantage Weights Mean 15.3459 +trainer/Advantage Weights Std 31.7303 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.60425e-10 +trainer/Advantage Score Mean -0.0275323 +trainer/Advantage Score Std 0.452947 +trainer/Advantage Score Max 1.17809 +trainer/Advantage Score Min -2.14989 +trainer/V1 Predictions Mean -72.4729 +trainer/V1 Predictions Std 16.7372 +trainer/V1 Predictions Max -1.25391 +trainer/V1 Predictions Min -87.9934 +trainer/VF Loss 0.0782258 +expl/num steps total 137000 +expl/num paths total 140 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0405213 +expl/Actions Std 0.812855 +expl/Actions Max 2.42009 +expl/Actions Min -2.66955 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 136482 +eval/num paths total 137 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00398403 +eval/Actions Std 0.687899 +eval/Actions Max 0.998408 +eval/Actions Min -0.998328 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.75578e-06 +time/evaluation sampling (s) 2.46587 +time/exploration sampling (s) 2.63537 +time/logging (s) 0.00704484 +time/saving (s) 0.0149552 +time/training (s) 13.3237 +time/epoch (s) 18.4469 +time/total (s) 3016.91 +Epoch -864 +------------------------------ ---------------- +2022-05-15 18:53:02.326375 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -863 finished +------------------------------ ---------------- +epoch -863 +replay_buffer/size 999047 +trainer/num train calls 138000 +trainer/QF1 Loss 0.682015 +trainer/QF2 Loss 0.65181 +trainer/Policy Loss 16.4281 +trainer/Q1 Predictions Mean -73.9383 +trainer/Q1 Predictions Std 16.5109 +trainer/Q1 Predictions Max -0.755925 +trainer/Q1 Predictions Min -86.9349 +trainer/Q2 Predictions Mean -73.9152 +trainer/Q2 Predictions Std 16.5152 +trainer/Q2 Predictions Max -0.548931 +trainer/Q2 Predictions Min -86.9237 +trainer/Q Targets Mean -73.6073 +trainer/Q Targets Std 16.6781 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8284 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0256995 +trainer/policy/mean Std 0.690979 +trainer/policy/mean Max 0.996263 +trainer/policy/mean Min -0.996185 +trainer/policy/std Mean 0.441514 +trainer/policy/std Std 0.0234735 +trainer/policy/std Max 0.468317 +trainer/policy/std Min 0.404095 +trainer/Advantage Weights Mean 3.07662 +trainer/Advantage Weights Std 12.863 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.13965e-11 +trainer/Advantage Score Mean -0.284425 +trainer/Advantage Score Std 0.427197 +trainer/Advantage Score Max 0.715691 +trainer/Advantage Score Min -2.45678 +trainer/V1 Predictions Mean -73.3993 +trainer/V1 Predictions Std 16.6754 +trainer/V1 Predictions Max -0.840961 +trainer/V1 Predictions Min -86.6962 +trainer/VF Loss 0.0348078 +expl/num steps total 138000 +expl/num paths total 141 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00597799 +expl/Actions Std 0.891745 +expl/Actions Max 2.52978 +expl/Actions Min -2.61953 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 137482 +eval/num paths total 138 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00562035 +eval/Actions Std 0.708815 +eval/Actions Max 0.998136 +eval/Actions Min -0.998408 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.00398e-06 +time/evaluation sampling (s) 2.62248 +time/exploration sampling (s) 3.0388 +time/logging (s) 0.00816685 +time/saving (s) 0.0122097 +time/training (s) 13.84 +time/epoch (s) 19.5216 +time/total (s) 3036.43 +Epoch -863 +------------------------------ ---------------- +2022-05-15 18:53:21.390003 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -862 finished +------------------------------ ---------------- +epoch -862 +replay_buffer/size 999047 +trainer/num train calls 139000 +trainer/QF1 Loss 1.15257 +trainer/QF2 Loss 1.19518 +trainer/Policy Loss 35.4713 +trainer/Q1 Predictions Mean -71.1079 +trainer/Q1 Predictions Std 19.4245 +trainer/Q1 Predictions Max -0.98348 +trainer/Q1 Predictions Min -87.7141 +trainer/Q2 Predictions Mean -71.1687 +trainer/Q2 Predictions Std 19.3822 +trainer/Q2 Predictions Max -0.735171 +trainer/Q2 Predictions Min -87.814 +trainer/Q Targets Mean -71.1525 +trainer/Q Targets Std 19.0882 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4142 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0172903 +trainer/policy/mean Std 0.708342 +trainer/policy/mean Max 0.999405 +trainer/policy/mean Min -0.998343 +trainer/policy/std Mean 0.441326 +trainer/policy/std Std 0.0246208 +trainer/policy/std Max 0.471509 +trainer/policy/std Min 0.403709 +trainer/Advantage Weights Mean 8.23632 +trainer/Advantage Weights Std 26.0563 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.76614e-12 +trainer/Advantage Score Mean -0.332476 +trainer/Advantage Score Std 0.478151 +trainer/Advantage Score Max 1.51697 +trainer/Advantage Score Min -2.70622 +trainer/V1 Predictions Mean -70.9328 +trainer/V1 Predictions Std 19.0147 +trainer/V1 Predictions Max -0.166984 +trainer/V1 Predictions Min -87.4869 +trainer/VF Loss 0.0719599 +expl/num steps total 139000 +expl/num paths total 142 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.220008 +expl/Actions Std 0.880825 +expl/Actions Max 2.51304 +expl/Actions Min -2.74699 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 138482 +eval/num paths total 139 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.250817 +eval/Actions Std 0.655839 +eval/Actions Max 0.997271 +eval/Actions Min -0.999771 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.67802e-06 +time/evaluation sampling (s) 2.79651 +time/exploration sampling (s) 2.96599 +time/logging (s) 0.00812688 +time/saving (s) 0.0111343 +time/training (s) 13.2753 +time/epoch (s) 19.0571 +time/total (s) 3055.49 +Epoch -862 +------------------------------ ---------------- +2022-05-15 18:53:40.426852 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -861 finished +------------------------------ ---------------- +epoch -861 +replay_buffer/size 999047 +trainer/num train calls 140000 +trainer/QF1 Loss 0.634012 +trainer/QF2 Loss 0.665423 +trainer/Policy Loss 11.2604 +trainer/Q1 Predictions Mean -73.6459 +trainer/Q1 Predictions Std 15.9296 +trainer/Q1 Predictions Max -6.9594 +trainer/Q1 Predictions Min -87.6476 +trainer/Q2 Predictions Mean -73.6749 +trainer/Q2 Predictions Std 15.8985 +trainer/Q2 Predictions Max -7.54389 +trainer/Q2 Predictions Min -87.0713 +trainer/Q Targets Mean -73.5155 +trainer/Q Targets Std 16.058 +trainer/Q Targets Max -7.38785 +trainer/Q Targets Min -87.1545 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00685578 +trainer/policy/mean Std 0.692179 +trainer/policy/mean Max 0.994567 +trainer/policy/mean Min -0.99636 +trainer/policy/std Mean 0.443789 +trainer/policy/std Std 0.0237604 +trainer/policy/std Max 0.473374 +trainer/policy/std Min 0.407548 +trainer/Advantage Weights Mean 3.22764 +trainer/Advantage Weights Std 13.6106 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.9144e-13 +trainer/Advantage Score Mean -0.30827 +trainer/Advantage Score Std 0.440938 +trainer/Advantage Score Max 0.885304 +trainer/Advantage Score Min -2.85689 +trainer/V1 Predictions Mean -73.2578 +trainer/V1 Predictions Std 16.1893 +trainer/V1 Predictions Max -7.01984 +trainer/V1 Predictions Min -87.0324 +trainer/VF Loss 0.0395845 +expl/num steps total 140000 +expl/num paths total 143 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0637685 +expl/Actions Std 0.81309 +expl/Actions Max 2.35858 +expl/Actions Min -2.16382 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 139482 +eval/num paths total 140 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.094552 +eval/Actions Std 0.652898 +eval/Actions Max 0.99904 +eval/Actions Min -0.997101 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.53581e-06 +time/evaluation sampling (s) 2.61725 +time/exploration sampling (s) 2.8419 +time/logging (s) 0.0108876 +time/saving (s) 0.015164 +time/training (s) 13.5476 +time/epoch (s) 19.0328 +time/total (s) 3074.53 +Epoch -861 +------------------------------ ---------------- +2022-05-15 18:53:59.724818 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -860 finished +------------------------------ ---------------- +epoch -860 +replay_buffer/size 999047 +trainer/num train calls 141000 +trainer/QF1 Loss 0.746598 +trainer/QF2 Loss 0.81483 +trainer/Policy Loss 26.1098 +trainer/Q1 Predictions Mean -70.2523 +trainer/Q1 Predictions Std 20.0845 +trainer/Q1 Predictions Max -0.461514 +trainer/Q1 Predictions Min -87.3795 +trainer/Q2 Predictions Mean -70.2651 +trainer/Q2 Predictions Std 20.1077 +trainer/Q2 Predictions Max -0.422977 +trainer/Q2 Predictions Min -87.212 +trainer/Q Targets Mean -70.8229 +trainer/Q Targets Std 20.0663 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5009 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0118666 +trainer/policy/mean Std 0.709384 +trainer/policy/mean Max 0.997029 +trainer/policy/mean Min -0.998857 +trainer/policy/std Mean 0.443957 +trainer/policy/std Std 0.0243013 +trainer/policy/std Max 0.475828 +trainer/policy/std Min 0.406073 +trainer/Advantage Weights Mean 3.99009 +trainer/Advantage Weights Std 18.153 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.33189e-12 +trainer/Advantage Score Mean -0.491267 +trainer/Advantage Score Std 0.49716 +trainer/Advantage Score Max 0.687614 +trainer/Advantage Score Min -2.73444 +trainer/V1 Predictions Mean -70.5253 +trainer/V1 Predictions Std 20.2883 +trainer/V1 Predictions Max 0.0563136 +trainer/V1 Predictions Min -87.4802 +trainer/VF Loss 0.0594227 +expl/num steps total 141000 +expl/num paths total 144 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0323924 +expl/Actions Std 0.758221 +expl/Actions Max 2.15053 +expl/Actions Min -2.47047 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 140482 +eval/num paths total 141 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.182468 +eval/Actions Std 0.712218 +eval/Actions Max 0.999748 +eval/Actions Min -0.995894 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.29506e-06 +time/evaluation sampling (s) 2.71278 +time/exploration sampling (s) 2.82212 +time/logging (s) 0.0114251 +time/saving (s) 0.0159064 +time/training (s) 13.7268 +time/epoch (s) 19.289 +time/total (s) 3093.83 +Epoch -860 +------------------------------ ---------------- +2022-05-15 18:54:18.393010 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -859 finished +------------------------------ ---------------- +epoch -859 +replay_buffer/size 999047 +trainer/num train calls 142000 +trainer/QF1 Loss 0.989943 +trainer/QF2 Loss 0.957838 +trainer/Policy Loss 4.63965 +trainer/Q1 Predictions Mean -73.0932 +trainer/Q1 Predictions Std 16.7207 +trainer/Q1 Predictions Max -0.812266 +trainer/Q1 Predictions Min -88.3314 +trainer/Q2 Predictions Mean -73.0625 +trainer/Q2 Predictions Std 16.734 +trainer/Q2 Predictions Max -0.79061 +trainer/Q2 Predictions Min -88.3155 +trainer/Q Targets Mean -72.8634 +trainer/Q Targets Std 16.6864 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5829 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000647702 +trainer/policy/mean Std 0.696863 +trainer/policy/mean Max 0.998278 +trainer/policy/mean Min -0.996658 +trainer/policy/std Mean 0.445426 +trainer/policy/std Std 0.0238777 +trainer/policy/std Max 0.47533 +trainer/policy/std Min 0.405915 +trainer/Advantage Weights Mean 1.02624 +trainer/Advantage Weights Std 7.74452 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.85333e-17 +trainer/Advantage Score Mean -0.534224 +trainer/Advantage Score Std 0.510332 +trainer/Advantage Score Max 0.577225 +trainer/Advantage Score Min -3.7795 +trainer/V1 Predictions Mean -72.5861 +trainer/V1 Predictions Std 16.7954 +trainer/V1 Predictions Max -1.15486 +trainer/V1 Predictions Min -87.6521 +trainer/VF Loss 0.0570864 +expl/num steps total 142000 +expl/num paths total 145 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.053588 +expl/Actions Std 0.825273 +expl/Actions Max 2.28329 +expl/Actions Min -2.35815 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 141482 +eval/num paths total 142 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0713224 +eval/Actions Std 0.701285 +eval/Actions Max 0.998271 +eval/Actions Min -0.997638 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.7162e-06 +time/evaluation sampling (s) 2.47117 +time/exploration sampling (s) 2.77994 +time/logging (s) 0.00739215 +time/saving (s) 0.0145535 +time/training (s) 13.384 +time/epoch (s) 18.657 +time/total (s) 3112.49 +Epoch -859 +------------------------------ ---------------- +2022-05-15 18:54:36.541390 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -858 finished +------------------------------ ---------------- +epoch -858 +replay_buffer/size 999047 +trainer/num train calls 143000 +trainer/QF1 Loss 0.821861 +trainer/QF2 Loss 0.808574 +trainer/Policy Loss 16.3553 +trainer/Q1 Predictions Mean -70.6976 +trainer/Q1 Predictions Std 20.8629 +trainer/Q1 Predictions Max -0.318558 +trainer/Q1 Predictions Min -87.2762 +trainer/Q2 Predictions Mean -70.729 +trainer/Q2 Predictions Std 20.9024 +trainer/Q2 Predictions Max -0.47674 +trainer/Q2 Predictions Min -87.1468 +trainer/Q Targets Mean -70.5379 +trainer/Q Targets Std 20.9002 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3389 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0063848 +trainer/policy/mean Std 0.694379 +trainer/policy/mean Max 0.995085 +trainer/policy/mean Min -0.992514 +trainer/policy/std Mean 0.44445 +trainer/policy/std Std 0.0238047 +trainer/policy/std Max 0.473612 +trainer/policy/std Min 0.406611 +trainer/Advantage Weights Mean 2.96454 +trainer/Advantage Weights Std 14.9886 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.28964e-13 +trainer/Advantage Score Mean -0.477228 +trainer/Advantage Score Std 0.470275 +trainer/Advantage Score Max 0.761322 +trainer/Advantage Score Min -2.87428 +trainer/V1 Predictions Mean -70.3001 +trainer/V1 Predictions Std 20.954 +trainer/V1 Predictions Max 0.554169 +trainer/V1 Predictions Min -87.2111 +trainer/VF Loss 0.0536684 +expl/num steps total 143000 +expl/num paths total 146 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0315613 +expl/Actions Std 0.774144 +expl/Actions Max 2.50413 +expl/Actions Min -2.21767 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 142482 +eval/num paths total 143 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.280836 +eval/Actions Std 0.668363 +eval/Actions Max 0.997093 +eval/Actions Min -0.996811 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18699e-06 +time/evaluation sampling (s) 2.42652 +time/exploration sampling (s) 2.72565 +time/logging (s) 0.0112103 +time/saving (s) 0.0157286 +time/training (s) 12.9672 +time/epoch (s) 18.1463 +time/total (s) 3130.64 +Epoch -858 +------------------------------ ---------------- +2022-05-15 18:54:54.420153 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -857 finished +------------------------------ --------------- +epoch -857 +replay_buffer/size 999047 +trainer/num train calls 144000 +trainer/QF1 Loss 0.462522 +trainer/QF2 Loss 0.459581 +trainer/Policy Loss 21.6139 +trainer/Q1 Predictions Mean -72.4017 +trainer/Q1 Predictions Std 15.7179 +trainer/Q1 Predictions Max -0.942188 +trainer/Q1 Predictions Min -87.4305 +trainer/Q2 Predictions Mean -72.3134 +trainer/Q2 Predictions Std 15.7644 +trainer/Q2 Predictions Max -0.570797 +trainer/Q2 Predictions Min -87.2713 +trainer/Q Targets Mean -72.2209 +trainer/Q Targets Std 16.0269 +trainer/Q Targets Max 0.78282 +trainer/Q Targets Min -87.4748 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0072609 +trainer/policy/mean Std 0.69684 +trainer/policy/mean Max 0.994526 +trainer/policy/mean Min -0.99266 +trainer/policy/std Mean 0.444035 +trainer/policy/std Std 0.0240571 +trainer/policy/std Max 0.471383 +trainer/policy/std Min 0.404128 +trainer/Advantage Weights Mean 4.9265 +trainer/Advantage Weights Std 16.9159 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.8212e-14 +trainer/Advantage Score Mean -0.330362 +trainer/Advantage Score Std 0.612617 +trainer/Advantage Score Max 1.54313 +trainer/Advantage Score Min -3.08956 +trainer/V1 Predictions Mean -71.9812 +trainer/V1 Predictions Std 16.1018 +trainer/V1 Predictions Max 2.41675 +trainer/V1 Predictions Min -87.3507 +trainer/VF Loss 0.067634 +expl/num steps total 144000 +expl/num paths total 147 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0489243 +expl/Actions Std 0.823137 +expl/Actions Max 2.44549 +expl/Actions Min -2.81368 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 143226 +eval/num paths total 144 +eval/path length Mean 744 +eval/path length Std 0 +eval/path length Max 744 +eval/path length Min 744 +eval/Rewards Mean 0.00134409 +eval/Rewards Std 0.0366371 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0590277 +eval/Actions Std 0.698708 +eval/Actions Max 0.998693 +eval/Actions Min -0.998452 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.6729e-06 +time/evaluation sampling (s) 2.52584 +time/exploration sampling (s) 2.87124 +time/logging (s) 0.0107145 +time/saving (s) 0.0163603 +time/training (s) 12.4464 +time/epoch (s) 17.8705 +time/total (s) 3148.52 +Epoch -857 +------------------------------ --------------- +2022-05-15 18:55:13.596030 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -856 finished +------------------------------ ---------------- +epoch -856 +replay_buffer/size 999047 +trainer/num train calls 145000 +trainer/QF1 Loss 0.508256 +trainer/QF2 Loss 0.588333 +trainer/Policy Loss 18.9847 +trainer/Q1 Predictions Mean -72.0579 +trainer/Q1 Predictions Std 18.3699 +trainer/Q1 Predictions Max -1.26226 +trainer/Q1 Predictions Min -87.471 +trainer/Q2 Predictions Mean -72.0677 +trainer/Q2 Predictions Std 18.3353 +trainer/Q2 Predictions Max -1.39125 +trainer/Q2 Predictions Min -87.7403 +trainer/Q Targets Mean -72.1097 +trainer/Q Targets Std 18.3376 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5669 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00151485 +trainer/policy/mean Std 0.706054 +trainer/policy/mean Max 0.998876 +trainer/policy/mean Min -0.998432 +trainer/policy/std Mean 0.442811 +trainer/policy/std Std 0.0246106 +trainer/policy/std Max 0.470991 +trainer/policy/std Min 0.402311 +trainer/Advantage Weights Mean 4.97101 +trainer/Advantage Weights Std 16.9872 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.73862e-09 +trainer/Advantage Score Mean -0.22582 +trainer/Advantage Score Std 0.42446 +trainer/Advantage Score Max 0.918076 +trainer/Advantage Score Min -2.01702 +trainer/V1 Predictions Mean -71.8714 +trainer/V1 Predictions Std 18.3666 +trainer/V1 Predictions Max -1.75188 +trainer/V1 Predictions Min -87.5782 +trainer/VF Loss 0.0392445 +expl/num steps total 145000 +expl/num paths total 148 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0833047 +expl/Actions Std 0.828195 +expl/Actions Max 2.40749 +expl/Actions Min -2.38961 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 144226 +eval/num paths total 145 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.045386 +eval/Actions Std 0.726552 +eval/Actions Max 0.997954 +eval/Actions Min -0.998647 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.97511e-06 +time/evaluation sampling (s) 2.55264 +time/exploration sampling (s) 2.94264 +time/logging (s) 0.00748644 +time/saving (s) 0.0108116 +time/training (s) 13.6504 +time/epoch (s) 19.164 +time/total (s) 3167.69 +Epoch -856 +------------------------------ ---------------- +2022-05-15 18:55:32.895510 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -855 finished +------------------------------ ---------------- +epoch -855 +replay_buffer/size 999047 +trainer/num train calls 146000 +trainer/QF1 Loss 1.64617 +trainer/QF2 Loss 1.48559 +trainer/Policy Loss 1.17062 +trainer/Q1 Predictions Mean -71.8118 +trainer/Q1 Predictions Std 16.9856 +trainer/Q1 Predictions Max -0.343296 +trainer/Q1 Predictions Min -86.5223 +trainer/Q2 Predictions Mean -71.7803 +trainer/Q2 Predictions Std 17.0167 +trainer/Q2 Predictions Max -0.782685 +trainer/Q2 Predictions Min -86.5588 +trainer/Q Targets Mean -70.9035 +trainer/Q Targets Std 17.1569 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8173 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00531425 +trainer/policy/mean Std 0.703691 +trainer/policy/mean Max 0.997942 +trainer/policy/mean Min -0.996105 +trainer/policy/std Mean 0.442722 +trainer/policy/std Std 0.0246863 +trainer/policy/std Max 0.476101 +trainer/policy/std Min 0.40252 +trainer/Advantage Weights Mean 0.461558 +trainer/Advantage Weights Std 6.28148 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.07507e-15 +trainer/Advantage Score Mean -0.995964 +trainer/Advantage Score Std 0.543405 +trainer/Advantage Score Max 0.566471 +trainer/Advantage Score Min -3.25822 +trainer/V1 Predictions Mean -70.6389 +trainer/V1 Predictions Std 17.2727 +trainer/V1 Predictions Max 2.84545 +trainer/V1 Predictions Min -85.5165 +trainer/VF Loss 0.129926 +expl/num steps total 146000 +expl/num paths total 149 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0620093 +expl/Actions Std 0.83945 +expl/Actions Max 2.49453 +expl/Actions Min -2.22363 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 145226 +eval/num paths total 146 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.159293 +eval/Actions Std 0.750212 +eval/Actions Max 0.998919 +eval/Actions Min -0.999077 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93599e-06 +time/evaluation sampling (s) 2.62847 +time/exploration sampling (s) 3.0981 +time/logging (s) 0.00736592 +time/saving (s) 0.0106505 +time/training (s) 13.5474 +time/epoch (s) 19.292 +time/total (s) 3186.98 +Epoch -855 +------------------------------ ---------------- +2022-05-15 18:55:52.340018 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -854 finished +------------------------------ ---------------- +epoch -854 +replay_buffer/size 999047 +trainer/num train calls 147000 +trainer/QF1 Loss 0.463045 +trainer/QF2 Loss 0.54816 +trainer/Policy Loss 3.17395 +trainer/Q1 Predictions Mean -72.6365 +trainer/Q1 Predictions Std 18.456 +trainer/Q1 Predictions Max -0.877684 +trainer/Q1 Predictions Min -87.424 +trainer/Q2 Predictions Mean -72.6083 +trainer/Q2 Predictions Std 18.4927 +trainer/Q2 Predictions Max -1.34293 +trainer/Q2 Predictions Min -87.5407 +trainer/Q Targets Mean -72.6132 +trainer/Q Targets Std 18.3265 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5074 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0291472 +trainer/policy/mean Std 0.695209 +trainer/policy/mean Max 0.998317 +trainer/policy/mean Min -0.995217 +trainer/policy/std Mean 0.441142 +trainer/policy/std Std 0.0241974 +trainer/policy/std Max 0.473934 +trainer/policy/std Min 0.403686 +trainer/Advantage Weights Mean 0.874123 +trainer/Advantage Weights Std 6.72301 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.54531e-16 +trainer/Advantage Score Mean -0.49236 +trainer/Advantage Score Std 0.483537 +trainer/Advantage Score Max 0.692461 +trainer/Advantage Score Min -3.64061 +trainer/V1 Predictions Mean -72.3472 +trainer/V1 Predictions Std 18.4297 +trainer/V1 Predictions Max -1.39383 +trainer/V1 Predictions Min -87.5073 +trainer/VF Loss 0.0504395 +expl/num steps total 147000 +expl/num paths total 151 +expl/path length Mean 500 +expl/path length Std 206 +expl/path length Max 706 +expl/path length Min 294 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0385707 +expl/Actions Std 0.805677 +expl/Actions Max 2.5751 +expl/Actions Min -2.41939 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 146226 +eval/num paths total 147 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.309762 +eval/Actions Std 0.636597 +eval/Actions Max 0.998751 +eval/Actions Min -0.999549 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.39095e-06 +time/evaluation sampling (s) 2.64237 +time/exploration sampling (s) 2.83681 +time/logging (s) 0.00830867 +time/saving (s) 0.0133127 +time/training (s) 13.938 +time/epoch (s) 19.4388 +time/total (s) 3206.43 +Epoch -854 +------------------------------ ---------------- +2022-05-15 18:56:11.794019 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -853 finished +------------------------------ ---------------- +epoch -853 +replay_buffer/size 999047 +trainer/num train calls 148000 +trainer/QF1 Loss 0.805146 +trainer/QF2 Loss 0.866999 +trainer/Policy Loss 12.5717 +trainer/Q1 Predictions Mean -70.8013 +trainer/Q1 Predictions Std 19.7557 +trainer/Q1 Predictions Max -0.799287 +trainer/Q1 Predictions Min -86.9878 +trainer/Q2 Predictions Mean -70.8386 +trainer/Q2 Predictions Std 19.8337 +trainer/Q2 Predictions Max -1.05627 +trainer/Q2 Predictions Min -87.5988 +trainer/Q Targets Mean -70.5067 +trainer/Q Targets Std 19.7281 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4289 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0148721 +trainer/policy/mean Std 0.708888 +trainer/policy/mean Max 0.998055 +trainer/policy/mean Min -0.996579 +trainer/policy/std Mean 0.441123 +trainer/policy/std Std 0.0261112 +trainer/policy/std Max 0.473896 +trainer/policy/std Min 0.396911 +trainer/Advantage Weights Mean 2.39641 +trainer/Advantage Weights Std 13.5033 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.87585e-11 +trainer/Advantage Score Mean -0.516332 +trainer/Advantage Score Std 0.508298 +trainer/Advantage Score Max 1.19792 +trainer/Advantage Score Min -2.46994 +trainer/V1 Predictions Mean -70.1857 +trainer/V1 Predictions Std 19.8919 +trainer/V1 Predictions Max -0.368173 +trainer/V1 Predictions Min -86.5181 +trainer/VF Loss 0.0632303 +expl/num steps total 148000 +expl/num paths total 152 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0258934 +expl/Actions Std 0.828999 +expl/Actions Max 2.37688 +expl/Actions Min -2.43991 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 147226 +eval/num paths total 148 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0368016 +eval/Actions Std 0.703492 +eval/Actions Max 0.999378 +eval/Actions Min -0.999174 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.94163e-06 +time/evaluation sampling (s) 2.59454 +time/exploration sampling (s) 2.93929 +time/logging (s) 0.0110634 +time/saving (s) 0.0164832 +time/training (s) 13.8879 +time/epoch (s) 19.4493 +time/total (s) 3225.88 +Epoch -853 +------------------------------ ---------------- +2022-05-15 18:56:30.833438 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -852 finished +------------------------------ ---------------- +epoch -852 +replay_buffer/size 999047 +trainer/num train calls 149000 +trainer/QF1 Loss 1.15468 +trainer/QF2 Loss 1.4127 +trainer/Policy Loss 6.80386 +trainer/Q1 Predictions Mean -71.9479 +trainer/Q1 Predictions Std 18.6399 +trainer/Q1 Predictions Max -1.05395 +trainer/Q1 Predictions Min -87.6485 +trainer/Q2 Predictions Mean -72.0617 +trainer/Q2 Predictions Std 18.575 +trainer/Q2 Predictions Max -1.1101 +trainer/Q2 Predictions Min -87.1025 +trainer/Q Targets Mean -71.879 +trainer/Q Targets Std 18.6829 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7231 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0217179 +trainer/policy/mean Std 0.707709 +trainer/policy/mean Max 0.994261 +trainer/policy/mean Min -0.99864 +trainer/policy/std Mean 0.441623 +trainer/policy/std Std 0.0245343 +trainer/policy/std Max 0.470952 +trainer/policy/std Min 0.4005 +trainer/Advantage Weights Mean 1.28229 +trainer/Advantage Weights Std 10.1442 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.28247e-13 +trainer/Advantage Score Mean -0.747782 +trainer/Advantage Score Std 0.489794 +trainer/Advantage Score Max 1.53269 +trainer/Advantage Score Min -2.77055 +trainer/V1 Predictions Mean -71.6275 +trainer/V1 Predictions Std 18.828 +trainer/V1 Predictions Max -0.156877 +trainer/V1 Predictions Min -87.63 +trainer/VF Loss 0.0896331 +expl/num steps total 149000 +expl/num paths total 153 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0274217 +expl/Actions Std 0.751827 +expl/Actions Max 2.52981 +expl/Actions Min -2.18725 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 148226 +eval/num paths total 149 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.114989 +eval/Actions Std 0.742669 +eval/Actions Max 0.998713 +eval/Actions Min -0.997142 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.45684e-06 +time/evaluation sampling (s) 2.59995 +time/exploration sampling (s) 2.78532 +time/logging (s) 0.00769385 +time/saving (s) 0.0147777 +time/training (s) 13.6177 +time/epoch (s) 19.0254 +time/total (s) 3244.91 +Epoch -852 +------------------------------ ---------------- +2022-05-15 18:56:49.202729 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -851 finished +------------------------------ ---------------- +epoch -851 +replay_buffer/size 999047 +trainer/num train calls 150000 +trainer/QF1 Loss 1.9537 +trainer/QF2 Loss 2.13135 +trainer/Policy Loss 8.12788 +trainer/Q1 Predictions Mean -71.1586 +trainer/Q1 Predictions Std 19.3253 +trainer/Q1 Predictions Max -0.874352 +trainer/Q1 Predictions Min -86.7559 +trainer/Q2 Predictions Mean -71.1365 +trainer/Q2 Predictions Std 19.3256 +trainer/Q2 Predictions Max -1.00034 +trainer/Q2 Predictions Min -86.7636 +trainer/Q Targets Mean -70.6623 +trainer/Q Targets Std 19.7298 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.172 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0226997 +trainer/policy/mean Std 0.703077 +trainer/policy/mean Max 0.99787 +trainer/policy/mean Min -0.995497 +trainer/policy/std Mean 0.440502 +trainer/policy/std Std 0.0236439 +trainer/policy/std Max 0.472113 +trainer/policy/std Min 0.402305 +trainer/Advantage Weights Mean 2.21964 +trainer/Advantage Weights Std 12.7508 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.28021e-15 +trainer/Advantage Score Mean -0.508069 +trainer/Advantage Score Std 0.515167 +trainer/Advantage Score Max 1.36245 +trainer/Advantage Score Min -3.33509 +trainer/V1 Predictions Mean -70.4588 +trainer/V1 Predictions Std 19.4998 +trainer/V1 Predictions Max 0.267013 +trainer/V1 Predictions Min -86.0731 +trainer/VF Loss 0.0646865 +expl/num steps total 150000 +expl/num paths total 154 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0531282 +expl/Actions Std 0.876179 +expl/Actions Max 2.4256 +expl/Actions Min -2.44942 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 149226 +eval/num paths total 150 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.145988 +eval/Actions Std 0.714148 +eval/Actions Max 0.998121 +eval/Actions Min -0.997027 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.79397e-06 +time/evaluation sampling (s) 2.61558 +time/exploration sampling (s) 2.76392 +time/logging (s) 0.00818965 +time/saving (s) 0.0132375 +time/training (s) 12.9612 +time/epoch (s) 18.3622 +time/total (s) 3263.28 +Epoch -851 +------------------------------ ---------------- +2022-05-15 18:57:07.278884 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -850 finished +------------------------------ ---------------- +epoch -850 +replay_buffer/size 999047 +trainer/num train calls 151000 +trainer/QF1 Loss 1.00299 +trainer/QF2 Loss 1.02454 +trainer/Policy Loss 25.7604 +trainer/Q1 Predictions Mean -70.4535 +trainer/Q1 Predictions Std 19.679 +trainer/Q1 Predictions Max -0.767004 +trainer/Q1 Predictions Min -86.9219 +trainer/Q2 Predictions Mean -70.4909 +trainer/Q2 Predictions Std 19.5995 +trainer/Q2 Predictions Max -1.11481 +trainer/Q2 Predictions Min -87.0879 +trainer/Q Targets Mean -70.6337 +trainer/Q Targets Std 19.4063 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0675 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00354565 +trainer/policy/mean Std 0.696013 +trainer/policy/mean Max 0.998344 +trainer/policy/mean Min -0.995159 +trainer/policy/std Mean 0.440128 +trainer/policy/std Std 0.0236067 +trainer/policy/std Max 0.470022 +trainer/policy/std Min 0.400821 +trainer/Advantage Weights Mean 6.50764 +trainer/Advantage Weights Std 21.7296 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.62929e-10 +trainer/Advantage Score Mean -0.263827 +trainer/Advantage Score Std 0.433105 +trainer/Advantage Score Max 1.43076 +trainer/Advantage Score Min -2.20591 +trainer/V1 Predictions Mean -70.4178 +trainer/V1 Predictions Std 19.4002 +trainer/V1 Predictions Max -1.94301 +trainer/V1 Predictions Min -86.6697 +trainer/VF Loss 0.0552863 +expl/num steps total 151000 +expl/num paths total 155 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.068557 +expl/Actions Std 0.814493 +expl/Actions Max 2.30688 +expl/Actions Min -2.60678 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 150226 +eval/num paths total 151 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.172639 +eval/Actions Std 0.609444 +eval/Actions Max 0.99782 +eval/Actions Min -0.998393 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.73181e-06 +time/evaluation sampling (s) 2.59358 +time/exploration sampling (s) 2.6465 +time/logging (s) 0.00850486 +time/saving (s) 0.0206946 +time/training (s) 12.8011 +time/epoch (s) 18.0704 +time/total (s) 3281.35 +Epoch -850 +------------------------------ ---------------- +2022-05-15 18:57:25.869837 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -849 finished +------------------------------ ---------------- +epoch -849 +replay_buffer/size 999047 +trainer/num train calls 152000 +trainer/QF1 Loss 0.686605 +trainer/QF2 Loss 0.723934 +trainer/Policy Loss 29.3543 +trainer/Q1 Predictions Mean -72.9132 +trainer/Q1 Predictions Std 16.7552 +trainer/Q1 Predictions Max -0.535646 +trainer/Q1 Predictions Min -89.1869 +trainer/Q2 Predictions Mean -72.8399 +trainer/Q2 Predictions Std 16.7044 +trainer/Q2 Predictions Max -0.768295 +trainer/Q2 Predictions Min -88.444 +trainer/Q Targets Mean -72.9699 +trainer/Q Targets Std 16.7991 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.7277 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.025159 +trainer/policy/mean Std 0.710796 +trainer/policy/mean Max 0.996687 +trainer/policy/mean Min -0.996594 +trainer/policy/std Mean 0.441416 +trainer/policy/std Std 0.024656 +trainer/policy/std Max 0.471538 +trainer/policy/std Min 0.397467 +trainer/Advantage Weights Mean 5.25424 +trainer/Advantage Weights Std 17.0991 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.41336e-09 +trainer/Advantage Score Mean -0.210684 +trainer/Advantage Score Std 0.403693 +trainer/Advantage Score Max 1.04389 +trainer/Advantage Score Min -1.85934 +trainer/V1 Predictions Mean -72.8128 +trainer/V1 Predictions Std 16.8178 +trainer/V1 Predictions Max 0.0560717 +trainer/V1 Predictions Min -88.5588 +trainer/VF Loss 0.0382827 +expl/num steps total 152000 +expl/num paths total 156 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0181368 +expl/Actions Std 0.761478 +expl/Actions Max 2.53069 +expl/Actions Min -2.45401 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 151226 +eval/num paths total 152 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0960468 +eval/Actions Std 0.672636 +eval/Actions Max 0.998995 +eval/Actions Min -0.999047 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.60794e-06 +time/evaluation sampling (s) 2.49267 +time/exploration sampling (s) 2.95416 +time/logging (s) 0.00839757 +time/saving (s) 0.0111522 +time/training (s) 13.1187 +time/epoch (s) 18.5851 +time/total (s) 3299.94 +Epoch -849 +------------------------------ ---------------- +2022-05-15 18:57:44.624030 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -848 finished +------------------------------ ---------------- +epoch -848 +replay_buffer/size 999047 +trainer/num train calls 153000 +trainer/QF1 Loss 0.491587 +trainer/QF2 Loss 0.548381 +trainer/Policy Loss 32.6996 +trainer/Q1 Predictions Mean -72.2719 +trainer/Q1 Predictions Std 17.3929 +trainer/Q1 Predictions Max -2.29327 +trainer/Q1 Predictions Min -87.412 +trainer/Q2 Predictions Mean -72.288 +trainer/Q2 Predictions Std 17.3337 +trainer/Q2 Predictions Max -2.6288 +trainer/Q2 Predictions Min -87.0223 +trainer/Q Targets Mean -72.4094 +trainer/Q Targets Std 17.3592 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.162 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00695144 +trainer/policy/mean Std 0.700648 +trainer/policy/mean Max 0.996456 +trainer/policy/mean Min -0.996815 +trainer/policy/std Mean 0.440605 +trainer/policy/std Std 0.0252323 +trainer/policy/std Max 0.467251 +trainer/policy/std Min 0.398255 +trainer/Advantage Weights Mean 7.48902 +trainer/Advantage Weights Std 21.0762 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.13799e-10 +trainer/Advantage Score Mean -0.13091 +trainer/Advantage Score Std 0.441386 +trainer/Advantage Score Max 1.75626 +trainer/Advantage Score Min -2.28966 +trainer/V1 Predictions Mean -72.1587 +trainer/V1 Predictions Std 17.4109 +trainer/V1 Predictions Max -3.51524 +trainer/V1 Predictions Min -87.0688 +trainer/VF Loss 0.0573247 +expl/num steps total 153000 +expl/num paths total 157 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0515478 +expl/Actions Std 0.811624 +expl/Actions Max 2.57103 +expl/Actions Min -2.36221 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 152226 +eval/num paths total 153 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0755281 +eval/Actions Std 0.694223 +eval/Actions Max 0.999052 +eval/Actions Min -0.997727 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.90293e-06 +time/evaluation sampling (s) 2.4971 +time/exploration sampling (s) 2.82039 +time/logging (s) 0.00667521 +time/saving (s) 0.0117759 +time/training (s) 13.41 +time/epoch (s) 18.746 +time/total (s) 3318.69 +Epoch -848 +------------------------------ ---------------- +2022-05-15 18:58:03.640109 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -847 finished +------------------------------ ---------------- +epoch -847 +replay_buffer/size 999047 +trainer/num train calls 154000 +trainer/QF1 Loss 1.20387 +trainer/QF2 Loss 1.45799 +trainer/Policy Loss 15.5373 +trainer/Q1 Predictions Mean -71.2015 +trainer/Q1 Predictions Std 18.7248 +trainer/Q1 Predictions Max -3.23277 +trainer/Q1 Predictions Min -88.1304 +trainer/Q2 Predictions Mean -71.2812 +trainer/Q2 Predictions Std 18.6225 +trainer/Q2 Predictions Max -3.37938 +trainer/Q2 Predictions Min -88.6382 +trainer/Q Targets Mean -70.8104 +trainer/Q Targets Std 19.0834 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.8831 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000424825 +trainer/policy/mean Std 0.70138 +trainer/policy/mean Max 0.997302 +trainer/policy/mean Min -0.998984 +trainer/policy/std Mean 0.440044 +trainer/policy/std Std 0.0235294 +trainer/policy/std Max 0.465099 +trainer/policy/std Min 0.401836 +trainer/Advantage Weights Mean 1.92094 +trainer/Advantage Weights Std 11.9779 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.83512e-12 +trainer/Advantage Score Mean -0.528783 +trainer/Advantage Score Std 0.481646 +trainer/Advantage Score Max 0.978573 +trainer/Advantage Score Min -2.65889 +trainer/V1 Predictions Mean -70.6672 +trainer/V1 Predictions Std 18.8804 +trainer/V1 Predictions Max -4.04419 +trainer/V1 Predictions Min -87.6397 +trainer/VF Loss 0.0583328 +expl/num steps total 154000 +expl/num paths total 158 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0649954 +expl/Actions Std 0.820718 +expl/Actions Max 2.42805 +expl/Actions Min -2.19165 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 153226 +eval/num paths total 154 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0174001 +eval/Actions Std 0.657987 +eval/Actions Max 0.998321 +eval/Actions Min -0.997229 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.41982e-06 +time/evaluation sampling (s) 2.79176 +time/exploration sampling (s) 2.81502 +time/logging (s) 0.0068681 +time/saving (s) 0.0121759 +time/training (s) 13.3829 +time/epoch (s) 19.0088 +time/total (s) 3337.71 +Epoch -847 +------------------------------ ---------------- +2022-05-15 18:58:21.559924 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -846 finished +------------------------------ ---------------- +epoch -846 +replay_buffer/size 999047 +trainer/num train calls 155000 +trainer/QF1 Loss 0.600604 +trainer/QF2 Loss 0.664425 +trainer/Policy Loss 20.8397 +trainer/Q1 Predictions Mean -73.218 +trainer/Q1 Predictions Std 17.7915 +trainer/Q1 Predictions Max -0.932266 +trainer/Q1 Predictions Min -87.3444 +trainer/Q2 Predictions Mean -73.0446 +trainer/Q2 Predictions Std 17.8329 +trainer/Q2 Predictions Max -0.808248 +trainer/Q2 Predictions Min -87.2008 +trainer/Q Targets Mean -73.3293 +trainer/Q Targets Std 17.8167 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0847 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0170467 +trainer/policy/mean Std 0.700701 +trainer/policy/mean Max 0.997758 +trainer/policy/mean Min -0.998996 +trainer/policy/std Mean 0.439749 +trainer/policy/std Std 0.0228243 +trainer/policy/std Max 0.465026 +trainer/policy/std Min 0.401514 +trainer/Advantage Weights Mean 3.70592 +trainer/Advantage Weights Std 16.5622 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.87593e-12 +trainer/Advantage Score Mean -0.338073 +trainer/Advantage Score Std 0.50725 +trainer/Advantage Score Max 2.24897 +trainer/Advantage Score Min -2.58602 +trainer/V1 Predictions Mean -73.0818 +trainer/V1 Predictions Std 17.8429 +trainer/V1 Predictions Max 0.522726 +trainer/V1 Predictions Min -86.9522 +trainer/VF Loss 0.0686831 +expl/num steps total 155000 +expl/num paths total 159 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.046667 +expl/Actions Std 0.801885 +expl/Actions Max 2.60593 +expl/Actions Min -2.29757 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 154226 +eval/num paths total 155 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.22326 +eval/Actions Std 0.647185 +eval/Actions Max 0.996946 +eval/Actions Min -0.998155 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.94699e-06 +time/evaluation sampling (s) 2.50152 +time/exploration sampling (s) 2.8978 +time/logging (s) 0.00707136 +time/saving (s) 0.0102744 +time/training (s) 12.4976 +time/epoch (s) 17.9143 +time/total (s) 3355.63 +Epoch -846 +------------------------------ ---------------- +2022-05-15 18:58:39.808440 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -845 finished +------------------------------ ---------------- +epoch -845 +replay_buffer/size 999047 +trainer/num train calls 156000 +trainer/QF1 Loss 21.7723 +trainer/QF2 Loss 21.7949 +trainer/Policy Loss 39.1533 +trainer/Q1 Predictions Mean -74.0385 +trainer/Q1 Predictions Std 15.7016 +trainer/Q1 Predictions Max -2.0475 +trainer/Q1 Predictions Min -87.3895 +trainer/Q2 Predictions Mean -73.9775 +trainer/Q2 Predictions Std 15.6566 +trainer/Q2 Predictions Max -1.58325 +trainer/Q2 Predictions Min -87.5622 +trainer/Q Targets Mean -74.6015 +trainer/Q Targets Std 15.1153 +trainer/Q Targets Max -2.20728 +trainer/Q Targets Min -87.6159 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000607666 +trainer/policy/mean Std 0.705276 +trainer/policy/mean Max 0.998275 +trainer/policy/mean Min -0.997207 +trainer/policy/std Mean 0.440474 +trainer/policy/std Std 0.0232151 +trainer/policy/std Max 0.467842 +trainer/policy/std Min 0.403213 +trainer/Advantage Weights Mean 7.14568 +trainer/Advantage Weights Std 20.4201 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.49212e-10 +trainer/Advantage Score Mean -0.16926 +trainer/Advantage Score Std 0.459831 +trainer/Advantage Score Max 1.87289 +trainer/Advantage Score Min -2.17753 +trainer/V1 Predictions Mean -74.2002 +trainer/V1 Predictions Std 15.6796 +trainer/V1 Predictions Max 0.135902 +trainer/V1 Predictions Min -87.4906 +trainer/VF Loss 0.0585704 +expl/num steps total 156000 +expl/num paths total 160 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0315272 +expl/Actions Std 0.800071 +expl/Actions Max 2.59764 +expl/Actions Min -2.57188 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 155226 +eval/num paths total 156 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.253037 +eval/Actions Std 0.715985 +eval/Actions Max 0.999315 +eval/Actions Min -0.998072 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.67616e-06 +time/evaluation sampling (s) 2.46356 +time/exploration sampling (s) 2.87704 +time/logging (s) 0.0110192 +time/saving (s) 0.0149749 +time/training (s) 12.8801 +time/epoch (s) 18.2467 +time/total (s) 3373.88 +Epoch -845 +------------------------------ ---------------- +2022-05-15 18:58:58.603484 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -844 finished +------------------------------ ---------------- +epoch -844 +replay_buffer/size 999047 +trainer/num train calls 157000 +trainer/QF1 Loss 1.22412 +trainer/QF2 Loss 1.32111 +trainer/Policy Loss 10.8017 +trainer/Q1 Predictions Mean -72.3134 +trainer/Q1 Predictions Std 18.3674 +trainer/Q1 Predictions Max -1.62306 +trainer/Q1 Predictions Min -86.9628 +trainer/Q2 Predictions Mean -72.2746 +trainer/Q2 Predictions Std 18.3294 +trainer/Q2 Predictions Max -2.29902 +trainer/Q2 Predictions Min -87.0941 +trainer/Q Targets Mean -71.8692 +trainer/Q Targets Std 18.4852 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8808 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0321462 +trainer/policy/mean Std 0.700301 +trainer/policy/mean Max 0.99909 +trainer/policy/mean Min -0.996933 +trainer/policy/std Mean 0.443098 +trainer/policy/std Std 0.0242495 +trainer/policy/std Max 0.469055 +trainer/policy/std Min 0.402851 +trainer/Advantage Weights Mean 2.23389 +trainer/Advantage Weights Std 12.6149 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.70051e-13 +trainer/Advantage Score Mean -0.391991 +trainer/Advantage Score Std 0.455364 +trainer/Advantage Score Max 0.704788 +trainer/Advantage Score Min -2.94027 +trainer/V1 Predictions Mean -71.6084 +trainer/V1 Predictions Std 18.5126 +trainer/V1 Predictions Max -1.36205 +trainer/V1 Predictions Min -86.7471 +trainer/VF Loss 0.0429316 +expl/num steps total 157000 +expl/num paths total 161 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0918804 +expl/Actions Std 0.833703 +expl/Actions Max 2.83411 +expl/Actions Min -2.49013 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 156226 +eval/num paths total 157 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.111158 +eval/Actions Std 0.702219 +eval/Actions Max 0.998755 +eval/Actions Min -0.997991 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.77627e-06 +time/evaluation sampling (s) 2.61013 +time/exploration sampling (s) 3.01207 +time/logging (s) 0.00807887 +time/saving (s) 0.0113947 +time/training (s) 13.1431 +time/epoch (s) 18.7848 +time/total (s) 3392.67 +Epoch -844 +------------------------------ ---------------- +2022-05-15 18:59:16.867319 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -843 finished +------------------------------ ---------------- +epoch -843 +replay_buffer/size 999047 +trainer/num train calls 158000 +trainer/QF1 Loss 1.0732 +trainer/QF2 Loss 1.08634 +trainer/Policy Loss 5.93043 +trainer/Q1 Predictions Mean -71.4542 +trainer/Q1 Predictions Std 18.3308 +trainer/Q1 Predictions Max -4.49834 +trainer/Q1 Predictions Min -86.9608 +trainer/Q2 Predictions Mean -71.4829 +trainer/Q2 Predictions Std 18.2996 +trainer/Q2 Predictions Max -3.87893 +trainer/Q2 Predictions Min -86.7701 +trainer/Q Targets Mean -71.2841 +trainer/Q Targets Std 18.5057 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6351 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00100957 +trainer/policy/mean Std 0.699659 +trainer/policy/mean Max 0.995026 +trainer/policy/mean Min -0.995652 +trainer/policy/std Mean 0.444551 +trainer/policy/std Std 0.0238627 +trainer/policy/std Max 0.471914 +trainer/policy/std Min 0.404566 +trainer/Advantage Weights Mean 1.28897 +trainer/Advantage Weights Std 8.96167 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.90782e-16 +trainer/Advantage Score Mean -0.489913 +trainer/Advantage Score Std 0.520804 +trainer/Advantage Score Max 0.706299 +trainer/Advantage Score Min -3.54784 +trainer/V1 Predictions Mean -70.9998 +trainer/V1 Predictions Std 18.6188 +trainer/V1 Predictions Max -2.69284 +trainer/V1 Predictions Min -86.5045 +trainer/VF Loss 0.0551318 +expl/num steps total 158000 +expl/num paths total 162 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0515939 +expl/Actions Std 0.795158 +expl/Actions Max 2.84214 +expl/Actions Min -2.05418 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 157226 +eval/num paths total 158 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0889562 +eval/Actions Std 0.689148 +eval/Actions Max 0.997119 +eval/Actions Min -0.998394 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.98861e-06 +time/evaluation sampling (s) 2.47779 +time/exploration sampling (s) 2.97761 +time/logging (s) 0.00747712 +time/saving (s) 0.0165844 +time/training (s) 12.7779 +time/epoch (s) 18.2574 +time/total (s) 3410.93 +Epoch -843 +------------------------------ ---------------- +2022-05-15 18:59:35.361242 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -842 finished +------------------------------ ---------------- +epoch -842 +replay_buffer/size 999047 +trainer/num train calls 159000 +trainer/QF1 Loss 1.97437 +trainer/QF2 Loss 2.1553 +trainer/Policy Loss 2.28012 +trainer/Q1 Predictions Mean -71.9885 +trainer/Q1 Predictions Std 18.8652 +trainer/Q1 Predictions Max -1.28768 +trainer/Q1 Predictions Min -87.6767 +trainer/Q2 Predictions Mean -71.9796 +trainer/Q2 Predictions Std 18.8481 +trainer/Q2 Predictions Max -0.712508 +trainer/Q2 Predictions Min -87.5977 +trainer/Q Targets Mean -71.3791 +trainer/Q Targets Std 19.6391 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8513 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0159274 +trainer/policy/mean Std 0.689979 +trainer/policy/mean Max 0.99875 +trainer/policy/mean Min -0.995451 +trainer/policy/std Mean 0.442898 +trainer/policy/std Std 0.0248891 +trainer/policy/std Max 0.473967 +trainer/policy/std Min 0.405039 +trainer/Advantage Weights Mean 0.554037 +trainer/Advantage Weights Std 3.53627 +trainer/Advantage Weights Max 44.7973 +trainer/Advantage Weights Min 1.34612e-13 +trainer/Advantage Score Mean -0.587741 +trainer/Advantage Score Std 0.511252 +trainer/Advantage Score Max 0.380215 +trainer/Advantage Score Min -2.96364 +trainer/V1 Predictions Mean -71.2654 +trainer/V1 Predictions Std 19.349 +trainer/V1 Predictions Max 0.384019 +trainer/V1 Predictions Min -86.8833 +trainer/VF Loss 0.0623149 +expl/num steps total 159000 +expl/num paths total 163 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0569867 +expl/Actions Std 0.788926 +expl/Actions Max 2.43475 +expl/Actions Min -2.4246 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 158226 +eval/num paths total 159 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.067419 +eval/Actions Std 0.708859 +eval/Actions Max 0.997971 +eval/Actions Min -0.998628 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.72132e-06 +time/evaluation sampling (s) 2.5163 +time/exploration sampling (s) 2.97938 +time/logging (s) 0.00864183 +time/saving (s) 0.0125614 +time/training (s) 12.9712 +time/epoch (s) 18.4881 +time/total (s) 3429.42 +Epoch -842 +------------------------------ ---------------- +2022-05-15 18:59:53.917462 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -841 finished +------------------------------ ---------------- +epoch -841 +replay_buffer/size 999047 +trainer/num train calls 160000 +trainer/QF1 Loss 0.876065 +trainer/QF2 Loss 0.956804 +trainer/Policy Loss 1.76989 +trainer/Q1 Predictions Mean -71.2572 +trainer/Q1 Predictions Std 20.107 +trainer/Q1 Predictions Max -0.623834 +trainer/Q1 Predictions Min -86.913 +trainer/Q2 Predictions Mean -71.3418 +trainer/Q2 Predictions Std 20.1675 +trainer/Q2 Predictions Max -0.393522 +trainer/Q2 Predictions Min -87.0544 +trainer/Q Targets Mean -70.9795 +trainer/Q Targets Std 20.3697 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6384 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0253022 +trainer/policy/mean Std 0.708506 +trainer/policy/mean Max 0.99599 +trainer/policy/mean Min -0.996554 +trainer/policy/std Mean 0.440596 +trainer/policy/std Std 0.0248653 +trainer/policy/std Max 0.473035 +trainer/policy/std Min 0.399233 +trainer/Advantage Weights Mean 0.654498 +trainer/Advantage Weights Std 6.4809 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.49311e-14 +trainer/Advantage Score Mean -0.577503 +trainer/Advantage Score Std 0.443449 +trainer/Advantage Score Max 0.464246 +trainer/Advantage Score Min -3.18353 +trainer/V1 Predictions Mean -70.7361 +trainer/V1 Predictions Std 20.4409 +trainer/V1 Predictions Max 0.508767 +trainer/V1 Predictions Min -86.5794 +trainer/VF Loss 0.0544252 +expl/num steps total 160000 +expl/num paths total 164 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0658643 +expl/Actions Std 0.831388 +expl/Actions Max 2.44886 +expl/Actions Min -2.4146 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 159226 +eval/num paths total 160 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.046768 +eval/Actions Std 0.721561 +eval/Actions Max 0.999155 +eval/Actions Min -0.998402 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.74787e-06 +time/evaluation sampling (s) 2.53184 +time/exploration sampling (s) 3.06537 +time/logging (s) 0.00675771 +time/saving (s) 0.00997114 +time/training (s) 12.9342 +time/epoch (s) 18.5481 +time/total (s) 3447.97 +Epoch -841 +------------------------------ ---------------- +2022-05-15 19:00:12.323595 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -840 finished +------------------------------ ---------------- +epoch -840 +replay_buffer/size 999047 +trainer/num train calls 161000 +trainer/QF1 Loss 0.804442 +trainer/QF2 Loss 0.853327 +trainer/Policy Loss 10.5193 +trainer/Q1 Predictions Mean -72.019 +trainer/Q1 Predictions Std 20.1133 +trainer/Q1 Predictions Max -1.07915 +trainer/Q1 Predictions Min -88.2271 +trainer/Q2 Predictions Mean -71.9384 +trainer/Q2 Predictions Std 20.1356 +trainer/Q2 Predictions Max -0.862099 +trainer/Q2 Predictions Min -87.8899 +trainer/Q Targets Mean -71.7642 +trainer/Q Targets Std 20.2902 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6445 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0274269 +trainer/policy/mean Std 0.700632 +trainer/policy/mean Max 0.998239 +trainer/policy/mean Min -0.997988 +trainer/policy/std Mean 0.439788 +trainer/policy/std Std 0.0240347 +trainer/policy/std Max 0.472575 +trainer/policy/std Min 0.400431 +trainer/Advantage Weights Mean 1.41239 +trainer/Advantage Weights Std 9.22964 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.49542e-12 +trainer/Advantage Score Mean -0.564659 +trainer/Advantage Score Std 0.532401 +trainer/Advantage Score Max 0.851747 +trainer/Advantage Score Min -2.63796 +trainer/V1 Predictions Mean -71.5927 +trainer/V1 Predictions Std 20.1617 +trainer/V1 Predictions Max -0.768273 +trainer/V1 Predictions Min -87.4836 +trainer/VF Loss 0.065082 +expl/num steps total 161000 +expl/num paths total 166 +expl/path length Mean 500 +expl/path length Std 396 +expl/path length Max 896 +expl/path length Min 104 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.054879 +expl/Actions Std 0.807805 +expl/Actions Max 2.53679 +expl/Actions Min -2.32581 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 160226 +eval/num paths total 161 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.133704 +eval/Actions Std 0.733922 +eval/Actions Max 0.993062 +eval/Actions Min -0.997418 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.62378e-06 +time/evaluation sampling (s) 2.46794 +time/exploration sampling (s) 2.96217 +time/logging (s) 0.00712475 +time/saving (s) 0.0103288 +time/training (s) 12.9524 +time/epoch (s) 18.4 +time/total (s) 3466.38 +Epoch -840 +------------------------------ ---------------- +2022-05-15 19:00:29.940535 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -839 finished +------------------------------ ---------------- +epoch -839 +replay_buffer/size 999047 +trainer/num train calls 162000 +trainer/QF1 Loss 0.623117 +trainer/QF2 Loss 0.661478 +trainer/Policy Loss 26.0099 +trainer/Q1 Predictions Mean -72.6346 +trainer/Q1 Predictions Std 17.5403 +trainer/Q1 Predictions Max -3.09366 +trainer/Q1 Predictions Min -88.098 +trainer/Q2 Predictions Mean -72.6311 +trainer/Q2 Predictions Std 17.555 +trainer/Q2 Predictions Max -3.68508 +trainer/Q2 Predictions Min -87.8445 +trainer/Q Targets Mean -72.6248 +trainer/Q Targets Std 17.7419 +trainer/Q Targets Max -5.80814 +trainer/Q Targets Min -87.6065 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.005029 +trainer/policy/mean Std 0.705918 +trainer/policy/mean Max 0.998398 +trainer/policy/mean Min -0.997995 +trainer/policy/std Mean 0.438365 +trainer/policy/std Std 0.0232206 +trainer/policy/std Max 0.469886 +trainer/policy/std Min 0.40044 +trainer/Advantage Weights Mean 6.01646 +trainer/Advantage Weights Std 19.5084 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.63156e-11 +trainer/Advantage Score Mean -0.231082 +trainer/Advantage Score Std 0.463422 +trainer/Advantage Score Max 0.966843 +trainer/Advantage Score Min -2.40388 +trainer/V1 Predictions Mean -72.3511 +trainer/V1 Predictions Std 17.8072 +trainer/V1 Predictions Max -4.04284 +trainer/V1 Predictions Min -87.461 +trainer/VF Loss 0.0465038 +expl/num steps total 162000 +expl/num paths total 167 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0566498 +expl/Actions Std 0.810465 +expl/Actions Max 2.31938 +expl/Actions Min -2.64829 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 161226 +eval/num paths total 162 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0441021 +eval/Actions Std 0.722396 +eval/Actions Max 0.999014 +eval/Actions Min -0.998213 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.73297e-06 +time/evaluation sampling (s) 2.46046 +time/exploration sampling (s) 2.8942 +time/logging (s) 0.00744512 +time/saving (s) 0.0124825 +time/training (s) 12.2367 +time/epoch (s) 17.6113 +time/total (s) 3483.99 +Epoch -839 +------------------------------ ---------------- +2022-05-15 19:00:48.719189 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -838 finished +------------------------------ ---------------- +epoch -838 +replay_buffer/size 999047 +trainer/num train calls 163000 +trainer/QF1 Loss 1.6345 +trainer/QF2 Loss 1.65298 +trainer/Policy Loss 5.90967 +trainer/Q1 Predictions Mean -72.352 +trainer/Q1 Predictions Std 19.3796 +trainer/Q1 Predictions Max -3.98967 +trainer/Q1 Predictions Min -88.5406 +trainer/Q2 Predictions Mean -72.3307 +trainer/Q2 Predictions Std 19.3619 +trainer/Q2 Predictions Max -3.35607 +trainer/Q2 Predictions Min -88.1428 +trainer/Q Targets Mean -71.5512 +trainer/Q Targets Std 19.7506 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6125 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0212882 +trainer/policy/mean Std 0.684624 +trainer/policy/mean Max 0.997811 +trainer/policy/mean Min -0.999472 +trainer/policy/std Mean 0.439646 +trainer/policy/std Std 0.0233057 +trainer/policy/std Max 0.468774 +trainer/policy/std Min 0.405713 +trainer/Advantage Weights Mean 1.79913 +trainer/Advantage Weights Std 12.5277 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.36557e-11 +trainer/Advantage Score Mean -0.669356 +trainer/Advantage Score Std 0.482444 +trainer/Advantage Score Max 1.3343 +trainer/Advantage Score Min -2.50169 +trainer/V1 Predictions Mean -71.3152 +trainer/V1 Predictions Std 19.7023 +trainer/V1 Predictions Max -0.179646 +trainer/V1 Predictions Min -87.3535 +trainer/VF Loss 0.0803528 +expl/num steps total 163000 +expl/num paths total 168 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0803974 +expl/Actions Std 0.816685 +expl/Actions Max 2.14621 +expl/Actions Min -2.25531 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 162226 +eval/num paths total 163 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.236605 +eval/Actions Std 0.81004 +eval/Actions Max 0.997075 +eval/Actions Min -0.994361 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66498e-06 +time/evaluation sampling (s) 2.63139 +time/exploration sampling (s) 2.99468 +time/logging (s) 0.00876805 +time/saving (s) 0.0127771 +time/training (s) 13.1228 +time/epoch (s) 18.7704 +time/total (s) 3502.77 +Epoch -838 +------------------------------ ---------------- +2022-05-15 19:01:07.118808 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -837 finished +------------------------------ ---------------- +epoch -837 +replay_buffer/size 999047 +trainer/num train calls 164000 +trainer/QF1 Loss 0.387947 +trainer/QF2 Loss 0.381234 +trainer/Policy Loss 22.7449 +trainer/Q1 Predictions Mean -72.6714 +trainer/Q1 Predictions Std 17.4333 +trainer/Q1 Predictions Max -3.9526 +trainer/Q1 Predictions Min -87.104 +trainer/Q2 Predictions Mean -72.6441 +trainer/Q2 Predictions Std 17.4414 +trainer/Q2 Predictions Max -3.84657 +trainer/Q2 Predictions Min -86.8958 +trainer/Q Targets Mean -72.6509 +trainer/Q Targets Std 17.2078 +trainer/Q Targets Max -4.88939 +trainer/Q Targets Min -86.6754 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0275841 +trainer/policy/mean Std 0.703275 +trainer/policy/mean Max 0.998188 +trainer/policy/mean Min -0.998086 +trainer/policy/std Mean 0.439907 +trainer/policy/std Std 0.0238446 +trainer/policy/std Max 0.47058 +trainer/policy/std Min 0.404533 +trainer/Advantage Weights Mean 4.54155 +trainer/Advantage Weights Std 18.9327 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.17852e-08 +trainer/Advantage Score Mean -0.388457 +trainer/Advantage Score Std 0.419669 +trainer/Advantage Score Max 1.60026 +trainer/Advantage Score Min -1.67762 +trainer/V1 Predictions Mean -72.3583 +trainer/V1 Predictions Std 17.3784 +trainer/V1 Predictions Max -4.0212 +trainer/V1 Predictions Min -86.5798 +trainer/VF Loss 0.0522476 +expl/num steps total 164000 +expl/num paths total 169 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0349035 +expl/Actions Std 0.810006 +expl/Actions Max 2.23364 +expl/Actions Min -2.36556 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 163226 +eval/num paths total 164 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.223088 +eval/Actions Std 0.821465 +eval/Actions Max 0.995006 +eval/Actions Min -0.997519 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.81539e-06 +time/evaluation sampling (s) 2.62325 +time/exploration sampling (s) 2.86056 +time/logging (s) 0.00754541 +time/saving (s) 0.0137405 +time/training (s) 12.8854 +time/epoch (s) 18.3905 +time/total (s) 3521.17 +Epoch -837 +------------------------------ ---------------- +2022-05-15 19:01:25.687710 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -836 finished +------------------------------ ---------------- +epoch -836 +replay_buffer/size 999047 +trainer/num train calls 165000 +trainer/QF1 Loss 1.0704 +trainer/QF2 Loss 1.13617 +trainer/Policy Loss 17.0384 +trainer/Q1 Predictions Mean -70.5857 +trainer/Q1 Predictions Std 20.8652 +trainer/Q1 Predictions Max -0.580451 +trainer/Q1 Predictions Min -87.4152 +trainer/Q2 Predictions Mean -70.6372 +trainer/Q2 Predictions Std 20.9021 +trainer/Q2 Predictions Max -0.864672 +trainer/Q2 Predictions Min -87.7426 +trainer/Q Targets Mean -70.1545 +trainer/Q Targets Std 21.1835 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6691 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0138743 +trainer/policy/mean Std 0.717684 +trainer/policy/mean Max 0.99936 +trainer/policy/mean Min -0.998282 +trainer/policy/std Mean 0.438821 +trainer/policy/std Std 0.0229514 +trainer/policy/std Max 0.469545 +trainer/policy/std Min 0.404155 +trainer/Advantage Weights Mean 3.94156 +trainer/Advantage Weights Std 16.7427 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.19612e-13 +trainer/Advantage Score Mean -0.466565 +trainer/Advantage Score Std 0.611945 +trainer/Advantage Score Max 1.74052 +trainer/Advantage Score Min -2.87717 +trainer/V1 Predictions Mean -69.9191 +trainer/V1 Predictions Std 21.2616 +trainer/V1 Predictions Max 1.90044 +trainer/V1 Predictions Min -87.824 +trainer/VF Loss 0.0838052 +expl/num steps total 165000 +expl/num paths total 170 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0151915 +expl/Actions Std 0.829919 +expl/Actions Max 2.45508 +expl/Actions Min -2.18836 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 164226 +eval/num paths total 165 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0416013 +eval/Actions Std 0.709485 +eval/Actions Max 0.999288 +eval/Actions Min -0.996913 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.88058e-06 +time/evaluation sampling (s) 2.55126 +time/exploration sampling (s) 2.8536 +time/logging (s) 0.00734926 +time/saving (s) 0.0110431 +time/training (s) 13.1364 +time/epoch (s) 18.5597 +time/total (s) 3539.73 +Epoch -836 +------------------------------ ---------------- +2022-05-15 19:01:42.866377 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -835 finished +------------------------------ ---------------- +epoch -835 +replay_buffer/size 999047 +trainer/num train calls 166000 +trainer/QF1 Loss 0.405727 +trainer/QF2 Loss 0.466992 +trainer/Policy Loss 24.4956 +trainer/Q1 Predictions Mean -73.217 +trainer/Q1 Predictions Std 17.9365 +trainer/Q1 Predictions Max -0.325152 +trainer/Q1 Predictions Min -88.8503 +trainer/Q2 Predictions Mean -73.2549 +trainer/Q2 Predictions Std 17.9924 +trainer/Q2 Predictions Max -0.349032 +trainer/Q2 Predictions Min -88.0817 +trainer/Q Targets Mean -73.1576 +trainer/Q Targets Std 17.9406 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6095 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0126811 +trainer/policy/mean Std 0.706729 +trainer/policy/mean Max 0.99913 +trainer/policy/mean Min -0.996277 +trainer/policy/std Mean 0.43857 +trainer/policy/std Std 0.0242395 +trainer/policy/std Max 0.467924 +trainer/policy/std Min 0.398344 +trainer/Advantage Weights Mean 4.07062 +trainer/Advantage Weights Std 13.9437 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.94746e-12 +trainer/Advantage Score Mean -0.266042 +trainer/Advantage Score Std 0.454075 +trainer/Advantage Score Max 0.87915 +trainer/Advantage Score Min -2.69645 +trainer/V1 Predictions Mean -72.9315 +trainer/V1 Predictions Std 18.1891 +trainer/V1 Predictions Max 0.360277 +trainer/V1 Predictions Min -88.0603 +trainer/VF Loss 0.0409885 +expl/num steps total 166000 +expl/num paths total 171 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.243542 +expl/Actions Std 0.784529 +expl/Actions Max 2.46236 +expl/Actions Min -2.40751 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 165226 +eval/num paths total 166 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0942014 +eval/Actions Std 0.706086 +eval/Actions Max 0.998475 +eval/Actions Min -0.999076 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.85311e-06 +time/evaluation sampling (s) 2.47783 +time/exploration sampling (s) 2.87074 +time/logging (s) 0.00684505 +time/saving (s) 0.00969786 +time/training (s) 11.8069 +time/epoch (s) 17.172 +time/total (s) 3556.91 +Epoch -835 +------------------------------ ---------------- +2022-05-15 19:02:00.837164 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -834 finished +------------------------------ ---------------- +epoch -834 +replay_buffer/size 999047 +trainer/num train calls 167000 +trainer/QF1 Loss 0.875718 +trainer/QF2 Loss 0.831051 +trainer/Policy Loss 38.4304 +trainer/Q1 Predictions Mean -72.0117 +trainer/Q1 Predictions Std 18.9298 +trainer/Q1 Predictions Max -1.78799 +trainer/Q1 Predictions Min -88.4069 +trainer/Q2 Predictions Mean -71.9227 +trainer/Q2 Predictions Std 18.984 +trainer/Q2 Predictions Max -2.06627 +trainer/Q2 Predictions Min -89.3761 +trainer/Q Targets Mean -71.8653 +trainer/Q Targets Std 19.3107 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.8049 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00969123 +trainer/policy/mean Std 0.702912 +trainer/policy/mean Max 0.998727 +trainer/policy/mean Min -0.997546 +trainer/policy/std Mean 0.438195 +trainer/policy/std Std 0.0244609 +trainer/policy/std Max 0.464551 +trainer/policy/std Min 0.395705 +trainer/Advantage Weights Mean 7.31981 +trainer/Advantage Weights Std 22.2522 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.31793e-11 +trainer/Advantage Score Mean -0.194299 +trainer/Advantage Score Std 0.469121 +trainer/Advantage Score Max 1.02457 +trainer/Advantage Score Min -2.50524 +trainer/V1 Predictions Mean -71.6211 +trainer/V1 Predictions Std 19.3745 +trainer/V1 Predictions Max -1.23643 +trainer/V1 Predictions Min -88.8127 +trainer/VF Loss 0.0475667 +expl/num steps total 167000 +expl/num paths total 172 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0655703 +expl/Actions Std 0.817416 +expl/Actions Max 2.5899 +expl/Actions Min -2.40273 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 166226 +eval/num paths total 167 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0917431 +eval/Actions Std 0.712036 +eval/Actions Max 0.997718 +eval/Actions Min -0.997781 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.82796e-06 +time/evaluation sampling (s) 2.56013 +time/exploration sampling (s) 2.89744 +time/logging (s) 0.0128091 +time/saving (s) 0.0131924 +time/training (s) 12.4872 +time/epoch (s) 17.9708 +time/total (s) 3574.88 +Epoch -834 +------------------------------ ---------------- +2022-05-15 19:02:19.542363 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -833 finished +------------------------------ ---------------- +epoch -833 +replay_buffer/size 999047 +trainer/num train calls 168000 +trainer/QF1 Loss 0.584134 +trainer/QF2 Loss 0.586166 +trainer/Policy Loss 25.5768 +trainer/Q1 Predictions Mean -72.9981 +trainer/Q1 Predictions Std 17.5694 +trainer/Q1 Predictions Max -0.406387 +trainer/Q1 Predictions Min -87.6283 +trainer/Q2 Predictions Mean -73.0132 +trainer/Q2 Predictions Std 17.5385 +trainer/Q2 Predictions Max -0.85556 +trainer/Q2 Predictions Min -87.1328 +trainer/Q Targets Mean -72.888 +trainer/Q Targets Std 17.7355 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.433 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0029482 +trainer/policy/mean Std 0.69513 +trainer/policy/mean Max 0.999797 +trainer/policy/mean Min -0.995863 +trainer/policy/std Mean 0.440352 +trainer/policy/std Std 0.0249341 +trainer/policy/std Max 0.469587 +trainer/policy/std Min 0.397223 +trainer/Advantage Weights Mean 4.36655 +trainer/Advantage Weights Std 17.6131 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.14472e-09 +trainer/Advantage Score Mean -0.278222 +trainer/Advantage Score Std 0.405217 +trainer/Advantage Score Max 1.31069 +trainer/Advantage Score Min -1.95775 +trainer/V1 Predictions Mean -72.6788 +trainer/V1 Predictions Std 17.8243 +trainer/V1 Predictions Max -0.410542 +trainer/V1 Predictions Min -86.962 +trainer/VF Loss 0.04214 +expl/num steps total 168000 +expl/num paths total 173 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.125477 +expl/Actions Std 0.80094 +expl/Actions Max 2.39897 +expl/Actions Min -2.28833 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 167226 +eval/num paths total 168 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00124589 +eval/Actions Std 0.728953 +eval/Actions Max 0.998738 +eval/Actions Min -0.997877 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.85869e-06 +time/evaluation sampling (s) 2.53051 +time/exploration sampling (s) 2.99595 +time/logging (s) 0.00900531 +time/saving (s) 0.0134429 +time/training (s) 13.1421 +time/epoch (s) 18.691 +time/total (s) 3593.58 +Epoch -833 +------------------------------ ---------------- +2022-05-15 19:02:37.531736 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -832 finished +------------------------------ ---------------- +epoch -832 +replay_buffer/size 999047 +trainer/num train calls 169000 +trainer/QF1 Loss 0.771813 +trainer/QF2 Loss 0.753518 +trainer/Policy Loss 15.6049 +trainer/Q1 Predictions Mean -73.1967 +trainer/Q1 Predictions Std 16.2341 +trainer/Q1 Predictions Max -5.51353 +trainer/Q1 Predictions Min -86.9555 +trainer/Q2 Predictions Mean -73.2063 +trainer/Q2 Predictions Std 16.1702 +trainer/Q2 Predictions Max -4.99119 +trainer/Q2 Predictions Min -87.1275 +trainer/Q Targets Mean -73.34 +trainer/Q Targets Std 16.1904 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.441 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -4.80497e-05 +trainer/policy/mean Std 0.683801 +trainer/policy/mean Max 0.995028 +trainer/policy/mean Min -0.998853 +trainer/policy/std Mean 0.438113 +trainer/policy/std Std 0.0244197 +trainer/policy/std Max 0.467786 +trainer/policy/std Min 0.396744 +trainer/Advantage Weights Mean 3.84076 +trainer/Advantage Weights Std 16.8994 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.41873e-15 +trainer/Advantage Score Mean -0.409511 +trainer/Advantage Score Std 0.540134 +trainer/Advantage Score Max 1.56454 +trainer/Advantage Score Min -3.4189 +trainer/V1 Predictions Mean -73.0724 +trainer/V1 Predictions Std 16.3472 +trainer/V1 Predictions Max -3.29934 +trainer/V1 Predictions Min -87.318 +trainer/VF Loss 0.0621221 +expl/num steps total 169000 +expl/num paths total 174 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.025138 +expl/Actions Std 0.802237 +expl/Actions Max 2.27421 +expl/Actions Min -2.27206 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 168226 +eval/num paths total 169 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.031555 +eval/Actions Std 0.687266 +eval/Actions Max 0.997863 +eval/Actions Min -0.998479 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.63192e-06 +time/evaluation sampling (s) 2.47778 +time/exploration sampling (s) 2.88383 +time/logging (s) 0.00675412 +time/saving (s) 0.01206 +time/training (s) 12.5994 +time/epoch (s) 17.9798 +time/total (s) 3611.56 +Epoch -832 +------------------------------ ---------------- +2022-05-15 19:02:56.648365 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -831 finished +------------------------------ ---------------- +epoch -831 +replay_buffer/size 999047 +trainer/num train calls 170000 +trainer/QF1 Loss 0.545413 +trainer/QF2 Loss 0.58581 +trainer/Policy Loss 23.4132 +trainer/Q1 Predictions Mean -72.1376 +trainer/Q1 Predictions Std 18.7963 +trainer/Q1 Predictions Max -0.894674 +trainer/Q1 Predictions Min -87.669 +trainer/Q2 Predictions Mean -72.1181 +trainer/Q2 Predictions Std 18.7962 +trainer/Q2 Predictions Max -0.740919 +trainer/Q2 Predictions Min -87.9834 +trainer/Q Targets Mean -72.4789 +trainer/Q Targets Std 18.6136 +trainer/Q Targets Max -1.05205 +trainer/Q Targets Min -88.0285 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00313721 +trainer/policy/mean Std 0.706966 +trainer/policy/mean Max 0.995113 +trainer/policy/mean Min -0.999256 +trainer/policy/std Mean 0.438127 +trainer/policy/std Std 0.0230048 +trainer/policy/std Max 0.468813 +trainer/policy/std Min 0.401417 +trainer/Advantage Weights Mean 6.65704 +trainer/Advantage Weights Std 21.3041 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.87564e-10 +trainer/Advantage Score Mean -0.208337 +trainer/Advantage Score Std 0.477291 +trainer/Advantage Score Max 1.58758 +trainer/Advantage Score Min -2.1255 +trainer/V1 Predictions Mean -72.2324 +trainer/V1 Predictions Std 18.7542 +trainer/V1 Predictions Max -1.38456 +trainer/V1 Predictions Min -87.8405 +trainer/VF Loss 0.0634117 +expl/num steps total 170000 +expl/num paths total 175 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.06229 +expl/Actions Std 0.781327 +expl/Actions Max 2.29909 +expl/Actions Min -2.27957 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 169226 +eval/num paths total 170 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00579999 +eval/Actions Std 0.729253 +eval/Actions Max 0.998764 +eval/Actions Min -0.999519 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.91504e-06 +time/evaluation sampling (s) 2.58015 +time/exploration sampling (s) 3.01061 +time/logging (s) 0.0124503 +time/saving (s) 0.0195985 +time/training (s) 13.4934 +time/epoch (s) 19.1162 +time/total (s) 3630.68 +Epoch -831 +------------------------------ ---------------- +2022-05-15 19:03:15.190027 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -830 finished +------------------------------ ---------------- +epoch -830 +replay_buffer/size 999047 +trainer/num train calls 171000 +trainer/QF1 Loss 0.672374 +trainer/QF2 Loss 0.606649 +trainer/Policy Loss 8.54853 +trainer/Q1 Predictions Mean -73.8722 +trainer/Q1 Predictions Std 15.68 +trainer/Q1 Predictions Max -0.99952 +trainer/Q1 Predictions Min -87.7539 +trainer/Q2 Predictions Mean -73.7484 +trainer/Q2 Predictions Std 15.6758 +trainer/Q2 Predictions Max -0.96984 +trainer/Q2 Predictions Min -87.4336 +trainer/Q Targets Mean -73.3826 +trainer/Q Targets Std 15.7472 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7157 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00558762 +trainer/policy/mean Std 0.701626 +trainer/policy/mean Max 0.996139 +trainer/policy/mean Min -0.996842 +trainer/policy/std Mean 0.437972 +trainer/policy/std Std 0.0224587 +trainer/policy/std Max 0.466012 +trainer/policy/std Min 0.40143 +trainer/Advantage Weights Mean 1.519 +trainer/Advantage Weights Std 8.7689 +trainer/Advantage Weights Max 99.5062 +trainer/Advantage Weights Min 1.18703e-12 +trainer/Advantage Score Mean -0.516071 +trainer/Advantage Score Std 0.480358 +trainer/Advantage Score Max 0.460022 +trainer/Advantage Score Min -2.74596 +trainer/V1 Predictions Mean -73.0976 +trainer/V1 Predictions Std 15.7927 +trainer/V1 Predictions Max 0.332218 +trainer/V1 Predictions Min -86.695 +trainer/VF Loss 0.0533916 +expl/num steps total 171000 +expl/num paths total 177 +expl/path length Mean 500 +expl/path length Std 242 +expl/path length Max 742 +expl/path length Min 258 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0268979 +expl/Actions Std 0.804349 +expl/Actions Max 2.19856 +expl/Actions Min -2.40099 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 170226 +eval/num paths total 171 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0698567 +eval/Actions Std 0.63045 +eval/Actions Max 0.996831 +eval/Actions Min -0.997658 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.84193e-06 +time/evaluation sampling (s) 2.51044 +time/exploration sampling (s) 2.89781 +time/logging (s) 0.00848821 +time/saving (s) 0.0103119 +time/training (s) 13.099 +time/epoch (s) 18.5261 +time/total (s) 3649.22 +Epoch -830 +------------------------------ ---------------- +2022-05-15 19:03:33.188976 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -829 finished +------------------------------ ---------------- +epoch -829 +replay_buffer/size 999047 +trainer/num train calls 172000 +trainer/QF1 Loss 0.789841 +trainer/QF2 Loss 0.871139 +trainer/Policy Loss 23.722 +trainer/Q1 Predictions Mean -74.0073 +trainer/Q1 Predictions Std 16.6931 +trainer/Q1 Predictions Max -3.65327 +trainer/Q1 Predictions Min -88.4433 +trainer/Q2 Predictions Mean -74.0036 +trainer/Q2 Predictions Std 16.7442 +trainer/Q2 Predictions Max -3.57283 +trainer/Q2 Predictions Min -88.6455 +trainer/Q Targets Mean -74.0377 +trainer/Q Targets Std 16.4336 +trainer/Q Targets Max -2.12921 +trainer/Q Targets Min -88.0399 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0437479 +trainer/policy/mean Std 0.707468 +trainer/policy/mean Max 0.998161 +trainer/policy/mean Min -0.998428 +trainer/policy/std Mean 0.438125 +trainer/policy/std Std 0.022601 +trainer/policy/std Max 0.46506 +trainer/policy/std Min 0.403384 +trainer/Advantage Weights Mean 5.09188 +trainer/Advantage Weights Std 17.7402 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.87384e-10 +trainer/Advantage Score Mean -0.226034 +trainer/Advantage Score Std 0.459068 +trainer/Advantage Score Max 1.39258 +trainer/Advantage Score Min -2.08427 +trainer/V1 Predictions Mean -73.7879 +trainer/V1 Predictions Std 16.6966 +trainer/V1 Predictions Max -2.08203 +trainer/V1 Predictions Min -87.9483 +trainer/VF Loss 0.0510083 +expl/num steps total 172000 +expl/num paths total 178 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0305241 +expl/Actions Std 0.826574 +expl/Actions Max 2.57672 +expl/Actions Min -2.41974 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 171226 +eval/num paths total 172 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.314087 +eval/Actions Std 0.714857 +eval/Actions Max 0.998012 +eval/Actions Min -0.998974 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.01795e-06 +time/evaluation sampling (s) 2.51042 +time/exploration sampling (s) 2.73738 +time/logging (s) 0.0131505 +time/saving (s) 0.0114563 +time/training (s) 12.7253 +time/epoch (s) 17.9977 +time/total (s) 3667.22 +Epoch -829 +------------------------------ ---------------- +2022-05-15 19:03:51.189075 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -828 finished +------------------------------ ---------------- +epoch -828 +replay_buffer/size 999047 +trainer/num train calls 173000 +trainer/QF1 Loss 0.770117 +trainer/QF2 Loss 0.770694 +trainer/Policy Loss 6.37058 +trainer/Q1 Predictions Mean -72.442 +trainer/Q1 Predictions Std 16.9018 +trainer/Q1 Predictions Max -5.42112 +trainer/Q1 Predictions Min -87.7818 +trainer/Q2 Predictions Mean -72.5072 +trainer/Q2 Predictions Std 16.8866 +trainer/Q2 Predictions Max -5.34174 +trainer/Q2 Predictions Min -88.1003 +trainer/Q Targets Mean -72.3646 +trainer/Q Targets Std 17.1899 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6086 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0185396 +trainer/policy/mean Std 0.706104 +trainer/policy/mean Max 0.998731 +trainer/policy/mean Min -0.995061 +trainer/policy/std Mean 0.439166 +trainer/policy/std Std 0.0233324 +trainer/policy/std Max 0.468051 +trainer/policy/std Min 0.403716 +trainer/Advantage Weights Mean 1.04111 +trainer/Advantage Weights Std 6.96353 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.61777e-20 +trainer/Advantage Score Mean -0.664641 +trainer/Advantage Score Std 0.764696 +trainer/Advantage Score Max 0.655468 +trainer/Advantage Score Min -4.45218 +trainer/V1 Predictions Mean -72.0718 +trainer/V1 Predictions Std 17.3005 +trainer/V1 Predictions Max -5.24675 +trainer/V1 Predictions Min -87.408 +trainer/VF Loss 0.10584 +expl/num steps total 173000 +expl/num paths total 179 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.142309 +expl/Actions Std 0.823755 +expl/Actions Max 2.64978 +expl/Actions Min -2.5019 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 172226 +eval/num paths total 173 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.134274 +eval/Actions Std 0.65771 +eval/Actions Max 0.998867 +eval/Actions Min -0.997848 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.01003e-06 +time/evaluation sampling (s) 2.51407 +time/exploration sampling (s) 3.07504 +time/logging (s) 0.00834383 +time/saving (s) 0.0099338 +time/training (s) 12.376 +time/epoch (s) 17.9834 +time/total (s) 3685.21 +Epoch -828 +------------------------------ ---------------- +2022-05-15 19:04:10.307379 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -827 finished +------------------------------ ---------------- +epoch -827 +replay_buffer/size 999047 +trainer/num train calls 174000 +trainer/QF1 Loss 1.06634 +trainer/QF2 Loss 1.20946 +trainer/Policy Loss 6.83849 +trainer/Q1 Predictions Mean -72.7887 +trainer/Q1 Predictions Std 17.7217 +trainer/Q1 Predictions Max -0.784814 +trainer/Q1 Predictions Min -87.218 +trainer/Q2 Predictions Mean -72.7257 +trainer/Q2 Predictions Std 17.7883 +trainer/Q2 Predictions Max -1.11351 +trainer/Q2 Predictions Min -87.334 +trainer/Q Targets Mean -72.8942 +trainer/Q Targets Std 17.5585 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8368 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0125219 +trainer/policy/mean Std 0.705447 +trainer/policy/mean Max 0.997923 +trainer/policy/mean Min -0.997726 +trainer/policy/std Mean 0.438478 +trainer/policy/std Std 0.0227194 +trainer/policy/std Max 0.465289 +trainer/policy/std Min 0.403844 +trainer/Advantage Weights Mean 1.36961 +trainer/Advantage Weights Std 9.26103 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.11257e-14 +trainer/Advantage Score Mean -0.400145 +trainer/Advantage Score Std 0.423421 +trainer/Advantage Score Max 1.03865 +trainer/Advantage Score Min -3.21295 +trainer/V1 Predictions Mean -72.6589 +trainer/V1 Predictions Std 17.6507 +trainer/V1 Predictions Max -0.778646 +trainer/V1 Predictions Min -86.8915 +trainer/VF Loss 0.0399399 +expl/num steps total 174000 +expl/num paths total 180 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0613299 +expl/Actions Std 0.815754 +expl/Actions Max 2.227 +expl/Actions Min -2.32853 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 173226 +eval/num paths total 174 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.102304 +eval/Actions Std 0.71061 +eval/Actions Max 0.997931 +eval/Actions Min -0.998903 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99187e-06 +time/evaluation sampling (s) 2.60421 +time/exploration sampling (s) 2.82805 +time/logging (s) 0.00819734 +time/saving (s) 0.0116284 +time/training (s) 13.6598 +time/epoch (s) 19.1119 +time/total (s) 3704.33 +Epoch -827 +------------------------------ ---------------- +2022-05-15 19:04:28.334982 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -826 finished +------------------------------ ---------------- +epoch -826 +replay_buffer/size 999047 +trainer/num train calls 175000 +trainer/QF1 Loss 0.742603 +trainer/QF2 Loss 0.768211 +trainer/Policy Loss 17.5493 +trainer/Q1 Predictions Mean -72.4171 +trainer/Q1 Predictions Std 18.5732 +trainer/Q1 Predictions Max -2.03458 +trainer/Q1 Predictions Min -87.1244 +trainer/Q2 Predictions Mean -72.4766 +trainer/Q2 Predictions Std 18.6031 +trainer/Q2 Predictions Max -2.34964 +trainer/Q2 Predictions Min -87.2613 +trainer/Q Targets Mean -72.0334 +trainer/Q Targets Std 18.4265 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6778 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0109165 +trainer/policy/mean Std 0.697121 +trainer/policy/mean Max 0.997843 +trainer/policy/mean Min -0.99663 +trainer/policy/std Mean 0.438804 +trainer/policy/std Std 0.0222608 +trainer/policy/std Max 0.464217 +trainer/policy/std Min 0.403461 +trainer/Advantage Weights Mean 2.97192 +trainer/Advantage Weights Std 15.4586 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.55913e-11 +trainer/Advantage Score Mean -0.422372 +trainer/Advantage Score Std 0.447517 +trainer/Advantage Score Max 1.09905 +trainer/Advantage Score Min -2.48843 +trainer/V1 Predictions Mean -71.7768 +trainer/V1 Predictions Std 18.5096 +trainer/V1 Predictions Max -1.77617 +trainer/V1 Predictions Min -86.4138 +trainer/VF Loss 0.0506399 +expl/num steps total 175000 +expl/num paths total 181 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0336166 +expl/Actions Std 0.804169 +expl/Actions Max 2.5207 +expl/Actions Min -2.31244 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 174226 +eval/num paths total 175 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0529265 +eval/Actions Std 0.685496 +eval/Actions Max 0.998855 +eval/Actions Min -0.998039 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.88384e-06 +time/evaluation sampling (s) 2.51269 +time/exploration sampling (s) 2.62989 +time/logging (s) 0.00679303 +time/saving (s) 0.0100776 +time/training (s) 12.8601 +time/epoch (s) 18.0196 +time/total (s) 3722.35 +Epoch -826 +------------------------------ ---------------- +2022-05-15 19:04:46.744634 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -825 finished +------------------------------ ---------------- +epoch -825 +replay_buffer/size 999047 +trainer/num train calls 176000 +trainer/QF1 Loss 4.74715 +trainer/QF2 Loss 4.27012 +trainer/Policy Loss 22.5072 +trainer/Q1 Predictions Mean -72.897 +trainer/Q1 Predictions Std 18.2035 +trainer/Q1 Predictions Max -1.45308 +trainer/Q1 Predictions Min -87.8296 +trainer/Q2 Predictions Mean -72.7898 +trainer/Q2 Predictions Std 18.1472 +trainer/Q2 Predictions Max -1.9148 +trainer/Q2 Predictions Min -88.0702 +trainer/Q Targets Mean -72.7511 +trainer/Q Targets Std 17.8347 +trainer/Q Targets Max -1.52851 +trainer/Q Targets Min -88.129 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0265549 +trainer/policy/mean Std 0.695307 +trainer/policy/mean Max 0.99581 +trainer/policy/mean Min -0.996477 +trainer/policy/std Mean 0.436031 +trainer/policy/std Std 0.0221657 +trainer/policy/std Max 0.465917 +trainer/policy/std Min 0.400645 +trainer/Advantage Weights Mean 4.75426 +trainer/Advantage Weights Std 18.3897 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.30856e-10 +trainer/Advantage Score Mean -0.248726 +trainer/Advantage Score Std 0.414346 +trainer/Advantage Score Max 0.849808 +trainer/Advantage Score Min -2.27569 +trainer/V1 Predictions Mean -72.5893 +trainer/V1 Predictions Std 18.0152 +trainer/V1 Predictions Max -2.0033 +trainer/V1 Predictions Min -88.0752 +trainer/VF Loss 0.0385283 +expl/num steps total 176000 +expl/num paths total 182 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.282143 +expl/Actions Std 0.86627 +expl/Actions Max 2.58936 +expl/Actions Min -2.57464 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 175226 +eval/num paths total 176 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0199899 +eval/Actions Std 0.716336 +eval/Actions Max 0.998881 +eval/Actions Min -0.99879 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.67709e-06 +time/evaluation sampling (s) 2.6169 +time/exploration sampling (s) 2.84147 +time/logging (s) 0.00655016 +time/saving (s) 0.0098369 +time/training (s) 12.9287 +time/epoch (s) 18.4035 +time/total (s) 3740.76 +Epoch -825 +------------------------------ ---------------- +2022-05-15 19:05:04.899251 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -824 finished +------------------------------ ---------------- +epoch -824 +replay_buffer/size 999047 +trainer/num train calls 177000 +trainer/QF1 Loss 0.814838 +trainer/QF2 Loss 0.800887 +trainer/Policy Loss 19.1993 +trainer/Q1 Predictions Mean -73.4197 +trainer/Q1 Predictions Std 17.1198 +trainer/Q1 Predictions Max -0.356625 +trainer/Q1 Predictions Min -88.1817 +trainer/Q2 Predictions Mean -73.4723 +trainer/Q2 Predictions Std 17.0755 +trainer/Q2 Predictions Max -0.304486 +trainer/Q2 Predictions Min -88.4926 +trainer/Q Targets Mean -73.5791 +trainer/Q Targets Std 16.7632 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.3511 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0106908 +trainer/policy/mean Std 0.70598 +trainer/policy/mean Max 0.999082 +trainer/policy/mean Min -0.998547 +trainer/policy/std Mean 0.436526 +trainer/policy/std Std 0.0235136 +trainer/policy/std Max 0.46952 +trainer/policy/std Min 0.398286 +trainer/Advantage Weights Mean 2.94091 +trainer/Advantage Weights Std 14.3218 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.43901e-12 +trainer/Advantage Score Mean -0.383721 +trainer/Advantage Score Std 0.457719 +trainer/Advantage Score Max 1.11304 +trainer/Advantage Score Min -2.59374 +trainer/V1 Predictions Mean -73.3221 +trainer/V1 Predictions Std 16.8377 +trainer/V1 Predictions Max -0.157866 +trainer/V1 Predictions Min -88.1611 +trainer/VF Loss 0.0463592 +expl/num steps total 177000 +expl/num paths total 183 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.012809 +expl/Actions Std 0.818914 +expl/Actions Max 2.37661 +expl/Actions Min -2.29538 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 176226 +eval/num paths total 177 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0486341 +eval/Actions Std 0.736305 +eval/Actions Max 0.997858 +eval/Actions Min -0.997961 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.67243e-06 +time/evaluation sampling (s) 2.45786 +time/exploration sampling (s) 2.65738 +time/logging (s) 0.00731186 +time/saving (s) 0.0119214 +time/training (s) 13.015 +time/epoch (s) 18.1495 +time/total (s) 3758.91 +Epoch -824 +------------------------------ ---------------- +2022-05-15 19:05:23.321272 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -823 finished +------------------------------ ---------------- +epoch -823 +replay_buffer/size 999047 +trainer/num train calls 178000 +trainer/QF1 Loss 0.968902 +trainer/QF2 Loss 0.921708 +trainer/Policy Loss 25.4631 +trainer/Q1 Predictions Mean -72.8849 +trainer/Q1 Predictions Std 17.9358 +trainer/Q1 Predictions Max -0.740128 +trainer/Q1 Predictions Min -87.8397 +trainer/Q2 Predictions Mean -72.9654 +trainer/Q2 Predictions Std 17.9423 +trainer/Q2 Predictions Max -0.931284 +trainer/Q2 Predictions Min -88.2784 +trainer/Q Targets Mean -73.0629 +trainer/Q Targets Std 18.3572 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.0073 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0303007 +trainer/policy/mean Std 0.708095 +trainer/policy/mean Max 0.998181 +trainer/policy/mean Min -0.998485 +trainer/policy/std Mean 0.435574 +trainer/policy/std Std 0.0222644 +trainer/policy/std Max 0.466142 +trainer/policy/std Min 0.401442 +trainer/Advantage Weights Mean 4.31615 +trainer/Advantage Weights Std 16.0582 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.37669e-10 +trainer/Advantage Score Mean -0.235169 +trainer/Advantage Score Std 0.430243 +trainer/Advantage Score Max 0.808655 +trainer/Advantage Score Min -2.27062 +trainer/V1 Predictions Mean -72.886 +trainer/V1 Predictions Std 18.1653 +trainer/V1 Predictions Max -0.636701 +trainer/V1 Predictions Min -87.9104 +trainer/VF Loss 0.0372486 +expl/num steps total 178000 +expl/num paths total 184 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0594137 +expl/Actions Std 0.814826 +expl/Actions Max 2.84815 +expl/Actions Min -2.17821 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 177226 +eval/num paths total 178 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.307095 +eval/Actions Std 0.715196 +eval/Actions Max 0.998313 +eval/Actions Min -0.998137 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.59699e-06 +time/evaluation sampling (s) 2.59183 +time/exploration sampling (s) 2.66705 +time/logging (s) 0.0070027 +time/saving (s) 0.0126597 +time/training (s) 13.1362 +time/epoch (s) 18.4148 +time/total (s) 3777.33 +Epoch -823 +------------------------------ ---------------- +2022-05-15 19:05:41.620707 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -822 finished +------------------------------ ---------------- +epoch -822 +replay_buffer/size 999047 +trainer/num train calls 179000 +trainer/QF1 Loss 1.13356 +trainer/QF2 Loss 1.04965 +trainer/Policy Loss 0.572686 +trainer/Q1 Predictions Mean -71.9356 +trainer/Q1 Predictions Std 19.5813 +trainer/Q1 Predictions Max -0.600542 +trainer/Q1 Predictions Min -86.8734 +trainer/Q2 Predictions Mean -71.9064 +trainer/Q2 Predictions Std 19.6225 +trainer/Q2 Predictions Max -0.6078 +trainer/Q2 Predictions Min -86.9576 +trainer/Q Targets Mean -71.5834 +trainer/Q Targets Std 20.0472 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9093 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00535443 +trainer/policy/mean Std 0.699788 +trainer/policy/mean Max 0.998715 +trainer/policy/mean Min -0.995678 +trainer/policy/std Mean 0.436265 +trainer/policy/std Std 0.0225449 +trainer/policy/std Max 0.463153 +trainer/policy/std Min 0.400384 +trainer/Advantage Weights Mean 0.214209 +trainer/Advantage Weights Std 2.68114 +trainer/Advantage Weights Max 42.8451 +trainer/Advantage Weights Min 5.03874e-18 +trainer/Advantage Score Mean -0.84669 +trainer/Advantage Score Std 0.682798 +trainer/Advantage Score Max 0.375759 +trainer/Advantage Score Min -3.98294 +trainer/V1 Predictions Mean -71.323 +trainer/V1 Predictions Std 20.1343 +trainer/V1 Predictions Max 0.973017 +trainer/V1 Predictions Min -86.75 +trainer/VF Loss 0.11881 +expl/num steps total 179000 +expl/num paths total 185 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0496186 +expl/Actions Std 0.831116 +expl/Actions Max 2.62151 +expl/Actions Min -2.17704 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 178226 +eval/num paths total 179 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.105856 +eval/Actions Std 0.627081 +eval/Actions Max 0.998696 +eval/Actions Min -0.998133 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.4268e-06 +time/evaluation sampling (s) 2.74373 +time/exploration sampling (s) 2.8459 +time/logging (s) 0.0069742 +time/saving (s) 0.0097179 +time/training (s) 12.6855 +time/epoch (s) 18.2918 +time/total (s) 3795.63 +Epoch -822 +------------------------------ ---------------- +2022-05-15 19:05:59.542383 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -821 finished +------------------------------ ---------------- +epoch -821 +replay_buffer/size 999047 +trainer/num train calls 180000 +trainer/QF1 Loss 0.576385 +trainer/QF2 Loss 0.614479 +trainer/Policy Loss 25.0752 +trainer/Q1 Predictions Mean -71.1174 +trainer/Q1 Predictions Std 19.6643 +trainer/Q1 Predictions Max -0.353942 +trainer/Q1 Predictions Min -87.1008 +trainer/Q2 Predictions Mean -71.047 +trainer/Q2 Predictions Std 19.6101 +trainer/Q2 Predictions Max -0.731729 +trainer/Q2 Predictions Min -87.0462 +trainer/Q Targets Mean -71.1995 +trainer/Q Targets Std 19.8026 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1856 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0170327 +trainer/policy/mean Std 0.711311 +trainer/policy/mean Max 0.998368 +trainer/policy/mean Min -0.997011 +trainer/policy/std Mean 0.438627 +trainer/policy/std Std 0.0226658 +trainer/policy/std Max 0.461735 +trainer/policy/std Min 0.401413 +trainer/Advantage Weights Mean 4.09144 +trainer/Advantage Weights Std 16.4495 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.7588e-14 +trainer/Advantage Score Mean -0.364069 +trainer/Advantage Score Std 0.532645 +trainer/Advantage Score Max 0.867495 +trainer/Advantage Score Min -3.16716 +trainer/V1 Predictions Mean -70.9318 +trainer/V1 Predictions Std 19.8925 +trainer/V1 Predictions Max -0.686956 +trainer/V1 Predictions Min -87.1471 +trainer/VF Loss 0.054916 +expl/num steps total 180000 +expl/num paths total 187 +expl/path length Mean 500 +expl/path length Std 404 +expl/path length Max 904 +expl/path length Min 96 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.037474 +expl/Actions Std 0.800233 +expl/Actions Max 2.28284 +expl/Actions Min -2.19901 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 179226 +eval/num paths total 180 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.295835 +eval/Actions Std 0.578693 +eval/Actions Max 0.99719 +eval/Actions Min -0.994485 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.88989e-06 +time/evaluation sampling (s) 2.42019 +time/exploration sampling (s) 2.59077 +time/logging (s) 0.00825197 +time/saving (s) 0.013942 +time/training (s) 12.883 +time/epoch (s) 17.9161 +time/total (s) 3813.55 +Epoch -821 +------------------------------ ---------------- +2022-05-15 19:06:18.211856 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -820 finished +------------------------------ ---------------- +epoch -820 +replay_buffer/size 999047 +trainer/num train calls 181000 +trainer/QF1 Loss 1.27389 +trainer/QF2 Loss 1.44295 +trainer/Policy Loss 39.0878 +trainer/Q1 Predictions Mean -71.3978 +trainer/Q1 Predictions Std 18.638 +trainer/Q1 Predictions Max -2.01013 +trainer/Q1 Predictions Min -85.9066 +trainer/Q2 Predictions Mean -71.4412 +trainer/Q2 Predictions Std 18.604 +trainer/Q2 Predictions Max -2.08065 +trainer/Q2 Predictions Min -85.8266 +trainer/Q Targets Mean -71.411 +trainer/Q Targets Std 19.0744 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5203 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0115879 +trainer/policy/mean Std 0.702908 +trainer/policy/mean Max 0.999346 +trainer/policy/mean Min -0.996428 +trainer/policy/std Mean 0.437736 +trainer/policy/std Std 0.0234986 +trainer/policy/std Max 0.463199 +trainer/policy/std Min 0.397832 +trainer/Advantage Weights Mean 8.99059 +trainer/Advantage Weights Std 23.9012 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.6328e-16 +trainer/Advantage Score Mean -0.280318 +trainer/Advantage Score Std 0.693356 +trainer/Advantage Score Max 0.901215 +trainer/Advantage Score Min -3.55514 +trainer/V1 Predictions Mean -71.1734 +trainer/V1 Predictions Std 19.1375 +trainer/V1 Predictions Max -0.781228 +trainer/V1 Predictions Min -86.312 +trainer/VF Loss 0.0850508 +expl/num steps total 181000 +expl/num paths total 188 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0463839 +expl/Actions Std 0.791597 +expl/Actions Max 2.24606 +expl/Actions Min -2.37448 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 180226 +eval/num paths total 181 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0452459 +eval/Actions Std 0.683117 +eval/Actions Max 0.998774 +eval/Actions Min -0.9992 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.32901e-06 +time/evaluation sampling (s) 2.63675 +time/exploration sampling (s) 2.85064 +time/logging (s) 0.00763366 +time/saving (s) 0.013546 +time/training (s) 13.1525 +time/epoch (s) 18.6611 +time/total (s) 3832.22 +Epoch -820 +------------------------------ ---------------- +2022-05-15 19:06:36.668780 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -819 finished +------------------------------ ---------------- +epoch -819 +replay_buffer/size 999047 +trainer/num train calls 182000 +trainer/QF1 Loss 0.831682 +trainer/QF2 Loss 0.869115 +trainer/Policy Loss 17.441 +trainer/Q1 Predictions Mean -72.5005 +trainer/Q1 Predictions Std 19.2848 +trainer/Q1 Predictions Max -0.301592 +trainer/Q1 Predictions Min -87.5738 +trainer/Q2 Predictions Mean -72.4751 +trainer/Q2 Predictions Std 19.2424 +trainer/Q2 Predictions Max -0.554834 +trainer/Q2 Predictions Min -86.9512 +trainer/Q Targets Mean -72.4972 +trainer/Q Targets Std 19.4117 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.472 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0114658 +trainer/policy/mean Std 0.705746 +trainer/policy/mean Max 0.998954 +trainer/policy/mean Min -0.998024 +trainer/policy/std Mean 0.436506 +trainer/policy/std Std 0.0243932 +trainer/policy/std Max 0.463105 +trainer/policy/std Min 0.395976 +trainer/Advantage Weights Mean 3.5021 +trainer/Advantage Weights Std 15.7027 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.03201e-20 +trainer/Advantage Score Mean -0.360849 +trainer/Advantage Score Std 0.513403 +trainer/Advantage Score Max 0.697128 +trainer/Advantage Score Min -4.44359 +trainer/V1 Predictions Mean -72.2178 +trainer/V1 Predictions Std 19.4636 +trainer/V1 Predictions Max -0.0756093 +trainer/V1 Predictions Min -87.2355 +trainer/VF Loss 0.0488504 +expl/num steps total 182000 +expl/num paths total 189 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0498631 +expl/Actions Std 0.85101 +expl/Actions Max 2.37886 +expl/Actions Min -2.31203 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 181226 +eval/num paths total 182 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.402605 +eval/Actions Std 0.650266 +eval/Actions Max 0.998303 +eval/Actions Min -0.998166 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.30666e-06 +time/evaluation sampling (s) 2.57915 +time/exploration sampling (s) 2.65996 +time/logging (s) 0.00667179 +time/saving (s) 0.0104446 +time/training (s) 13.1911 +time/epoch (s) 18.4473 +time/total (s) 3850.67 +Epoch -819 +------------------------------ ---------------- +2022-05-15 19:06:55.016596 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -818 finished +------------------------------ ---------------- +epoch -818 +replay_buffer/size 999047 +trainer/num train calls 183000 +trainer/QF1 Loss 1.78378 +trainer/QF2 Loss 1.83471 +trainer/Policy Loss 13.4907 +trainer/Q1 Predictions Mean -73.1912 +trainer/Q1 Predictions Std 17.7595 +trainer/Q1 Predictions Max -0.898618 +trainer/Q1 Predictions Min -87.0975 +trainer/Q2 Predictions Mean -73.2524 +trainer/Q2 Predictions Std 17.821 +trainer/Q2 Predictions Max -1.23791 +trainer/Q2 Predictions Min -87.0155 +trainer/Q Targets Mean -73.3192 +trainer/Q Targets Std 17.6792 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0211 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0044743 +trainer/policy/mean Std 0.704977 +trainer/policy/mean Max 0.996379 +trainer/policy/mean Min -0.999085 +trainer/policy/std Mean 0.438259 +trainer/policy/std Std 0.0228323 +trainer/policy/std Max 0.464712 +trainer/policy/std Min 0.400482 +trainer/Advantage Weights Mean 2.83643 +trainer/Advantage Weights Std 13.3593 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.1122e-20 +trainer/Advantage Score Mean -0.383845 +trainer/Advantage Score Std 0.565785 +trainer/Advantage Score Max 0.83979 +trainer/Advantage Score Min -4.59454 +trainer/V1 Predictions Mean -73.0805 +trainer/V1 Predictions Std 17.6429 +trainer/V1 Predictions Max -0.799541 +trainer/V1 Predictions Min -86.7585 +trainer/VF Loss 0.0554816 +expl/num steps total 183000 +expl/num paths total 190 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0833141 +expl/Actions Std 0.838919 +expl/Actions Max 2.53181 +expl/Actions Min -2.38281 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 181921 +eval/num paths total 183 +eval/path length Mean 695 +eval/path length Std 0 +eval/path length Max 695 +eval/path length Min 695 +eval/Rewards Mean 0.00143885 +eval/Rewards Std 0.0379049 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0317039 +eval/Actions Std 0.713758 +eval/Actions Max 0.997598 +eval/Actions Min -0.998395 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.11299e-06 +time/evaluation sampling (s) 2.33198 +time/exploration sampling (s) 2.81147 +time/logging (s) 0.00713776 +time/saving (s) 0.0150603 +time/training (s) 13.1761 +time/epoch (s) 18.3417 +time/total (s) 3869.01 +Epoch -818 +------------------------------ ---------------- +2022-05-15 19:07:13.017875 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -817 finished +------------------------------ ---------------- +epoch -817 +replay_buffer/size 999047 +trainer/num train calls 184000 +trainer/QF1 Loss 0.45893 +trainer/QF2 Loss 0.564144 +trainer/Policy Loss 19.1171 +trainer/Q1 Predictions Mean -72.359 +trainer/Q1 Predictions Std 18.939 +trainer/Q1 Predictions Max -1.95153 +trainer/Q1 Predictions Min -87.181 +trainer/Q2 Predictions Mean -72.4068 +trainer/Q2 Predictions Std 18.9383 +trainer/Q2 Predictions Max -1.80131 +trainer/Q2 Predictions Min -86.9122 +trainer/Q Targets Mean -72.3988 +trainer/Q Targets Std 19.1953 +trainer/Q Targets Max 0.0631928 +trainer/Q Targets Min -87.213 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00163711 +trainer/policy/mean Std 0.701139 +trainer/policy/mean Max 0.998445 +trainer/policy/mean Min -0.996754 +trainer/policy/std Mean 0.437086 +trainer/policy/std Std 0.0219457 +trainer/policy/std Max 0.465705 +trainer/policy/std Min 0.400595 +trainer/Advantage Weights Mean 4.95717 +trainer/Advantage Weights Std 14.5662 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.31848e-16 +trainer/Advantage Score Mean -0.297584 +trainer/Advantage Score Std 0.653576 +trainer/Advantage Score Max 0.569241 +trainer/Advantage Score Min -3.65649 +trainer/V1 Predictions Mean -72.1029 +trainer/V1 Predictions Std 19.431 +trainer/V1 Predictions Max 1.09237 +trainer/V1 Predictions Min -86.9515 +trainer/VF Loss 0.065557 +expl/num steps total 184000 +expl/num paths total 191 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0605437 +expl/Actions Std 0.8179 +expl/Actions Max 2.40414 +expl/Actions Min -2.33028 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 182921 +eval/num paths total 184 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0239041 +eval/Actions Std 0.721428 +eval/Actions Max 0.998494 +eval/Actions Min -0.999779 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.01981e-06 +time/evaluation sampling (s) 2.31379 +time/exploration sampling (s) 2.68465 +time/logging (s) 0.012697 +time/saving (s) 0.0114892 +time/training (s) 12.975 +time/epoch (s) 17.9976 +time/total (s) 3887.02 +Epoch -817 +------------------------------ ---------------- +2022-05-15 19:07:31.020443 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -816 finished +------------------------------ --------------- +epoch -816 +replay_buffer/size 999047 +trainer/num train calls 185000 +trainer/QF1 Loss 0.644474 +trainer/QF2 Loss 0.629901 +trainer/Policy Loss 5.25482 +trainer/Q1 Predictions Mean -74.5098 +trainer/Q1 Predictions Std 16.4191 +trainer/Q1 Predictions Max -1.63976 +trainer/Q1 Predictions Min -87.1126 +trainer/Q2 Predictions Mean -74.4348 +trainer/Q2 Predictions Std 16.4313 +trainer/Q2 Predictions Max -1.47318 +trainer/Q2 Predictions Min -87.3101 +trainer/Q Targets Mean -74.2914 +trainer/Q Targets Std 16.2446 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1621 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0126263 +trainer/policy/mean Std 0.694557 +trainer/policy/mean Max 0.998215 +trainer/policy/mean Min -0.998672 +trainer/policy/std Mean 0.436428 +trainer/policy/std Std 0.0212231 +trainer/policy/std Max 0.462797 +trainer/policy/std Min 0.3996 +trainer/Advantage Weights Mean 1.44259 +trainer/Advantage Weights Std 9.89343 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.4054e-11 +trainer/Advantage Score Mean -0.465021 +trainer/Advantage Score Std 0.43555 +trainer/Advantage Score Max 0.83242 +trainer/Advantage Score Min -2.44507 +trainer/V1 Predictions Mean -73.9898 +trainer/V1 Predictions Std 16.4982 +trainer/V1 Predictions Max -0.765548 +trainer/V1 Predictions Min -86.3815 +trainer/VF Loss 0.0455794 +expl/num steps total 185000 +expl/num paths total 192 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.151571 +expl/Actions Std 0.842849 +expl/Actions Max 2.47859 +expl/Actions Min -2.41032 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 183921 +eval/num paths total 185 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0230098 +eval/Actions Std 0.703571 +eval/Actions Max 0.999359 +eval/Actions Min -0.999491 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.9197e-06 +time/evaluation sampling (s) 2.39525 +time/exploration sampling (s) 2.77073 +time/logging (s) 0.00851242 +time/saving (s) 0.0150735 +time/training (s) 12.7967 +time/epoch (s) 17.9862 +time/total (s) 3905.01 +Epoch -816 +------------------------------ --------------- +2022-05-15 19:07:49.233204 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -815 finished +------------------------------ ---------------- +epoch -815 +replay_buffer/size 999047 +trainer/num train calls 186000 +trainer/QF1 Loss 0.655825 +trainer/QF2 Loss 0.626439 +trainer/Policy Loss 4.65245 +trainer/Q1 Predictions Mean -73.009 +trainer/Q1 Predictions Std 19.3602 +trainer/Q1 Predictions Max -0.542186 +trainer/Q1 Predictions Min -87.0854 +trainer/Q2 Predictions Mean -72.9533 +trainer/Q2 Predictions Std 19.4122 +trainer/Q2 Predictions Max -0.460774 +trainer/Q2 Predictions Min -87.5588 +trainer/Q Targets Mean -72.9111 +trainer/Q Targets Std 19.2596 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3405 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000719826 +trainer/policy/mean Std 0.700114 +trainer/policy/mean Max 0.995279 +trainer/policy/mean Min -0.998923 +trainer/policy/std Mean 0.435704 +trainer/policy/std Std 0.0208957 +trainer/policy/std Max 0.458382 +trainer/policy/std Min 0.400487 +trainer/Advantage Weights Mean 1.19752 +trainer/Advantage Weights Std 8.87103 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.54353e-17 +trainer/Advantage Score Mean -0.465364 +trainer/Advantage Score Std 0.580666 +trainer/Advantage Score Max 0.771553 +trainer/Advantage Score Min -3.82104 +trainer/V1 Predictions Mean -72.649 +trainer/V1 Predictions Std 19.4909 +trainer/V1 Predictions Max 0.0174871 +trainer/V1 Predictions Min -87.1017 +trainer/VF Loss 0.0589314 +expl/num steps total 186000 +expl/num paths total 193 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0768732 +expl/Actions Std 0.796249 +expl/Actions Max 2.20841 +expl/Actions Min -2.15071 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 184921 +eval/num paths total 186 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.101001 +eval/Actions Std 0.683709 +eval/Actions Max 0.997809 +eval/Actions Min -0.996718 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.78093e-06 +time/evaluation sampling (s) 2.3377 +time/exploration sampling (s) 2.72841 +time/logging (s) 0.00798788 +time/saving (s) 0.0108106 +time/training (s) 13.1182 +time/epoch (s) 18.2031 +time/total (s) 3923.22 +Epoch -815 +------------------------------ ---------------- +2022-05-15 19:08:06.865437 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -814 finished +------------------------------ ---------------- +epoch -814 +replay_buffer/size 999047 +trainer/num train calls 187000 +trainer/QF1 Loss 0.756077 +trainer/QF2 Loss 0.665944 +trainer/Policy Loss 12.822 +trainer/Q1 Predictions Mean -75.7635 +trainer/Q1 Predictions Std 13.8243 +trainer/Q1 Predictions Max -0.521825 +trainer/Q1 Predictions Min -86.2511 +trainer/Q2 Predictions Mean -75.6653 +trainer/Q2 Predictions Std 13.7509 +trainer/Q2 Predictions Max -0.836197 +trainer/Q2 Predictions Min -86.1973 +trainer/Q Targets Mean -75.3156 +trainer/Q Targets Std 13.5562 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5214 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.019188 +trainer/policy/mean Std 0.69951 +trainer/policy/mean Max 0.996025 +trainer/policy/mean Min -0.9969 +trainer/policy/std Mean 0.437329 +trainer/policy/std Std 0.0214085 +trainer/policy/std Max 0.462207 +trainer/policy/std Min 0.400205 +trainer/Advantage Weights Mean 1.46504 +trainer/Advantage Weights Std 10.7805 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.01067e-14 +trainer/Advantage Score Mean -0.448008 +trainer/Advantage Score Std 0.428125 +trainer/Advantage Score Max 1.19166 +trainer/Advantage Score Min -3.15377 +trainer/V1 Predictions Mean -75.0767 +trainer/V1 Predictions Std 13.6856 +trainer/V1 Predictions Max -1.41412 +trainer/V1 Predictions Min -86.4861 +trainer/VF Loss 0.0484749 +expl/num steps total 187000 +expl/num paths total 195 +expl/path length Mean 500 +expl/path length Std 385 +expl/path length Max 885 +expl/path length Min 115 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0154066 +expl/Actions Std 0.803143 +expl/Actions Max 2.34967 +expl/Actions Min -2.28867 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 185921 +eval/num paths total 187 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.071099 +eval/Actions Std 0.667365 +eval/Actions Max 0.999059 +eval/Actions Min -0.999226 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.8892e-06 +time/evaluation sampling (s) 2.23538 +time/exploration sampling (s) 2.69034 +time/logging (s) 0.00841378 +time/saving (s) 0.0133861 +time/training (s) 12.6789 +time/epoch (s) 17.6265 +time/total (s) 3940.85 +Epoch -814 +------------------------------ ---------------- +2022-05-15 19:08:24.673167 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -813 finished +------------------------------ ---------------- +epoch -813 +replay_buffer/size 999047 +trainer/num train calls 188000 +trainer/QF1 Loss 2.72006 +trainer/QF2 Loss 2.77139 +trainer/Policy Loss 33.5905 +trainer/Q1 Predictions Mean -72.6332 +trainer/Q1 Predictions Std 18.0797 +trainer/Q1 Predictions Max -3.88256 +trainer/Q1 Predictions Min -86.4841 +trainer/Q2 Predictions Mean -72.6231 +trainer/Q2 Predictions Std 18.0661 +trainer/Q2 Predictions Max -4.02029 +trainer/Q2 Predictions Min -86.448 +trainer/Q Targets Mean -73.3104 +trainer/Q Targets Std 18.0992 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1956 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0199887 +trainer/policy/mean Std 0.700896 +trainer/policy/mean Max 0.9976 +trainer/policy/mean Min -0.997959 +trainer/policy/std Mean 0.436736 +trainer/policy/std Std 0.0209318 +trainer/policy/std Max 0.460767 +trainer/policy/std Min 0.40346 +trainer/Advantage Weights Mean 6.23033 +trainer/Advantage Weights Std 19.3745 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.44843e-12 +trainer/Advantage Score Mean -0.164976 +trainer/Advantage Score Std 0.465948 +trainer/Advantage Score Max 1.87922 +trainer/Advantage Score Min -2.67356 +trainer/V1 Predictions Mean -73.0245 +trainer/V1 Predictions Std 18.1856 +trainer/V1 Predictions Max -3.57091 +trainer/V1 Predictions Min -87.2406 +trainer/VF Loss 0.0570563 +expl/num steps total 188000 +expl/num paths total 196 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0987046 +expl/Actions Std 0.808763 +expl/Actions Max 2.22591 +expl/Actions Min -2.79929 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 186921 +eval/num paths total 188 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.26982 +eval/Actions Std 0.70513 +eval/Actions Max 0.998106 +eval/Actions Min -0.997954 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94391e-06 +time/evaluation sampling (s) 2.35511 +time/exploration sampling (s) 2.70088 +time/logging (s) 0.00666275 +time/saving (s) 0.00959671 +time/training (s) 12.7259 +time/epoch (s) 17.7982 +time/total (s) 3958.65 +Epoch -813 +------------------------------ ---------------- +2022-05-15 19:08:42.884953 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -812 finished +------------------------------ ---------------- +epoch -812 +replay_buffer/size 999047 +trainer/num train calls 189000 +trainer/QF1 Loss 1.00906 +trainer/QF2 Loss 0.930505 +trainer/Policy Loss 35.8577 +trainer/Q1 Predictions Mean -72.3345 +trainer/Q1 Predictions Std 18.1736 +trainer/Q1 Predictions Max -0.596121 +trainer/Q1 Predictions Min -86.7921 +trainer/Q2 Predictions Mean -72.3104 +trainer/Q2 Predictions Std 18.1908 +trainer/Q2 Predictions Max -1.37313 +trainer/Q2 Predictions Min -86.7666 +trainer/Q Targets Mean -72.459 +trainer/Q Targets Std 18.4111 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6091 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0361956 +trainer/policy/mean Std 0.704836 +trainer/policy/mean Max 0.997907 +trainer/policy/mean Min -0.998784 +trainer/policy/std Mean 0.435845 +trainer/policy/std Std 0.0216717 +trainer/policy/std Max 0.461047 +trainer/policy/std Min 0.402328 +trainer/Advantage Weights Mean 4.96816 +trainer/Advantage Weights Std 18.8012 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.02276e-09 +trainer/Advantage Score Mean -0.266895 +trainer/Advantage Score Std 0.420317 +trainer/Advantage Score Max 1.42071 +trainer/Advantage Score Min -2.07008 +trainer/V1 Predictions Mean -72.3787 +trainer/V1 Predictions Std 18.0982 +trainer/V1 Predictions Max 0.192837 +trainer/V1 Predictions Min -86.5104 +trainer/VF Loss 0.0560057 +expl/num steps total 189000 +expl/num paths total 197 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0431129 +expl/Actions Std 0.795254 +expl/Actions Max 2.2353 +expl/Actions Min -2.43471 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 187921 +eval/num paths total 189 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.288291 +eval/Actions Std 0.739595 +eval/Actions Max 0.999501 +eval/Actions Min -0.998927 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.74276e-06 +time/evaluation sampling (s) 2.44307 +time/exploration sampling (s) 2.68735 +time/logging (s) 0.00701371 +time/saving (s) 0.0104113 +time/training (s) 13.0586 +time/epoch (s) 18.2064 +time/total (s) 3976.86 +Epoch -812 +------------------------------ ---------------- +2022-05-15 19:09:01.771723 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -811 finished +------------------------------ ---------------- +epoch -811 +replay_buffer/size 999047 +trainer/num train calls 190000 +trainer/QF1 Loss 0.87528 +trainer/QF2 Loss 0.824464 +trainer/Policy Loss 15.4276 +trainer/Q1 Predictions Mean -74.0803 +trainer/Q1 Predictions Std 15.7083 +trainer/Q1 Predictions Max -3.25888 +trainer/Q1 Predictions Min -86.2296 +trainer/Q2 Predictions Mean -74.2375 +trainer/Q2 Predictions Std 15.6903 +trainer/Q2 Predictions Max -4.07833 +trainer/Q2 Predictions Min -86.3032 +trainer/Q Targets Mean -74.2591 +trainer/Q Targets Std 15.8028 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.429 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0138905 +trainer/policy/mean Std 0.709137 +trainer/policy/mean Max 0.998557 +trainer/policy/mean Min -0.99356 +trainer/policy/std Mean 0.434511 +trainer/policy/std Std 0.0232838 +trainer/policy/std Max 0.460814 +trainer/policy/std Min 0.397702 +trainer/Advantage Weights Mean 4.12902 +trainer/Advantage Weights Std 17.7358 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.83376e-13 +trainer/Advantage Score Mean -0.332462 +trainer/Advantage Score Std 0.486472 +trainer/Advantage Score Max 1.47856 +trainer/Advantage Score Min -2.93272 +trainer/V1 Predictions Mean -74.0522 +trainer/V1 Predictions Std 15.6596 +trainer/V1 Predictions Max -5.08644 +trainer/V1 Predictions Min -86.849 +trainer/VF Loss 0.0573593 +expl/num steps total 190000 +expl/num paths total 198 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0250152 +expl/Actions Std 0.803845 +expl/Actions Max 2.26514 +expl/Actions Min -2.29328 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 188921 +eval/num paths total 190 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0231959 +eval/Actions Std 0.709586 +eval/Actions Max 0.999106 +eval/Actions Min -0.997548 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.6077e-06 +time/evaluation sampling (s) 2.42777 +time/exploration sampling (s) 2.84211 +time/logging (s) 0.00740811 +time/saving (s) 0.0119138 +time/training (s) 13.5918 +time/epoch (s) 18.881 +time/total (s) 3995.75 +Epoch -811 +------------------------------ ---------------- +2022-05-15 19:09:19.881636 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -810 finished +------------------------------ ---------------- +epoch -810 +replay_buffer/size 999047 +trainer/num train calls 191000 +trainer/QF1 Loss 0.597976 +trainer/QF2 Loss 0.523398 +trainer/Policy Loss 43.6415 +trainer/Q1 Predictions Mean -74.732 +trainer/Q1 Predictions Std 15.7756 +trainer/Q1 Predictions Max -2.03172 +trainer/Q1 Predictions Min -86.9402 +trainer/Q2 Predictions Mean -74.6549 +trainer/Q2 Predictions Std 15.7488 +trainer/Q2 Predictions Max -1.72435 +trainer/Q2 Predictions Min -87.636 +trainer/Q Targets Mean -74.9162 +trainer/Q Targets Std 16.0238 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4488 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00572285 +trainer/policy/mean Std 0.705602 +trainer/policy/mean Max 0.997863 +trainer/policy/mean Min -0.995041 +trainer/policy/std Mean 0.437259 +trainer/policy/std Std 0.023741 +trainer/policy/std Max 0.463292 +trainer/policy/std Min 0.399529 +trainer/Advantage Weights Mean 7.74591 +trainer/Advantage Weights Std 20.1534 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.77925e-06 +trainer/Advantage Score Mean -0.103534 +trainer/Advantage Score Std 0.374089 +trainer/Advantage Score Max 1.30131 +trainer/Advantage Score Min -1.27933 +trainer/V1 Predictions Mean -74.7636 +trainer/V1 Predictions Std 16.0206 +trainer/V1 Predictions Max -1.5124 +trainer/V1 Predictions Min -87.2943 +trainer/VF Loss 0.0473334 +expl/num steps total 191000 +expl/num paths total 199 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.035113 +expl/Actions Std 0.802051 +expl/Actions Max 2.44853 +expl/Actions Min -2.51245 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 189921 +eval/num paths total 191 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.287918 +eval/Actions Std 0.652413 +eval/Actions Max 0.998545 +eval/Actions Min -0.996665 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.3048e-06 +time/evaluation sampling (s) 2.35384 +time/exploration sampling (s) 2.76052 +time/logging (s) 0.00761124 +time/saving (s) 0.0112753 +time/training (s) 12.9704 +time/epoch (s) 18.1037 +time/total (s) 4013.86 +Epoch -810 +------------------------------ ---------------- +2022-05-15 19:09:38.065677 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -809 finished +------------------------------ ---------------- +epoch -809 +replay_buffer/size 999047 +trainer/num train calls 192000 +trainer/QF1 Loss 0.776405 +trainer/QF2 Loss 0.762094 +trainer/Policy Loss 29.4458 +trainer/Q1 Predictions Mean -73.423 +trainer/Q1 Predictions Std 16.5309 +trainer/Q1 Predictions Max -5.26088 +trainer/Q1 Predictions Min -86.3475 +trainer/Q2 Predictions Mean -73.4595 +trainer/Q2 Predictions Std 16.5052 +trainer/Q2 Predictions Max -4.7997 +trainer/Q2 Predictions Min -86.4959 +trainer/Q Targets Mean -73.7129 +trainer/Q Targets Std 16.8757 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6429 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00674212 +trainer/policy/mean Std 0.697501 +trainer/policy/mean Max 0.998447 +trainer/policy/mean Min -0.997914 +trainer/policy/std Mean 0.43559 +trainer/policy/std Std 0.0236489 +trainer/policy/std Max 0.461316 +trainer/policy/std Min 0.395045 +trainer/Advantage Weights Mean 4.5912 +trainer/Advantage Weights Std 18.6824 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.8817e-11 +trainer/Advantage Score Mean -0.357224 +trainer/Advantage Score Std 0.479591 +trainer/Advantage Score Max 0.964659 +trainer/Advantage Score Min -2.37429 +trainer/V1 Predictions Mean -73.5209 +trainer/V1 Predictions Std 16.8252 +trainer/V1 Predictions Max -4.01881 +trainer/V1 Predictions Min -86.5741 +trainer/VF Loss 0.0515878 +expl/num steps total 192000 +expl/num paths total 200 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00712694 +expl/Actions Std 0.81523 +expl/Actions Max 2.21048 +expl/Actions Min -2.25191 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 190921 +eval/num paths total 192 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.234905 +eval/Actions Std 0.694106 +eval/Actions Max 0.99817 +eval/Actions Min -0.999537 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.03679e-05 +time/evaluation sampling (s) 2.62337 +time/exploration sampling (s) 2.87237 +time/logging (s) 0.0115411 +time/saving (s) 0.0150976 +time/training (s) 12.6588 +time/epoch (s) 18.1812 +time/total (s) 4032.04 +Epoch -809 +------------------------------ ---------------- +2022-05-15 19:09:56.558756 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -808 finished +------------------------------ ---------------- +epoch -808 +replay_buffer/size 999047 +trainer/num train calls 193000 +trainer/QF1 Loss 1.06244 +trainer/QF2 Loss 1.19568 +trainer/Policy Loss 34.7656 +trainer/Q1 Predictions Mean -71.4385 +trainer/Q1 Predictions Std 19.9592 +trainer/Q1 Predictions Max -0.99555 +trainer/Q1 Predictions Min -87.7884 +trainer/Q2 Predictions Mean -71.3881 +trainer/Q2 Predictions Std 19.9026 +trainer/Q2 Predictions Max -1.27727 +trainer/Q2 Predictions Min -87.1765 +trainer/Q Targets Mean -71.5122 +trainer/Q Targets Std 20.2987 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6369 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0293841 +trainer/policy/mean Std 0.709161 +trainer/policy/mean Max 0.997541 +trainer/policy/mean Min -0.997911 +trainer/policy/std Mean 0.435305 +trainer/policy/std Std 0.0230177 +trainer/policy/std Max 0.460245 +trainer/policy/std Min 0.39549 +trainer/Advantage Weights Mean 5.57175 +trainer/Advantage Weights Std 18.1491 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.35542e-12 +trainer/Advantage Score Mean -0.273433 +trainer/Advantage Score Std 0.519325 +trainer/Advantage Score Max 1.23406 +trainer/Advantage Score Min -2.73269 +trainer/V1 Predictions Mean -71.316 +trainer/V1 Predictions Std 20.145 +trainer/V1 Predictions Max 0.47423 +trainer/V1 Predictions Min -87.4853 +trainer/VF Loss 0.0568824 +expl/num steps total 193000 +expl/num paths total 202 +expl/path length Mean 500 +expl/path length Std 499 +expl/path length Max 999 +expl/path length Min 1 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0516355 +expl/Actions Std 0.798917 +expl/Actions Max 2.43382 +expl/Actions Min -2.30165 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 191921 +eval/num paths total 193 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0124289 +eval/Actions Std 0.711672 +eval/Actions Max 0.997641 +eval/Actions Min -0.998501 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18093e-06 +time/evaluation sampling (s) 2.27729 +time/exploration sampling (s) 2.69793 +time/logging (s) 0.00728614 +time/saving (s) 0.0108735 +time/training (s) 13.4871 +time/epoch (s) 18.4805 +time/total (s) 4050.53 +Epoch -808 +------------------------------ ---------------- +2022-05-15 19:10:14.965758 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -807 finished +------------------------------ ---------------- +epoch -807 +replay_buffer/size 999047 +trainer/num train calls 194000 +trainer/QF1 Loss 0.879275 +trainer/QF2 Loss 0.914873 +trainer/Policy Loss 16.7198 +trainer/Q1 Predictions Mean -72.7265 +trainer/Q1 Predictions Std 18.9245 +trainer/Q1 Predictions Max -1.46567 +trainer/Q1 Predictions Min -87.2127 +trainer/Q2 Predictions Mean -72.7784 +trainer/Q2 Predictions Std 18.9162 +trainer/Q2 Predictions Max -1.53139 +trainer/Q2 Predictions Min -87.5835 +trainer/Q Targets Mean -72.8354 +trainer/Q Targets Std 18.6483 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7999 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0263714 +trainer/policy/mean Std 0.712156 +trainer/policy/mean Max 0.997322 +trainer/policy/mean Min -0.998041 +trainer/policy/std Mean 0.433282 +trainer/policy/std Std 0.0215399 +trainer/policy/std Max 0.455675 +trainer/policy/std Min 0.396687 +trainer/Advantage Weights Mean 4.07645 +trainer/Advantage Weights Std 16.1969 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.16635e-13 +trainer/Advantage Score Mean -0.348982 +trainer/Advantage Score Std 0.51058 +trainer/Advantage Score Max 1.09136 +trainer/Advantage Score Min -2.79642 +trainer/V1 Predictions Mean -72.5345 +trainer/V1 Predictions Std 18.7872 +trainer/V1 Predictions Max -0.978739 +trainer/V1 Predictions Min -86.7301 +trainer/VF Loss 0.0531293 +expl/num steps total 194000 +expl/num paths total 203 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.185285 +expl/Actions Std 0.903102 +expl/Actions Max 2.53195 +expl/Actions Min -2.58663 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 192858 +eval/num paths total 194 +eval/path length Mean 937 +eval/path length Std 0 +eval/path length Max 937 +eval/path length Min 937 +eval/Rewards Mean 0.00106724 +eval/Rewards Std 0.0326511 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0453516 +eval/Actions Std 0.722127 +eval/Actions Max 0.998904 +eval/Actions Min -0.998997 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.04217e-06 +time/evaluation sampling (s) 2.3009 +time/exploration sampling (s) 2.82034 +time/logging (s) 0.00883099 +time/saving (s) 0.0144852 +time/training (s) 13.2569 +time/epoch (s) 18.4015 +time/total (s) 4068.93 +Epoch -807 +------------------------------ ---------------- +2022-05-15 19:10:32.641931 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -806 finished +------------------------------ ---------------- +epoch -806 +replay_buffer/size 999047 +trainer/num train calls 195000 +trainer/QF1 Loss 0.982236 +trainer/QF2 Loss 0.985302 +trainer/Policy Loss 25.4027 +trainer/Q1 Predictions Mean -74.7314 +trainer/Q1 Predictions Std 14.889 +trainer/Q1 Predictions Max -3.25039 +trainer/Q1 Predictions Min -86.8012 +trainer/Q2 Predictions Mean -74.701 +trainer/Q2 Predictions Std 14.9172 +trainer/Q2 Predictions Max -2.96596 +trainer/Q2 Predictions Min -86.6788 +trainer/Q Targets Mean -74.7925 +trainer/Q Targets Std 14.785 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2853 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0146984 +trainer/policy/mean Std 0.704952 +trainer/policy/mean Max 0.999309 +trainer/policy/mean Min -0.994982 +trainer/policy/std Mean 0.434158 +trainer/policy/std Std 0.0223132 +trainer/policy/std Max 0.45822 +trainer/policy/std Min 0.397295 +trainer/Advantage Weights Mean 6.1467 +trainer/Advantage Weights Std 20.0255 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.14429e-10 +trainer/Advantage Score Mean -0.278015 +trainer/Advantage Score Std 0.495879 +trainer/Advantage Score Max 1.23093 +trainer/Advantage Score Min -2.16041 +trainer/V1 Predictions Mean -74.5343 +trainer/V1 Predictions Std 14.8221 +trainer/V1 Predictions Max -4.00122 +trainer/V1 Predictions Min -87.7853 +trainer/VF Loss 0.0566707 +expl/num steps total 195000 +expl/num paths total 204 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0342436 +expl/Actions Std 0.832247 +expl/Actions Max 2.4886 +expl/Actions Min -2.31445 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 193858 +eval/num paths total 195 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.064658 +eval/Actions Std 0.729612 +eval/Actions Max 0.999154 +eval/Actions Min -0.999327 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.21213e-06 +time/evaluation sampling (s) 2.3482 +time/exploration sampling (s) 2.71156 +time/logging (s) 0.00806265 +time/saving (s) 0.0144 +time/training (s) 12.585 +time/epoch (s) 17.6672 +time/total (s) 4086.61 +Epoch -806 +------------------------------ ---------------- +2022-05-15 19:10:50.777121 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -805 finished +------------------------------ ---------------- +epoch -805 +replay_buffer/size 999047 +trainer/num train calls 196000 +trainer/QF1 Loss 0.677 +trainer/QF2 Loss 0.649155 +trainer/Policy Loss 57.5333 +trainer/Q1 Predictions Mean -74.3599 +trainer/Q1 Predictions Std 14.9354 +trainer/Q1 Predictions Max -2.25491 +trainer/Q1 Predictions Min -88.487 +trainer/Q2 Predictions Mean -74.441 +trainer/Q2 Predictions Std 14.9352 +trainer/Q2 Predictions Max -2.2202 +trainer/Q2 Predictions Min -89.2807 +trainer/Q Targets Mean -74.7413 +trainer/Q Targets Std 15.1027 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.2819 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0210038 +trainer/policy/mean Std 0.700339 +trainer/policy/mean Max 0.99725 +trainer/policy/mean Min -0.995588 +trainer/policy/std Mean 0.432265 +trainer/policy/std Std 0.0210866 +trainer/policy/std Max 0.454522 +trainer/policy/std Min 0.396306 +trainer/Advantage Weights Mean 12.3949 +trainer/Advantage Weights Std 26.9125 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.95805e-13 +trainer/Advantage Score Mean -0.0781352 +trainer/Advantage Score Std 0.465587 +trainer/Advantage Score Max 0.989601 +trainer/Advantage Score Min -2.92617 +trainer/V1 Predictions Mean -74.5202 +trainer/V1 Predictions Std 15.0877 +trainer/V1 Predictions Max -2.63584 +trainer/V1 Predictions Min -88.2208 +trainer/VF Loss 0.0577531 +expl/num steps total 196000 +expl/num paths total 206 +expl/path length Mean 500 +expl/path length Std 443 +expl/path length Max 943 +expl/path length Min 57 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0218703 +expl/Actions Std 0.78957 +expl/Actions Max 2.41268 +expl/Actions Min -2.34395 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 194858 +eval/num paths total 196 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0357354 +eval/Actions Std 0.708172 +eval/Actions Max 0.998361 +eval/Actions Min -0.999398 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.07616e-06 +time/evaluation sampling (s) 2.30712 +time/exploration sampling (s) 2.72885 +time/logging (s) 0.012229 +time/saving (s) 0.0198508 +time/training (s) 13.0624 +time/epoch (s) 18.1304 +time/total (s) 4104.74 +Epoch -805 +------------------------------ ---------------- +2022-05-15 19:11:08.354039 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -804 finished +------------------------------ ---------------- +epoch -804 +replay_buffer/size 999047 +trainer/num train calls 197000 +trainer/QF1 Loss 1.32766 +trainer/QF2 Loss 1.42855 +trainer/Policy Loss 12.6382 +trainer/Q1 Predictions Mean -73.1913 +trainer/Q1 Predictions Std 18.3181 +trainer/Q1 Predictions Max -0.931765 +trainer/Q1 Predictions Min -86.867 +trainer/Q2 Predictions Mean -73.2153 +trainer/Q2 Predictions Std 18.2884 +trainer/Q2 Predictions Max -0.894163 +trainer/Q2 Predictions Min -87.1002 +trainer/Q Targets Mean -72.8116 +trainer/Q Targets Std 18.3768 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4524 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0103983 +trainer/policy/mean Std 0.716851 +trainer/policy/mean Max 0.997229 +trainer/policy/mean Min -0.99933 +trainer/policy/std Mean 0.435285 +trainer/policy/std Std 0.020876 +trainer/policy/std Max 0.460097 +trainer/policy/std Min 0.402107 +trainer/Advantage Weights Mean 2.67947 +trainer/Advantage Weights Std 13.9923 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.47537e-12 +trainer/Advantage Score Mean -0.53171 +trainer/Advantage Score Std 0.58234 +trainer/Advantage Score Max 2.45076 +trainer/Advantage Score Min -2.61324 +trainer/V1 Predictions Mean -72.5996 +trainer/V1 Predictions Std 18.349 +trainer/V1 Predictions Max 0.450516 +trainer/V1 Predictions Min -86.3354 +trainer/VF Loss 0.0890619 +expl/num steps total 197000 +expl/num paths total 207 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0404328 +expl/Actions Std 0.827567 +expl/Actions Max 2.3604 +expl/Actions Min -2.9297 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 195858 +eval/num paths total 197 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.235313 +eval/Actions Std 0.71063 +eval/Actions Max 0.99824 +eval/Actions Min -0.997456 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.79397e-06 +time/evaluation sampling (s) 2.31011 +time/exploration sampling (s) 2.71902 +time/logging (s) 0.00716415 +time/saving (s) 0.0101993 +time/training (s) 12.5175 +time/epoch (s) 17.564 +time/total (s) 4122.31 +Epoch -804 +------------------------------ ---------------- +2022-05-15 19:11:26.009899 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -803 finished +------------------------------ ---------------- +epoch -803 +replay_buffer/size 999047 +trainer/num train calls 198000 +trainer/QF1 Loss 0.912376 +trainer/QF2 Loss 1.02524 +trainer/Policy Loss 18.1885 +trainer/Q1 Predictions Mean -74.9603 +trainer/Q1 Predictions Std 15.1608 +trainer/Q1 Predictions Max -2.10989 +trainer/Q1 Predictions Min -87.6678 +trainer/Q2 Predictions Mean -74.9091 +trainer/Q2 Predictions Std 15.1376 +trainer/Q2 Predictions Max -2.40617 +trainer/Q2 Predictions Min -87.5953 +trainer/Q Targets Mean -75.3212 +trainer/Q Targets Std 14.7705 +trainer/Q Targets Max -3.75161 +trainer/Q Targets Min -87.6732 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00190229 +trainer/policy/mean Std 0.720265 +trainer/policy/mean Max 0.998236 +trainer/policy/mean Min -0.998048 +trainer/policy/std Mean 0.434466 +trainer/policy/std Std 0.021702 +trainer/policy/std Max 0.45812 +trainer/policy/std Min 0.400728 +trainer/Advantage Weights Mean 4.34429 +trainer/Advantage Weights Std 17.4992 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.17944e-10 +trainer/Advantage Score Mean -0.268799 +trainer/Advantage Score Std 0.442577 +trainer/Advantage Score Max 1.16996 +trainer/Advantage Score Min -2.18691 +trainer/V1 Predictions Mean -75.0271 +trainer/V1 Predictions Std 14.9311 +trainer/V1 Predictions Max -2.99719 +trainer/V1 Predictions Min -87.5487 +trainer/VF Loss 0.0457807 +expl/num steps total 198000 +expl/num paths total 208 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0431452 +expl/Actions Std 0.821431 +expl/Actions Max 2.35982 +expl/Actions Min -2.67839 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 196858 +eval/num paths total 198 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0272984 +eval/Actions Std 0.721658 +eval/Actions Max 0.999688 +eval/Actions Min -0.999418 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.57231e-06 +time/evaluation sampling (s) 2.24716 +time/exploration sampling (s) 2.81091 +time/logging (s) 0.00701928 +time/saving (s) 0.00937122 +time/training (s) 12.5749 +time/epoch (s) 17.6494 +time/total (s) 4139.96 +Epoch -803 +------------------------------ ---------------- +2022-05-15 19:11:44.075028 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -802 finished +------------------------------ ---------------- +epoch -802 +replay_buffer/size 999047 +trainer/num train calls 199000 +trainer/QF1 Loss 0.762179 +trainer/QF2 Loss 0.874776 +trainer/Policy Loss 54.584 +trainer/Q1 Predictions Mean -75.1414 +trainer/Q1 Predictions Std 14.3272 +trainer/Q1 Predictions Max -0.530959 +trainer/Q1 Predictions Min -87.5855 +trainer/Q2 Predictions Mean -75.0523 +trainer/Q2 Predictions Std 14.2692 +trainer/Q2 Predictions Max -0.481783 +trainer/Q2 Predictions Min -87.2564 +trainer/Q Targets Mean -75.7244 +trainer/Q Targets Std 14.1437 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4407 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00295187 +trainer/policy/mean Std 0.704444 +trainer/policy/mean Max 0.996078 +trainer/policy/mean Min -0.997612 +trainer/policy/std Mean 0.435422 +trainer/policy/std Std 0.0226834 +trainer/policy/std Max 0.462401 +trainer/policy/std Min 0.397838 +trainer/Advantage Weights Mean 12.3155 +trainer/Advantage Weights Std 27.3916 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.65746e-07 +trainer/Advantage Score Mean -0.0086245 +trainer/Advantage Score Std 0.374466 +trainer/Advantage Score Max 1.43475 +trainer/Advantage Score Min -1.38504 +trainer/V1 Predictions Mean -75.4639 +trainer/V1 Predictions Std 14.2822 +trainer/V1 Predictions Max -0.442608 +trainer/V1 Predictions Min -87.374 +trainer/VF Loss 0.0620213 +expl/num steps total 199000 +expl/num paths total 210 +expl/path length Mean 500 +expl/path length Std 458 +expl/path length Max 958 +expl/path length Min 42 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0442644 +expl/Actions Std 0.818752 +expl/Actions Max 2.4031 +expl/Actions Min -2.38841 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 197858 +eval/num paths total 199 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0619787 +eval/Actions Std 0.682106 +eval/Actions Max 0.998723 +eval/Actions Min -0.998214 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.40166e-06 +time/evaluation sampling (s) 2.24989 +time/exploration sampling (s) 2.70281 +time/logging (s) 0.00695894 +time/saving (s) 0.00975443 +time/training (s) 13.0893 +time/epoch (s) 18.0587 +time/total (s) 4158.03 +Epoch -802 +------------------------------ ---------------- +2022-05-15 19:12:01.913907 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -801 finished +------------------------------ ---------------- +epoch -801 +replay_buffer/size 999047 +trainer/num train calls 200000 +trainer/QF1 Loss 11.4823 +trainer/QF2 Loss 11.3822 +trainer/Policy Loss 13.3443 +trainer/Q1 Predictions Mean -75.1269 +trainer/Q1 Predictions Std 14.3084 +trainer/Q1 Predictions Max -2.29928 +trainer/Q1 Predictions Min -86.8875 +trainer/Q2 Predictions Mean -75.0342 +trainer/Q2 Predictions Std 14.3266 +trainer/Q2 Predictions Max -1.77114 +trainer/Q2 Predictions Min -86.8287 +trainer/Q Targets Mean -74.7902 +trainer/Q Targets Std 14.5725 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4693 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0173503 +trainer/policy/mean Std 0.69774 +trainer/policy/mean Max 0.993905 +trainer/policy/mean Min -0.996416 +trainer/policy/std Mean 0.434826 +trainer/policy/std Std 0.0214528 +trainer/policy/std Max 0.459892 +trainer/policy/std Min 0.399713 +trainer/Advantage Weights Mean 4.10815 +trainer/Advantage Weights Std 17.0693 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.36129e-16 +trainer/Advantage Score Mean -0.352493 +trainer/Advantage Score Std 0.501164 +trainer/Advantage Score Max 1.18227 +trainer/Advantage Score Min -3.46048 +trainer/V1 Predictions Mean -74.7144 +trainer/V1 Predictions Std 14.5147 +trainer/V1 Predictions Max -0.827814 +trainer/V1 Predictions Min -86.3194 +trainer/VF Loss 0.0530967 +expl/num steps total 200000 +expl/num paths total 212 +expl/path length Mean 500 +expl/path length Std 290 +expl/path length Max 790 +expl/path length Min 210 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0523749 +expl/Actions Std 0.838073 +expl/Actions Max 2.73083 +expl/Actions Min -2.29618 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 198858 +eval/num paths total 200 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0560696 +eval/Actions Std 0.648178 +eval/Actions Max 0.998205 +eval/Actions Min -0.999918 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.92202e-06 +time/evaluation sampling (s) 2.3198 +time/exploration sampling (s) 2.75037 +time/logging (s) 0.0125677 +time/saving (s) 0.0112878 +time/training (s) 12.7445 +time/epoch (s) 17.8385 +time/total (s) 4175.87 +Epoch -801 +------------------------------ ---------------- +2022-05-15 19:12:19.932718 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -800 finished +------------------------------ ---------------- +epoch -800 +replay_buffer/size 999047 +trainer/num train calls 201000 +trainer/QF1 Loss 0.821536 +trainer/QF2 Loss 0.748172 +trainer/Policy Loss 5.18282 +trainer/Q1 Predictions Mean -74.7935 +trainer/Q1 Predictions Std 15.3279 +trainer/Q1 Predictions Max -6.74242 +trainer/Q1 Predictions Min -87.0905 +trainer/Q2 Predictions Mean -74.8078 +trainer/Q2 Predictions Std 15.3578 +trainer/Q2 Predictions Max -6.33064 +trainer/Q2 Predictions Min -87.0615 +trainer/Q Targets Mean -74.4705 +trainer/Q Targets Std 15.2685 +trainer/Q Targets Max -4.93154 +trainer/Q Targets Min -86.9971 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0155161 +trainer/policy/mean Std 0.71305 +trainer/policy/mean Max 0.998104 +trainer/policy/mean Min -0.994734 +trainer/policy/std Mean 0.434158 +trainer/policy/std Std 0.0213853 +trainer/policy/std Max 0.457379 +trainer/policy/std Min 0.397829 +trainer/Advantage Weights Mean 1.35511 +trainer/Advantage Weights Std 10.7858 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.38461e-12 +trainer/Advantage Score Mean -0.533398 +trainer/Advantage Score Std 0.430506 +trainer/Advantage Score Max 0.835953 +trainer/Advantage Score Min -2.64118 +trainer/V1 Predictions Mean -74.1683 +trainer/V1 Predictions Std 15.4261 +trainer/V1 Predictions Max -5.57335 +trainer/V1 Predictions Min -86.8566 +trainer/VF Loss 0.0518833 +expl/num steps total 201000 +expl/num paths total 213 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0276949 +expl/Actions Std 0.800817 +expl/Actions Max 2.35818 +expl/Actions Min -2.5494 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 199858 +eval/num paths total 201 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0271926 +eval/Actions Std 0.730071 +eval/Actions Max 0.999425 +eval/Actions Min -0.998156 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83215e-06 +time/evaluation sampling (s) 2.27568 +time/exploration sampling (s) 2.68928 +time/logging (s) 0.00682077 +time/saving (s) 0.01836 +time/training (s) 13.0107 +time/epoch (s) 18.0008 +time/total (s) 4193.88 +Epoch -800 +------------------------------ ---------------- +2022-05-15 19:12:37.766054 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -799 finished +------------------------------ ---------------- +epoch -799 +replay_buffer/size 999047 +trainer/num train calls 202000 +trainer/QF1 Loss 0.79507 +trainer/QF2 Loss 0.771283 +trainer/Policy Loss 20.4594 +trainer/Q1 Predictions Mean -71.8985 +trainer/Q1 Predictions Std 18.4588 +trainer/Q1 Predictions Max -1.42392 +trainer/Q1 Predictions Min -86.5754 +trainer/Q2 Predictions Mean -71.9676 +trainer/Q2 Predictions Std 18.4978 +trainer/Q2 Predictions Max -1.49705 +trainer/Q2 Predictions Min -87.1834 +trainer/Q Targets Mean -72.1838 +trainer/Q Targets Std 18.9778 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9086 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0201458 +trainer/policy/mean Std 0.698135 +trainer/policy/mean Max 0.998266 +trainer/policy/mean Min -0.997418 +trainer/policy/std Mean 0.432509 +trainer/policy/std Std 0.0217065 +trainer/policy/std Max 0.457731 +trainer/policy/std Min 0.395245 +trainer/Advantage Weights Mean 4.18696 +trainer/Advantage Weights Std 16.8587 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.49229e-17 +trainer/Advantage Score Mean -0.379701 +trainer/Advantage Score Std 0.606307 +trainer/Advantage Score Max 1.12985 +trainer/Advantage Score Min -3.82307 +trainer/V1 Predictions Mean -71.9511 +trainer/V1 Predictions Std 19.0576 +trainer/V1 Predictions Max 0.338007 +trainer/V1 Predictions Min -87.1361 +trainer/VF Loss 0.0692125 +expl/num steps total 202000 +expl/num paths total 214 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0576493 +expl/Actions Std 0.820238 +expl/Actions Max 2.64706 +expl/Actions Min -2.32684 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 200858 +eval/num paths total 202 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0327959 +eval/Actions Std 0.740648 +eval/Actions Max 0.999214 +eval/Actions Min -0.999137 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95369e-06 +time/evaluation sampling (s) 2.24034 +time/exploration sampling (s) 2.73772 +time/logging (s) 0.00988116 +time/saving (s) 0.015084 +time/training (s) 12.8266 +time/epoch (s) 17.8297 +time/total (s) 4211.71 +Epoch -799 +------------------------------ ---------------- +2022-05-15 19:12:55.910258 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -798 finished +------------------------------ ---------------- +epoch -798 +replay_buffer/size 999047 +trainer/num train calls 203000 +trainer/QF1 Loss 0.664036 +trainer/QF2 Loss 0.665526 +trainer/Policy Loss 15.8898 +trainer/Q1 Predictions Mean -73.2205 +trainer/Q1 Predictions Std 18.136 +trainer/Q1 Predictions Max -0.471825 +trainer/Q1 Predictions Min -87.3055 +trainer/Q2 Predictions Mean -73.1613 +trainer/Q2 Predictions Std 18.1387 +trainer/Q2 Predictions Max -0.56923 +trainer/Q2 Predictions Min -87.4159 +trainer/Q Targets Mean -73.2946 +trainer/Q Targets Std 18.0623 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.276 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00181328 +trainer/policy/mean Std 0.709946 +trainer/policy/mean Max 0.997784 +trainer/policy/mean Min -0.999172 +trainer/policy/std Mean 0.433791 +trainer/policy/std Std 0.0222317 +trainer/policy/std Max 0.457691 +trainer/policy/std Min 0.397627 +trainer/Advantage Weights Mean 4.56953 +trainer/Advantage Weights Std 17.316 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.00431e-20 +trainer/Advantage Score Mean -0.356354 +trainer/Advantage Score Std 0.582412 +trainer/Advantage Score Max 0.771187 +trainer/Advantage Score Min -4.53564 +trainer/V1 Predictions Mean -72.9823 +trainer/V1 Predictions Std 18.2201 +trainer/V1 Predictions Max -0.857594 +trainer/V1 Predictions Min -88.1356 +trainer/VF Loss 0.0601063 +expl/num steps total 203000 +expl/num paths total 215 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0552695 +expl/Actions Std 0.835682 +expl/Actions Max 2.41357 +expl/Actions Min -2.54435 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 201858 +eval/num paths total 203 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0804307 +eval/Actions Std 0.703503 +eval/Actions Max 0.998696 +eval/Actions Min -0.998714 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.90712e-06 +time/evaluation sampling (s) 2.2655 +time/exploration sampling (s) 2.66378 +time/logging (s) 0.00687957 +time/saving (s) 0.00972418 +time/training (s) 13.1875 +time/epoch (s) 18.1334 +time/total (s) 4229.85 +Epoch -798 +------------------------------ ---------------- +2022-05-15 19:13:13.843217 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -797 finished +------------------------------ ---------------- +epoch -797 +replay_buffer/size 999047 +trainer/num train calls 204000 +trainer/QF1 Loss 0.979625 +trainer/QF2 Loss 0.944424 +trainer/Policy Loss 28.3532 +trainer/Q1 Predictions Mean -69.6805 +trainer/Q1 Predictions Std 22.019 +trainer/Q1 Predictions Max -0.549008 +trainer/Q1 Predictions Min -87.0304 +trainer/Q2 Predictions Mean -69.6011 +trainer/Q2 Predictions Std 22.0502 +trainer/Q2 Predictions Max -0.48317 +trainer/Q2 Predictions Min -87.0828 +trainer/Q Targets Mean -69.6655 +trainer/Q Targets Std 22.3711 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8264 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000852968 +trainer/policy/mean Std 0.7136 +trainer/policy/mean Max 0.998571 +trainer/policy/mean Min -0.999265 +trainer/policy/std Mean 0.431912 +trainer/policy/std Std 0.023164 +trainer/policy/std Max 0.455354 +trainer/policy/std Min 0.395236 +trainer/Advantage Weights Mean 5.41703 +trainer/Advantage Weights Std 20.3507 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.86281e-14 +trainer/Advantage Score Mean -0.413208 +trainer/Advantage Score Std 0.651832 +trainer/Advantage Score Max 1.39474 +trainer/Advantage Score Min -3.11844 +trainer/V1 Predictions Mean -69.4333 +trainer/V1 Predictions Std 22.4369 +trainer/V1 Predictions Max 0.4232 +trainer/V1 Predictions Min -86.6931 +trainer/VF Loss 0.0841949 +expl/num steps total 204000 +expl/num paths total 216 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0779637 +expl/Actions Std 0.816044 +expl/Actions Max 2.41583 +expl/Actions Min -2.34688 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 202858 +eval/num paths total 204 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.06268 +eval/Actions Std 0.715678 +eval/Actions Max 0.998761 +eval/Actions Min -0.999441 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.02913e-06 +time/evaluation sampling (s) 2.26299 +time/exploration sampling (s) 2.73817 +time/logging (s) 0.00925895 +time/saving (s) 0.0134966 +time/training (s) 12.905 +time/epoch (s) 17.929 +time/total (s) 4247.78 +Epoch -797 +------------------------------ ---------------- +2022-05-15 19:13:32.368585 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -796 finished +------------------------------ ---------------- +epoch -796 +replay_buffer/size 999047 +trainer/num train calls 205000 +trainer/QF1 Loss 0.703621 +trainer/QF2 Loss 0.757855 +trainer/Policy Loss 19.2368 +trainer/Q1 Predictions Mean -72.7066 +trainer/Q1 Predictions Std 18.2899 +trainer/Q1 Predictions Max -0.938724 +trainer/Q1 Predictions Min -86.4926 +trainer/Q2 Predictions Mean -72.7034 +trainer/Q2 Predictions Std 18.3647 +trainer/Q2 Predictions Max -1.06035 +trainer/Q2 Predictions Min -86.983 +trainer/Q Targets Mean -72.968 +trainer/Q Targets Std 18.4594 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0563 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0473902 +trainer/policy/mean Std 0.719899 +trainer/policy/mean Max 0.994729 +trainer/policy/mean Min -0.998624 +trainer/policy/std Mean 0.433384 +trainer/policy/std Std 0.0252543 +trainer/policy/std Max 0.461316 +trainer/policy/std Min 0.389968 +trainer/Advantage Weights Mean 4.31289 +trainer/Advantage Weights Std 17.752 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.70434e-14 +trainer/Advantage Score Mean -0.296305 +trainer/Advantage Score Std 0.455162 +trainer/Advantage Score Max 0.808586 +trainer/Advantage Score Min -3.1703 +trainer/V1 Predictions Mean -72.7504 +trainer/V1 Predictions Std 18.4873 +trainer/V1 Predictions Max -0.0590786 +trainer/V1 Predictions Min -86.9954 +trainer/VF Loss 0.044145 +expl/num steps total 205000 +expl/num paths total 218 +expl/path length Mean 500 +expl/path length Std 286 +expl/path length Max 786 +expl/path length Min 214 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0363176 +expl/Actions Std 0.816359 +expl/Actions Max 2.61689 +expl/Actions Min -2.26456 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 203858 +eval/num paths total 205 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.232963 +eval/Actions Std 0.709042 +eval/Actions Max 0.999227 +eval/Actions Min -0.999149 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.52086e-06 +time/evaluation sampling (s) 2.45496 +time/exploration sampling (s) 2.81679 +time/logging (s) 0.0085872 +time/saving (s) 0.0128138 +time/training (s) 13.225 +time/epoch (s) 18.5182 +time/total (s) 4266.31 +Epoch -796 +------------------------------ ---------------- +2022-05-15 19:13:50.506122 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -795 finished +------------------------------ ---------------- +epoch -795 +replay_buffer/size 999047 +trainer/num train calls 206000 +trainer/QF1 Loss 1.30951 +trainer/QF2 Loss 1.29395 +trainer/Policy Loss 53.0124 +trainer/Q1 Predictions Mean -72.32 +trainer/Q1 Predictions Std 19.6012 +trainer/Q1 Predictions Max -0.795909 +trainer/Q1 Predictions Min -86.247 +trainer/Q2 Predictions Mean -72.3365 +trainer/Q2 Predictions Std 19.6066 +trainer/Q2 Predictions Max -0.519288 +trainer/Q2 Predictions Min -86.5067 +trainer/Q Targets Mean -72.8306 +trainer/Q Targets Std 19.7047 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8731 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0209288 +trainer/policy/mean Std 0.71165 +trainer/policy/mean Max 0.998761 +trainer/policy/mean Min -0.997702 +trainer/policy/std Mean 0.432335 +trainer/policy/std Std 0.0231278 +trainer/policy/std Max 0.4574 +trainer/policy/std Min 0.392279 +trainer/Advantage Weights Mean 11.8977 +trainer/Advantage Weights Std 27.6071 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.74534e-14 +trainer/Advantage Score Mean -0.141916 +trainer/Advantage Score Std 0.567661 +trainer/Advantage Score Max 1.09421 +trainer/Advantage Score Min -3.03273 +trainer/V1 Predictions Mean -72.5663 +trainer/V1 Predictions Std 19.8332 +trainer/V1 Predictions Max -0.583566 +trainer/V1 Predictions Min -86.7624 +trainer/VF Loss 0.0721917 +expl/num steps total 206000 +expl/num paths total 219 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0305565 +expl/Actions Std 0.842884 +expl/Actions Max 2.14649 +expl/Actions Min -2.35527 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 204858 +eval/num paths total 206 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.042036 +eval/Actions Std 0.735528 +eval/Actions Max 0.99869 +eval/Actions Min -0.999405 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.69409e-06 +time/evaluation sampling (s) 2.35938 +time/exploration sampling (s) 2.94105 +time/logging (s) 0.0102953 +time/saving (s) 0.0157828 +time/training (s) 12.8056 +time/epoch (s) 18.1321 +time/total (s) 4284.44 +Epoch -795 +------------------------------ ---------------- +2022-05-15 19:14:08.606524 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -794 finished +------------------------------ ---------------- +epoch -794 +replay_buffer/size 999047 +trainer/num train calls 207000 +trainer/QF1 Loss 1.02911 +trainer/QF2 Loss 1.07537 +trainer/Policy Loss 8.13628 +trainer/Q1 Predictions Mean -73.7983 +trainer/Q1 Predictions Std 15.2991 +trainer/Q1 Predictions Max -0.372396 +trainer/Q1 Predictions Min -86.5739 +trainer/Q2 Predictions Mean -73.8044 +trainer/Q2 Predictions Std 15.3019 +trainer/Q2 Predictions Max -0.364096 +trainer/Q2 Predictions Min -86.5335 +trainer/Q Targets Mean -74.5319 +trainer/Q Targets Std 15.3143 +trainer/Q Targets Max -0.327061 +trainer/Q Targets Min -87.0766 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0102237 +trainer/policy/mean Std 0.703341 +trainer/policy/mean Max 0.999906 +trainer/policy/mean Min -0.996004 +trainer/policy/std Mean 0.432601 +trainer/policy/std Std 0.0228982 +trainer/policy/std Max 0.459269 +trainer/policy/std Min 0.393127 +trainer/Advantage Weights Mean 2.18973 +trainer/Advantage Weights Std 11.8251 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.49728e-12 +trainer/Advantage Score Mean -0.315174 +trainer/Advantage Score Std 0.397689 +trainer/Advantage Score Max 1.00604 +trainer/Advantage Score Min -2.61275 +trainer/V1 Predictions Mean -74.2411 +trainer/V1 Predictions Std 15.5198 +trainer/V1 Predictions Max -0.437707 +trainer/V1 Predictions Min -86.8601 +trainer/VF Loss 0.0331156 +expl/num steps total 207000 +expl/num paths total 221 +expl/path length Mean 500 +expl/path length Std 185 +expl/path length Max 685 +expl/path length Min 315 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0296126 +expl/Actions Std 0.79986 +expl/Actions Max 2.18976 +expl/Actions Min -2.4509 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 205858 +eval/num paths total 207 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0469036 +eval/Actions Std 0.734146 +eval/Actions Max 0.999337 +eval/Actions Min -0.999286 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.95304e-06 +time/evaluation sampling (s) 2.3693 +time/exploration sampling (s) 2.84009 +time/logging (s) 0.00855669 +time/saving (s) 0.0150477 +time/training (s) 12.8545 +time/epoch (s) 18.0875 +time/total (s) 4302.54 +Epoch -794 +------------------------------ ---------------- +2022-05-15 19:14:26.940566 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -793 finished +------------------------------ ---------------- +epoch -793 +replay_buffer/size 999047 +trainer/num train calls 208000 +trainer/QF1 Loss 1.19526 +trainer/QF2 Loss 1.18634 +trainer/Policy Loss 6.12335 +trainer/Q1 Predictions Mean -76.2326 +trainer/Q1 Predictions Std 12.8214 +trainer/Q1 Predictions Max -8.63924 +trainer/Q1 Predictions Min -86.6018 +trainer/Q2 Predictions Mean -76.3218 +trainer/Q2 Predictions Std 12.8378 +trainer/Q2 Predictions Max -9.26608 +trainer/Q2 Predictions Min -86.7291 +trainer/Q Targets Mean -75.9713 +trainer/Q Targets Std 13.105 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3416 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0259729 +trainer/policy/mean Std 0.718728 +trainer/policy/mean Max 0.999673 +trainer/policy/mean Min -0.995945 +trainer/policy/std Mean 0.433818 +trainer/policy/std Std 0.0225698 +trainer/policy/std Max 0.458953 +trainer/policy/std Min 0.398511 +trainer/Advantage Weights Mean 1.12096 +trainer/Advantage Weights Std 7.07115 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.28818e-23 +trainer/Advantage Score Mean -0.554268 +trainer/Advantage Score Std 0.620199 +trainer/Advantage Score Max 0.491837 +trainer/Advantage Score Min -5.1294 +trainer/V1 Predictions Mean -75.7404 +trainer/V1 Predictions Std 13.0775 +trainer/V1 Predictions Max -6.16681 +trainer/V1 Predictions Min -86.1772 +trainer/VF Loss 0.0721049 +expl/num steps total 208000 +expl/num paths total 222 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.12696 +expl/Actions Std 0.807801 +expl/Actions Max 2.37911 +expl/Actions Min -2.09404 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 206280 +eval/num paths total 208 +eval/path length Mean 422 +eval/path length Std 0 +eval/path length Max 422 +eval/path length Min 422 +eval/Rewards Mean 0.00236967 +eval/Rewards Std 0.0486215 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.018073 +eval/Actions Std 0.71072 +eval/Actions Max 0.996722 +eval/Actions Min -0.999071 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.95183e-06 +time/evaluation sampling (s) 2.29863 +time/exploration sampling (s) 2.93274 +time/logging (s) 0.00730086 +time/saving (s) 0.0119021 +time/training (s) 13.073 +time/epoch (s) 18.3236 +time/total (s) 4320.87 +Epoch -793 +------------------------------ ---------------- +2022-05-15 19:14:44.894951 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -792 finished +------------------------------ ---------------- +epoch -792 +replay_buffer/size 999047 +trainer/num train calls 209000 +trainer/QF1 Loss 0.611295 +trainer/QF2 Loss 0.637846 +trainer/Policy Loss 20.3104 +trainer/Q1 Predictions Mean -74.8805 +trainer/Q1 Predictions Std 16.4962 +trainer/Q1 Predictions Max -0.71168 +trainer/Q1 Predictions Min -87.1644 +trainer/Q2 Predictions Mean -74.8657 +trainer/Q2 Predictions Std 16.4511 +trainer/Q2 Predictions Max -1.06151 +trainer/Q2 Predictions Min -87.1327 +trainer/Q Targets Mean -74.6697 +trainer/Q Targets Std 16.4231 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0668 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0119118 +trainer/policy/mean Std 0.700523 +trainer/policy/mean Max 0.99844 +trainer/policy/mean Min -0.998928 +trainer/policy/std Mean 0.432698 +trainer/policy/std Std 0.0213526 +trainer/policy/std Max 0.457185 +trainer/policy/std Min 0.39971 +trainer/Advantage Weights Mean 4.16769 +trainer/Advantage Weights Std 18.5371 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.3766e-13 +trainer/Advantage Score Mean -0.387168 +trainer/Advantage Score Std 0.494529 +trainer/Advantage Score Max 1.41744 +trainer/Advantage Score Min -2.87167 +trainer/V1 Predictions Mean -74.4793 +trainer/V1 Predictions Std 16.4975 +trainer/V1 Predictions Max -0.022918 +trainer/V1 Predictions Min -86.9355 +trainer/VF Loss 0.065909 +expl/num steps total 209000 +expl/num paths total 224 +expl/path length Mean 500 +expl/path length Std 136 +expl/path length Max 636 +expl/path length Min 364 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0156379 +expl/Actions Std 0.819069 +expl/Actions Max 2.51523 +expl/Actions Min -2.6078 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 207132 +eval/num paths total 209 +eval/path length Mean 852 +eval/path length Std 0 +eval/path length Max 852 +eval/path length Min 852 +eval/Rewards Mean 0.00117371 +eval/Rewards Std 0.0342393 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.042245 +eval/Actions Std 0.727074 +eval/Actions Max 0.998901 +eval/Actions Min -0.998982 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.20235e-06 +time/evaluation sampling (s) 2.31148 +time/exploration sampling (s) 2.91415 +time/logging (s) 0.00720631 +time/saving (s) 0.018253 +time/training (s) 12.6973 +time/epoch (s) 17.9484 +time/total (s) 4338.82 +Epoch -792 +------------------------------ ---------------- +2022-05-15 19:15:03.272962 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -791 finished +------------------------------ ---------------- +epoch -791 +replay_buffer/size 999047 +trainer/num train calls 210000 +trainer/QF1 Loss 0.971878 +trainer/QF2 Loss 0.99862 +trainer/Policy Loss 4.21487 +trainer/Q1 Predictions Mean -73.5662 +trainer/Q1 Predictions Std 17.9899 +trainer/Q1 Predictions Max -1.05192 +trainer/Q1 Predictions Min -86.5907 +trainer/Q2 Predictions Mean -73.5248 +trainer/Q2 Predictions Std 17.9614 +trainer/Q2 Predictions Max -1.57509 +trainer/Q2 Predictions Min -86.3119 +trainer/Q Targets Mean -73.511 +trainer/Q Targets Std 17.8916 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1764 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00294914 +trainer/policy/mean Std 0.693051 +trainer/policy/mean Max 0.997421 +trainer/policy/mean Min -0.994728 +trainer/policy/std Mean 0.435805 +trainer/policy/std Std 0.0206381 +trainer/policy/std Max 0.460136 +trainer/policy/std Min 0.403197 +trainer/Advantage Weights Mean 1.15281 +trainer/Advantage Weights Std 5.51437 +trainer/Advantage Weights Max 60.6517 +trainer/Advantage Weights Min 5.2315e-14 +trainer/Advantage Score Mean -0.523879 +trainer/Advantage Score Std 0.481245 +trainer/Advantage Score Max 0.410515 +trainer/Advantage Score Min -3.05815 +trainer/V1 Predictions Mean -73.3119 +trainer/V1 Predictions Std 17.8478 +trainer/V1 Predictions Max -2.16178 +trainer/V1 Predictions Min -86.0425 +trainer/VF Loss 0.0539695 +expl/num steps total 210000 +expl/num paths total 226 +expl/path length Mean 500 +expl/path length Std 400 +expl/path length Max 900 +expl/path length Min 100 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0505487 +expl/Actions Std 0.82433 +expl/Actions Max 2.47752 +expl/Actions Min -2.30577 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 208132 +eval/num paths total 210 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.228042 +eval/Actions Std 0.670253 +eval/Actions Max 0.999224 +eval/Actions Min -0.998898 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.89315e-06 +time/evaluation sampling (s) 2.46558 +time/exploration sampling (s) 2.93819 +time/logging (s) 0.0080759 +time/saving (s) 0.0131696 +time/training (s) 12.9461 +time/epoch (s) 18.3711 +time/total (s) 4357.19 +Epoch -791 +------------------------------ ---------------- +2022-05-15 19:15:22.149120 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -790 finished +------------------------------ ---------------- +epoch -790 +replay_buffer/size 999047 +trainer/num train calls 211000 +trainer/QF1 Loss 1.6778 +trainer/QF2 Loss 1.71785 +trainer/Policy Loss 11.6862 +trainer/Q1 Predictions Mean -72.5935 +trainer/Q1 Predictions Std 18.8626 +trainer/Q1 Predictions Max -1.06527 +trainer/Q1 Predictions Min -86.8283 +trainer/Q2 Predictions Mean -72.5941 +trainer/Q2 Predictions Std 18.8399 +trainer/Q2 Predictions Max -0.930433 +trainer/Q2 Predictions Min -86.7246 +trainer/Q Targets Mean -72.51 +trainer/Q Targets Std 19.1878 +trainer/Q Targets Max 0.341153 +trainer/Q Targets Min -86.3868 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0151664 +trainer/policy/mean Std 0.703582 +trainer/policy/mean Max 0.998042 +trainer/policy/mean Min -0.999712 +trainer/policy/std Mean 0.436121 +trainer/policy/std Std 0.0220614 +trainer/policy/std Max 0.463524 +trainer/policy/std Min 0.399971 +trainer/Advantage Weights Mean 3.49283 +trainer/Advantage Weights Std 16.179 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.39887e-13 +trainer/Advantage Score Mean -0.39643 +trainer/Advantage Score Std 0.537938 +trainer/Advantage Score Max 1.21329 +trainer/Advantage Score Min -2.90586 +trainer/V1 Predictions Mean -72.2627 +trainer/V1 Predictions Std 19.1031 +trainer/V1 Predictions Max -0.912317 +trainer/V1 Predictions Min -86.2584 +trainer/VF Loss 0.0573853 +expl/num steps total 211000 +expl/num paths total 227 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.288586 +expl/Actions Std 0.849373 +expl/Actions Max 2.33149 +expl/Actions Min -2.61502 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 209132 +eval/num paths total 211 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.166207 +eval/Actions Std 0.695846 +eval/Actions Max 0.998736 +eval/Actions Min -0.999274 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.963e-06 +time/evaluation sampling (s) 2.53063 +time/exploration sampling (s) 3.0012 +time/logging (s) 0.00733324 +time/saving (s) 0.0118134 +time/training (s) 13.3166 +time/epoch (s) 18.8675 +time/total (s) 4376.07 +Epoch -790 +------------------------------ ---------------- +2022-05-15 19:15:40.446026 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -789 finished +------------------------------ ---------------- +epoch -789 +replay_buffer/size 999047 +trainer/num train calls 212000 +trainer/QF1 Loss 0.547933 +trainer/QF2 Loss 0.546454 +trainer/Policy Loss 17.6418 +trainer/Q1 Predictions Mean -73.0639 +trainer/Q1 Predictions Std 17.7806 +trainer/Q1 Predictions Max -0.497456 +trainer/Q1 Predictions Min -87.2949 +trainer/Q2 Predictions Mean -73.1195 +trainer/Q2 Predictions Std 17.7842 +trainer/Q2 Predictions Max -0.859395 +trainer/Q2 Predictions Min -87.0137 +trainer/Q Targets Mean -73.3122 +trainer/Q Targets Std 17.9857 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2117 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00826876 +trainer/policy/mean Std 0.715675 +trainer/policy/mean Max 0.998923 +trainer/policy/mean Min -0.994667 +trainer/policy/std Mean 0.435982 +trainer/policy/std Std 0.0210712 +trainer/policy/std Max 0.460622 +trainer/policy/std Min 0.40292 +trainer/Advantage Weights Mean 4.07036 +trainer/Advantage Weights Std 16.6062 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.33372e-19 +trainer/Advantage Score Mean -0.37225 +trainer/Advantage Score Std 0.60434 +trainer/Advantage Score Max 0.740616 +trainer/Advantage Score Min -4.19032 +trainer/V1 Predictions Mean -73.1324 +trainer/V1 Predictions Std 17.9751 +trainer/V1 Predictions Max -0.297414 +trainer/V1 Predictions Min -87.1631 +trainer/VF Loss 0.0616923 +expl/num steps total 212000 +expl/num paths total 228 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0284244 +expl/Actions Std 0.813778 +expl/Actions Max 2.31094 +expl/Actions Min -2.33894 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 210132 +eval/num paths total 212 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.251302 +eval/Actions Std 0.698101 +eval/Actions Max 0.999421 +eval/Actions Min -0.999498 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99467e-06 +time/evaluation sampling (s) 2.54059 +time/exploration sampling (s) 2.96621 +time/logging (s) 0.00754502 +time/saving (s) 0.0145283 +time/training (s) 12.7611 +time/epoch (s) 18.29 +time/total (s) 4394.36 +Epoch -789 +------------------------------ ---------------- +2022-05-15 19:15:58.548075 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -788 finished +------------------------------ ---------------- +epoch -788 +replay_buffer/size 999047 +trainer/num train calls 213000 +trainer/QF1 Loss 0.970291 +trainer/QF2 Loss 0.874179 +trainer/Policy Loss 22.4493 +trainer/Q1 Predictions Mean -74.0829 +trainer/Q1 Predictions Std 17.4366 +trainer/Q1 Predictions Max -1.55317 +trainer/Q1 Predictions Min -87.66 +trainer/Q2 Predictions Mean -74.1221 +trainer/Q2 Predictions Std 17.402 +trainer/Q2 Predictions Max -1.75357 +trainer/Q2 Predictions Min -87.4181 +trainer/Q Targets Mean -74.1766 +trainer/Q Targets Std 17.3219 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1677 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0176618 +trainer/policy/mean Std 0.716943 +trainer/policy/mean Max 0.998796 +trainer/policy/mean Min -0.999087 +trainer/policy/std Mean 0.435981 +trainer/policy/std Std 0.0201121 +trainer/policy/std Max 0.458755 +trainer/policy/std Min 0.404324 +trainer/Advantage Weights Mean 8.03349 +trainer/Advantage Weights Std 24.9806 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.43628e-11 +trainer/Advantage Score Mean -0.209944 +trainer/Advantage Score Std 0.488494 +trainer/Advantage Score Max 1.29178 +trainer/Advantage Score Min -2.4094 +trainer/V1 Predictions Mean -73.9274 +trainer/V1 Predictions Std 17.3716 +trainer/V1 Predictions Max -2.73948 +trainer/V1 Predictions Min -87.5924 +trainer/VF Loss 0.0727913 +expl/num steps total 213000 +expl/num paths total 229 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0352629 +expl/Actions Std 0.864581 +expl/Actions Max 2.36978 +expl/Actions Min -2.21084 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 211132 +eval/num paths total 213 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.356944 +eval/Actions Std 0.691997 +eval/Actions Max 0.998232 +eval/Actions Min -0.998628 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.54996e-06 +time/evaluation sampling (s) 2.61063 +time/exploration sampling (s) 2.90631 +time/logging (s) 0.00762822 +time/saving (s) 0.0103773 +time/training (s) 12.5605 +time/epoch (s) 18.0954 +time/total (s) 4412.46 +Epoch -788 +------------------------------ ---------------- +2022-05-15 19:16:16.597464 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -787 finished +------------------------------ ---------------- +epoch -787 +replay_buffer/size 999047 +trainer/num train calls 214000 +trainer/QF1 Loss 0.960331 +trainer/QF2 Loss 0.900604 +trainer/Policy Loss 64.1355 +trainer/Q1 Predictions Mean -72.2623 +trainer/Q1 Predictions Std 19.1751 +trainer/Q1 Predictions Max -1.41864 +trainer/Q1 Predictions Min -86.8132 +trainer/Q2 Predictions Mean -72.3577 +trainer/Q2 Predictions Std 19.1608 +trainer/Q2 Predictions Max -1.5408 +trainer/Q2 Predictions Min -87.0815 +trainer/Q Targets Mean -72.6629 +trainer/Q Targets Std 19.0626 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7949 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.033124 +trainer/policy/mean Std 0.710797 +trainer/policy/mean Max 0.995551 +trainer/policy/mean Min -0.99809 +trainer/policy/std Mean 0.434306 +trainer/policy/std Std 0.0208156 +trainer/policy/std Max 0.457702 +trainer/policy/std Min 0.401399 +trainer/Advantage Weights Mean 12.8854 +trainer/Advantage Weights Std 26.9829 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.19141e-12 +trainer/Advantage Score Mean -0.103696 +trainer/Advantage Score Std 0.538304 +trainer/Advantage Score Max 1.18356 +trainer/Advantage Score Min -2.68465 +trainer/V1 Predictions Mean -72.3893 +trainer/V1 Predictions Std 19.1075 +trainer/V1 Predictions Max -1.52025 +trainer/V1 Predictions Min -86.3591 +trainer/VF Loss 0.0777375 +expl/num steps total 214000 +expl/num paths total 230 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0254524 +expl/Actions Std 0.806205 +expl/Actions Max 2.35797 +expl/Actions Min -2.26151 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 211813 +eval/num paths total 214 +eval/path length Mean 681 +eval/path length Std 0 +eval/path length Max 681 +eval/path length Min 681 +eval/Rewards Mean 0.00146843 +eval/Rewards Std 0.0382919 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0279281 +eval/Actions Std 0.730254 +eval/Actions Max 0.999122 +eval/Actions Min -0.998773 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.82796e-06 +time/evaluation sampling (s) 2.4999 +time/exploration sampling (s) 2.92764 +time/logging (s) 0.00616817 +time/saving (s) 0.00975448 +time/training (s) 12.5982 +time/epoch (s) 18.0417 +time/total (s) 4430.51 +Epoch -787 +------------------------------ ---------------- +2022-05-15 19:16:35.291609 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -786 finished +------------------------------ ---------------- +epoch -786 +replay_buffer/size 999047 +trainer/num train calls 215000 +trainer/QF1 Loss 0.601459 +trainer/QF2 Loss 0.654405 +trainer/Policy Loss 21.0403 +trainer/Q1 Predictions Mean -71.9859 +trainer/Q1 Predictions Std 19.7262 +trainer/Q1 Predictions Max -0.974668 +trainer/Q1 Predictions Min -87.2306 +trainer/Q2 Predictions Mean -71.8634 +trainer/Q2 Predictions Std 19.7058 +trainer/Q2 Predictions Max -0.609828 +trainer/Q2 Predictions Min -87.4374 +trainer/Q Targets Mean -71.8523 +trainer/Q Targets Std 19.6218 +trainer/Q Targets Max -0.413379 +trainer/Q Targets Min -87.2414 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.012545 +trainer/policy/mean Std 0.707208 +trainer/policy/mean Max 0.999578 +trainer/policy/mean Min -0.997291 +trainer/policy/std Mean 0.433484 +trainer/policy/std Std 0.02078 +trainer/policy/std Max 0.460793 +trainer/policy/std Min 0.398587 +trainer/Advantage Weights Mean 2.87032 +trainer/Advantage Weights Std 14.6069 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.76505e-11 +trainer/Advantage Score Mean -0.484916 +trainer/Advantage Score Std 0.465951 +trainer/Advantage Score Max 0.709518 +trainer/Advantage Score Min -2.35766 +trainer/V1 Predictions Mean -71.5883 +trainer/V1 Predictions Std 19.7966 +trainer/V1 Predictions Max -0.257904 +trainer/V1 Predictions Min -87.1119 +trainer/VF Loss 0.0532812 +expl/num steps total 215000 +expl/num paths total 232 +expl/path length Mean 500 +expl/path length Std 303 +expl/path length Max 803 +expl/path length Min 197 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0543653 +expl/Actions Std 0.835415 +expl/Actions Max 2.32321 +expl/Actions Min -2.32789 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 212813 +eval/num paths total 215 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0153933 +eval/Actions Std 0.71592 +eval/Actions Max 0.99887 +eval/Actions Min -0.999016 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99281e-06 +time/evaluation sampling (s) 2.56788 +time/exploration sampling (s) 2.9014 +time/logging (s) 0.00814319 +time/saving (s) 0.0130825 +time/training (s) 13.1988 +time/epoch (s) 18.6894 +time/total (s) 4449.2 +Epoch -786 +------------------------------ ---------------- +2022-05-15 19:16:53.593453 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -785 finished +------------------------------ ---------------- +epoch -785 +replay_buffer/size 999047 +trainer/num train calls 216000 +trainer/QF1 Loss 0.555059 +trainer/QF2 Loss 0.598243 +trainer/Policy Loss 23.6398 +trainer/Q1 Predictions Mean -72.5824 +trainer/Q1 Predictions Std 18.8856 +trainer/Q1 Predictions Max -0.820591 +trainer/Q1 Predictions Min -86.4031 +trainer/Q2 Predictions Mean -72.5761 +trainer/Q2 Predictions Std 18.9403 +trainer/Q2 Predictions Max -0.660116 +trainer/Q2 Predictions Min -86.5048 +trainer/Q Targets Mean -72.8658 +trainer/Q Targets Std 18.748 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6398 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0185545 +trainer/policy/mean Std 0.701661 +trainer/policy/mean Max 0.997548 +trainer/policy/mean Min -0.999275 +trainer/policy/std Mean 0.43542 +trainer/policy/std Std 0.0215862 +trainer/policy/std Max 0.463958 +trainer/policy/std Min 0.400061 +trainer/Advantage Weights Mean 4.96042 +trainer/Advantage Weights Std 19.1379 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.15738e-13 +trainer/Advantage Score Mean -0.288949 +trainer/Advantage Score Std 0.443033 +trainer/Advantage Score Max 1.30665 +trainer/Advantage Score Min -2.85087 +trainer/V1 Predictions Mean -72.5713 +trainer/V1 Predictions Std 19.0038 +trainer/V1 Predictions Max -0.217974 +trainer/V1 Predictions Min -86.5025 +trainer/VF Loss 0.0503679 +expl/num steps total 216000 +expl/num paths total 233 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0380031 +expl/Actions Std 0.795008 +expl/Actions Max 2.3228 +expl/Actions Min -2.39654 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 213813 +eval/num paths total 216 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0210368 +eval/Actions Std 0.701427 +eval/Actions Max 0.998928 +eval/Actions Min -0.997926 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.03099e-06 +time/evaluation sampling (s) 2.52521 +time/exploration sampling (s) 2.9432 +time/logging (s) 0.00663354 +time/saving (s) 0.00947291 +time/training (s) 12.8079 +time/epoch (s) 18.2924 +time/total (s) 4467.5 +Epoch -785 +------------------------------ ---------------- +2022-05-15 19:17:11.838829 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -784 finished +------------------------------ ---------------- +epoch -784 +replay_buffer/size 999047 +trainer/num train calls 217000 +trainer/QF1 Loss 1.43428 +trainer/QF2 Loss 1.43845 +trainer/Policy Loss 5.10808 +trainer/Q1 Predictions Mean -74.0637 +trainer/Q1 Predictions Std 17.275 +trainer/Q1 Predictions Max -2.27771 +trainer/Q1 Predictions Min -86.6069 +trainer/Q2 Predictions Mean -73.9675 +trainer/Q2 Predictions Std 17.319 +trainer/Q2 Predictions Max -2.18013 +trainer/Q2 Predictions Min -86.6267 +trainer/Q Targets Mean -73.7071 +trainer/Q Targets Std 17.8692 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2401 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0121567 +trainer/policy/mean Std 0.709697 +trainer/policy/mean Max 0.999495 +trainer/policy/mean Min -0.998091 +trainer/policy/std Mean 0.434496 +trainer/policy/std Std 0.0213062 +trainer/policy/std Max 0.459849 +trainer/policy/std Min 0.399419 +trainer/Advantage Weights Mean 1.31415 +trainer/Advantage Weights Std 9.16002 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.34726e-16 +trainer/Advantage Score Mean -0.480878 +trainer/Advantage Score Std 0.492492 +trainer/Advantage Score Max 0.847838 +trainer/Advantage Score Min -3.56332 +trainer/V1 Predictions Mean -73.574 +trainer/V1 Predictions Std 17.6901 +trainer/V1 Predictions Max -1.29505 +trainer/V1 Predictions Min -86.2864 +trainer/VF Loss 0.0533343 +expl/num steps total 217000 +expl/num paths total 234 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0119356 +expl/Actions Std 0.810357 +expl/Actions Max 2.47203 +expl/Actions Min -2.27271 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 214813 +eval/num paths total 217 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.157712 +eval/Actions Std 0.754429 +eval/Actions Max 0.996253 +eval/Actions Min -0.997014 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18605e-06 +time/evaluation sampling (s) 2.59705 +time/exploration sampling (s) 2.9705 +time/logging (s) 0.0085494 +time/saving (s) 0.0137467 +time/training (s) 12.6514 +time/epoch (s) 18.2413 +time/total (s) 4485.74 +Epoch -784 +------------------------------ ---------------- +2022-05-15 19:17:30.007702 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -783 finished +------------------------------ ---------------- +epoch -783 +replay_buffer/size 999047 +trainer/num train calls 218000 +trainer/QF1 Loss 0.581084 +trainer/QF2 Loss 0.556342 +trainer/Policy Loss 16.6448 +trainer/Q1 Predictions Mean -75.7537 +trainer/Q1 Predictions Std 13.7493 +trainer/Q1 Predictions Max -13.7128 +trainer/Q1 Predictions Min -87.52 +trainer/Q2 Predictions Mean -75.7253 +trainer/Q2 Predictions Std 13.7564 +trainer/Q2 Predictions Max -15.0261 +trainer/Q2 Predictions Min -86.9628 +trainer/Q Targets Mean -75.5733 +trainer/Q Targets Std 13.7038 +trainer/Q Targets Max -15.9912 +trainer/Q Targets Min -86.8282 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0137171 +trainer/policy/mean Std 0.69873 +trainer/policy/mean Max 0.998019 +trainer/policy/mean Min -0.998867 +trainer/policy/std Mean 0.43392 +trainer/policy/std Std 0.0214361 +trainer/policy/std Max 0.460743 +trainer/policy/std Min 0.398855 +trainer/Advantage Weights Mean 3.49395 +trainer/Advantage Weights Std 15.9078 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.54801e-15 +trainer/Advantage Score Mean -0.350396 +trainer/Advantage Score Std 0.489958 +trainer/Advantage Score Max 0.962494 +trainer/Advantage Score Min -3.28253 +trainer/V1 Predictions Mean -75.2762 +trainer/V1 Predictions Std 13.8451 +trainer/V1 Predictions Max -13.7236 +trainer/V1 Predictions Min -86.6517 +trainer/VF Loss 0.0484397 +expl/num steps total 218000 +expl/num paths total 235 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00763706 +expl/Actions Std 0.815138 +expl/Actions Max 2.39432 +expl/Actions Min -2.54421 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 215813 +eval/num paths total 218 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.167214 +eval/Actions Std 0.646992 +eval/Actions Max 0.995668 +eval/Actions Min -0.998289 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.9807e-06 +time/evaluation sampling (s) 2.55537 +time/exploration sampling (s) 3.00234 +time/logging (s) 0.00875173 +time/saving (s) 0.0130688 +time/training (s) 12.5807 +time/epoch (s) 18.1602 +time/total (s) 4503.91 +Epoch -783 +------------------------------ ---------------- +2022-05-15 19:17:47.545870 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -782 finished +------------------------------ ---------------- +epoch -782 +replay_buffer/size 999047 +trainer/num train calls 219000 +trainer/QF1 Loss 0.813709 +trainer/QF2 Loss 0.887906 +trainer/Policy Loss 20.127 +trainer/Q1 Predictions Mean -73.2519 +trainer/Q1 Predictions Std 17.9877 +trainer/Q1 Predictions Max -2.44102 +trainer/Q1 Predictions Min -86.9548 +trainer/Q2 Predictions Mean -73.2239 +trainer/Q2 Predictions Std 17.9891 +trainer/Q2 Predictions Max -1.86633 +trainer/Q2 Predictions Min -86.5154 +trainer/Q Targets Mean -73.5352 +trainer/Q Targets Std 18.109 +trainer/Q Targets Max -2.58581 +trainer/Q Targets Min -86.6967 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00256758 +trainer/policy/mean Std 0.704341 +trainer/policy/mean Max 0.997306 +trainer/policy/mean Min -0.998382 +trainer/policy/std Mean 0.433667 +trainer/policy/std Std 0.0220625 +trainer/policy/std Max 0.460941 +trainer/policy/std Min 0.399129 +trainer/Advantage Weights Mean 4.99096 +trainer/Advantage Weights Std 16.5202 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.09296e-20 +trainer/Advantage Score Mean -0.240975 +trainer/Advantage Score Std 0.539855 +trainer/Advantage Score Max 1.21496 +trainer/Advantage Score Min -4.59628 +trainer/V1 Predictions Mean -73.2924 +trainer/V1 Predictions Std 18.2588 +trainer/V1 Predictions Max -0.289689 +trainer/V1 Predictions Min -86.522 +trainer/VF Loss 0.0541559 +expl/num steps total 219000 +expl/num paths total 236 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0824363 +expl/Actions Std 0.84427 +expl/Actions Max 2.90851 +expl/Actions Min -2.2889 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 216813 +eval/num paths total 219 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0461187 +eval/Actions Std 0.728783 +eval/Actions Max 0.998728 +eval/Actions Min -0.999017 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.963e-06 +time/evaluation sampling (s) 2.55979 +time/exploration sampling (s) 2.98151 +time/logging (s) 0.00666757 +time/saving (s) 0.00965416 +time/training (s) 11.9701 +time/epoch (s) 17.5277 +time/total (s) 4521.44 +Epoch -782 +------------------------------ ---------------- +2022-05-15 19:18:05.327079 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -781 finished +------------------------------ ---------------- +epoch -781 +replay_buffer/size 999047 +trainer/num train calls 220000 +trainer/QF1 Loss 2.43523 +trainer/QF2 Loss 2.60129 +trainer/Policy Loss 54.2306 +trainer/Q1 Predictions Mean -73.3995 +trainer/Q1 Predictions Std 18.2265 +trainer/Q1 Predictions Max -0.579153 +trainer/Q1 Predictions Min -88.1619 +trainer/Q2 Predictions Mean -73.3182 +trainer/Q2 Predictions Std 18.221 +trainer/Q2 Predictions Max -0.639826 +trainer/Q2 Predictions Min -88.0122 +trainer/Q Targets Mean -73.6635 +trainer/Q Targets Std 18.3472 +trainer/Q Targets Max 0.00233436 +trainer/Q Targets Min -88.5163 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0510602 +trainer/policy/mean Std 0.705839 +trainer/policy/mean Max 0.996895 +trainer/policy/mean Min -0.997837 +trainer/policy/std Mean 0.433944 +trainer/policy/std Std 0.0217652 +trainer/policy/std Max 0.459948 +trainer/policy/std Min 0.396967 +trainer/Advantage Weights Mean 12.0735 +trainer/Advantage Weights Std 28.0161 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.07552e-14 +trainer/Advantage Score Mean -0.115729 +trainer/Advantage Score Std 0.563065 +trainer/Advantage Score Max 1.67595 +trainer/Advantage Score Min -3.08312 +trainer/V1 Predictions Mean -73.4483 +trainer/V1 Predictions Std 18.4955 +trainer/V1 Predictions Max 2.06951 +trainer/V1 Predictions Min -88.3015 +trainer/VF Loss 0.0905546 +expl/num steps total 220000 +expl/num paths total 237 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0506208 +expl/Actions Std 0.751219 +expl/Actions Max 2.28613 +expl/Actions Min -2.61567 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 217813 +eval/num paths total 220 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.236254 +eval/Actions Std 0.634602 +eval/Actions Max 0.996107 +eval/Actions Min -0.995402 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.97977e-06 +time/evaluation sampling (s) 2.62181 +time/exploration sampling (s) 2.93863 +time/logging (s) 0.00893785 +time/saving (s) 0.0128364 +time/training (s) 12.1951 +time/epoch (s) 17.7773 +time/total (s) 4539.22 +Epoch -781 +------------------------------ ---------------- +2022-05-15 19:18:23.089413 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -780 finished +------------------------------ ---------------- +epoch -780 +replay_buffer/size 999047 +trainer/num train calls 221000 +trainer/QF1 Loss 0.584186 +trainer/QF2 Loss 0.558223 +trainer/Policy Loss 5.38011 +trainer/Q1 Predictions Mean -73.6599 +trainer/Q1 Predictions Std 18.3102 +trainer/Q1 Predictions Max -4.01128 +trainer/Q1 Predictions Min -87.2375 +trainer/Q2 Predictions Mean -73.6945 +trainer/Q2 Predictions Std 18.2956 +trainer/Q2 Predictions Max -4.00947 +trainer/Q2 Predictions Min -87.572 +trainer/Q Targets Mean -73.3031 +trainer/Q Targets Std 18.2886 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9082 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0279058 +trainer/policy/mean Std 0.711499 +trainer/policy/mean Max 0.999096 +trainer/policy/mean Min -0.994871 +trainer/policy/std Mean 0.432442 +trainer/policy/std Std 0.0219261 +trainer/policy/std Max 0.460395 +trainer/policy/std Min 0.395101 +trainer/Advantage Weights Mean 1.32709 +trainer/Advantage Weights Std 10.1866 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.38141e-09 +trainer/Advantage Score Mean -0.540246 +trainer/Advantage Score Std 0.434084 +trainer/Advantage Score Max 2.43378 +trainer/Advantage Score Min -2.04002 +trainer/V1 Predictions Mean -73.1089 +trainer/V1 Predictions Std 18.1875 +trainer/V1 Predictions Max -3.8149 +trainer/V1 Predictions Min -86.7265 +trainer/VF Loss 0.0697673 +expl/num steps total 221000 +expl/num paths total 238 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.113185 +expl/Actions Std 0.859713 +expl/Actions Max 2.17558 +expl/Actions Min -2.49902 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 218813 +eval/num paths total 221 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.035689 +eval/Actions Std 0.732633 +eval/Actions Max 0.998386 +eval/Actions Min -0.998843 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.02913e-06 +time/evaluation sampling (s) 2.48455 +time/exploration sampling (s) 2.95184 +time/logging (s) 0.00718738 +time/saving (s) 0.0104234 +time/training (s) 12.2971 +time/epoch (s) 17.7511 +time/total (s) 4556.98 +Epoch -780 +------------------------------ ---------------- +2022-05-15 19:18:40.706930 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -779 finished +------------------------------ ---------------- +epoch -779 +replay_buffer/size 999047 +trainer/num train calls 222000 +trainer/QF1 Loss 0.633011 +trainer/QF2 Loss 0.642969 +trainer/Policy Loss 17.451 +trainer/Q1 Predictions Mean -73.699 +trainer/Q1 Predictions Std 18.4761 +trainer/Q1 Predictions Max -0.416219 +trainer/Q1 Predictions Min -86.7315 +trainer/Q2 Predictions Mean -73.6773 +trainer/Q2 Predictions Std 18.4456 +trainer/Q2 Predictions Max -0.372009 +trainer/Q2 Predictions Min -86.7033 +trainer/Q Targets Mean -73.3573 +trainer/Q Targets Std 18.3881 +trainer/Q Targets Max -0.302399 +trainer/Q Targets Min -86.3819 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00968354 +trainer/policy/mean Std 0.718336 +trainer/policy/mean Max 0.99408 +trainer/policy/mean Min -0.993981 +trainer/policy/std Mean 0.433451 +trainer/policy/std Std 0.0214357 +trainer/policy/std Max 0.458157 +trainer/policy/std Min 0.39579 +trainer/Advantage Weights Mean 3.15567 +trainer/Advantage Weights Std 13.9846 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.14095e-11 +trainer/Advantage Score Mean -0.295809 +trainer/Advantage Score Std 0.446211 +trainer/Advantage Score Max 1.19733 +trainer/Advantage Score Min -2.51966 +trainer/V1 Predictions Mean -73.0818 +trainer/V1 Predictions Std 18.5229 +trainer/V1 Predictions Max 0.670864 +trainer/V1 Predictions Min -86.2448 +trainer/VF Loss 0.0406365 +expl/num steps total 222000 +expl/num paths total 239 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0698866 +expl/Actions Std 0.834443 +expl/Actions Max 2.44116 +expl/Actions Min -2.61615 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 219813 +eval/num paths total 222 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.132097 +eval/Actions Std 0.68491 +eval/Actions Max 0.999366 +eval/Actions Min -0.999622 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99141e-06 +time/evaluation sampling (s) 2.46657 +time/exploration sampling (s) 2.92992 +time/logging (s) 0.00667728 +time/saving (s) 0.00941979 +time/training (s) 12.1978 +time/epoch (s) 17.6104 +time/total (s) 4574.59 +Epoch -779 +------------------------------ ---------------- +2022-05-15 19:18:58.908190 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -778 finished +------------------------------ --------------- +epoch -778 +replay_buffer/size 999047 +trainer/num train calls 223000 +trainer/QF1 Loss 7.23302 +trainer/QF2 Loss 7.37665 +trainer/Policy Loss 22.5724 +trainer/Q1 Predictions Mean -71.1358 +trainer/Q1 Predictions Std 21.8332 +trainer/Q1 Predictions Max -0.323758 +trainer/Q1 Predictions Min -86.4971 +trainer/Q2 Predictions Mean -71.0613 +trainer/Q2 Predictions Std 21.7205 +trainer/Q2 Predictions Max -0.268606 +trainer/Q2 Predictions Min -86.3275 +trainer/Q Targets Mean -71.4329 +trainer/Q Targets Std 21.4213 +trainer/Q Targets Max -2.75985 +trainer/Q Targets Min -86.7353 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0165046 +trainer/policy/mean Std 0.704619 +trainer/policy/mean Max 0.998638 +trainer/policy/mean Min -0.998736 +trainer/policy/std Mean 0.434117 +trainer/policy/std Std 0.0215833 +trainer/policy/std Max 0.45884 +trainer/policy/std Min 0.398875 +trainer/Advantage Weights Mean 4.57524 +trainer/Advantage Weights Std 18.2063 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.7533e-10 +trainer/Advantage Score Mean -0.405608 +trainer/Advantage Score Std 0.510463 +trainer/Advantage Score Max 1.4334 +trainer/Advantage Score Min -2.24644 +trainer/V1 Predictions Mean -71.0172 +trainer/V1 Predictions Std 21.9108 +trainer/V1 Predictions Max 0.340713 +trainer/V1 Predictions Min -86.6938 +trainer/VF Loss 0.0746434 +expl/num steps total 223000 +expl/num paths total 240 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0660612 +expl/Actions Std 0.806424 +expl/Actions Max 2.87813 +expl/Actions Min -2.25351 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 220813 +eval/num paths total 223 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0201598 +eval/Actions Std 0.717567 +eval/Actions Max 0.999056 +eval/Actions Min -0.998736 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.6417e-06 +time/evaluation sampling (s) 2.4922 +time/exploration sampling (s) 2.95451 +time/logging (s) 0.00950164 +time/saving (s) 0.0118685 +time/training (s) 12.73 +time/epoch (s) 18.1981 +time/total (s) 4592.79 +Epoch -778 +------------------------------ --------------- +2022-05-15 19:19:16.415650 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -777 finished +------------------------------ ---------------- +epoch -777 +replay_buffer/size 999047 +trainer/num train calls 224000 +trainer/QF1 Loss 2.98182 +trainer/QF2 Loss 2.9475 +trainer/Policy Loss 0.243805 +trainer/Q1 Predictions Mean -73.0421 +trainer/Q1 Predictions Std 18.6946 +trainer/Q1 Predictions Max -1.42611 +trainer/Q1 Predictions Min -87.1099 +trainer/Q2 Predictions Mean -73.1626 +trainer/Q2 Predictions Std 18.7905 +trainer/Q2 Predictions Max -1.44269 +trainer/Q2 Predictions Min -87.5798 +trainer/Q Targets Mean -72.8455 +trainer/Q Targets Std 18.8271 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7153 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000627121 +trainer/policy/mean Std 0.71334 +trainer/policy/mean Max 0.999562 +trainer/policy/mean Min -0.999415 +trainer/policy/std Mean 0.433659 +trainer/policy/std Std 0.0219918 +trainer/policy/std Max 0.457806 +trainer/policy/std Min 0.398071 +trainer/Advantage Weights Mean 0.0642291 +trainer/Advantage Weights Std 0.185212 +trainer/Advantage Weights Max 1.31638 +trainer/Advantage Weights Min 2.91612e-17 +trainer/Advantage Score Mean -0.69993 +trainer/Advantage Score Std 0.52734 +trainer/Advantage Score Max 0.0274887 +trainer/Advantage Score Min -3.80737 +trainer/V1 Predictions Mean -72.6358 +trainer/V1 Predictions Std 19.0159 +trainer/V1 Predictions Max 0.103701 +trainer/V1 Predictions Min -87.6241 +trainer/VF Loss 0.0768038 +expl/num steps total 224000 +expl/num paths total 241 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0425862 +expl/Actions Std 0.802957 +expl/Actions Max 2.28569 +expl/Actions Min -2.41646 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 221813 +eval/num paths total 224 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0231982 +eval/Actions Std 0.716156 +eval/Actions Max 0.99877 +eval/Actions Min -0.999053 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.685e-06 +time/evaluation sampling (s) 2.45418 +time/exploration sampling (s) 2.9753 +time/logging (s) 0.00768327 +time/saving (s) 0.0105469 +time/training (s) 12.0517 +time/epoch (s) 17.4994 +time/total (s) 4610.3 +Epoch -777 +------------------------------ ---------------- +2022-05-15 19:19:34.372880 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -776 finished +------------------------------ ---------------- +epoch -776 +replay_buffer/size 999047 +trainer/num train calls 225000 +trainer/QF1 Loss 0.555984 +trainer/QF2 Loss 0.693057 +trainer/Policy Loss 14.3971 +trainer/Q1 Predictions Mean -73.6764 +trainer/Q1 Predictions Std 16.5388 +trainer/Q1 Predictions Max -0.272416 +trainer/Q1 Predictions Min -86.7417 +trainer/Q2 Predictions Mean -73.61 +trainer/Q2 Predictions Std 16.54 +trainer/Q2 Predictions Max -0.614114 +trainer/Q2 Predictions Min -86.7669 +trainer/Q Targets Mean -73.4639 +trainer/Q Targets Std 16.7067 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8455 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00906408 +trainer/policy/mean Std 0.6991 +trainer/policy/mean Max 0.996963 +trainer/policy/mean Min -0.99469 +trainer/policy/std Mean 0.433116 +trainer/policy/std Std 0.023408 +trainer/policy/std Max 0.45941 +trainer/policy/std Min 0.396417 +trainer/Advantage Weights Mean 3.38568 +trainer/Advantage Weights Std 16.3634 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.49356e-15 +trainer/Advantage Score Mean -0.372468 +trainer/Advantage Score Std 0.452353 +trainer/Advantage Score Max 0.715618 +trainer/Advantage Score Min -3.41376 +trainer/V1 Predictions Mean -73.3092 +trainer/V1 Predictions Std 16.7048 +trainer/V1 Predictions Max -0.245606 +trainer/V1 Predictions Min -86.7118 +trainer/VF Loss 0.044276 +expl/num steps total 225000 +expl/num paths total 242 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.120547 +expl/Actions Std 0.813222 +expl/Actions Max 2.26394 +expl/Actions Min -2.29039 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 222813 +eval/num paths total 225 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0257601 +eval/Actions Std 0.703405 +eval/Actions Max 0.999364 +eval/Actions Min -0.998989 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.00741e-05 +time/evaluation sampling (s) 2.55038 +time/exploration sampling (s) 2.96025 +time/logging (s) 0.00789132 +time/saving (s) 0.0123797 +time/training (s) 12.4204 +time/epoch (s) 17.9513 +time/total (s) 4628.25 +Epoch -776 +------------------------------ ---------------- +2022-05-15 19:19:52.232864 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -775 finished +------------------------------ --------------- +epoch -775 +replay_buffer/size 999047 +trainer/num train calls 226000 +trainer/QF1 Loss 0.639803 +trainer/QF2 Loss 0.719418 +trainer/Policy Loss 24.4141 +trainer/Q1 Predictions Mean -72.868 +trainer/Q1 Predictions Std 20.1442 +trainer/Q1 Predictions Max -0.387416 +trainer/Q1 Predictions Min -88.1404 +trainer/Q2 Predictions Mean -72.9142 +trainer/Q2 Predictions Std 20.0387 +trainer/Q2 Predictions Max -0.2678 +trainer/Q2 Predictions Min -88.1287 +trainer/Q Targets Mean -73.1137 +trainer/Q Targets Std 20.0179 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.444 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.023903 +trainer/policy/mean Std 0.703779 +trainer/policy/mean Max 0.997506 +trainer/policy/mean Min -0.996503 +trainer/policy/std Mean 0.43136 +trainer/policy/std Std 0.0235471 +trainer/policy/std Max 0.456463 +trainer/policy/std Min 0.391963 +trainer/Advantage Weights Mean 6.93932 +trainer/Advantage Weights Std 19.6874 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.1303e-10 +trainer/Advantage Score Mean -0.228753 +trainer/Advantage Score Std 0.430916 +trainer/Advantage Score Max 0.766655 +trainer/Advantage Score Min -2.13907 +trainer/V1 Predictions Mean -72.8023 +trainer/V1 Predictions Std 20.2583 +trainer/V1 Predictions Max 0.302136 +trainer/V1 Predictions Min -87.22 +trainer/VF Loss 0.0430692 +expl/num steps total 226000 +expl/num paths total 243 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0356406 +expl/Actions Std 0.819926 +expl/Actions Max 2.28679 +expl/Actions Min -2.35956 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 223813 +eval/num paths total 226 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.084669 +eval/Actions Std 0.619643 +eval/Actions Max 0.997526 +eval/Actions Min -0.999319 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.0701e-06 +time/evaluation sampling (s) 2.49012 +time/exploration sampling (s) 2.92857 +time/logging (s) 0.0067188 +time/saving (s) 0.00980485 +time/training (s) 12.4166 +time/epoch (s) 17.8518 +time/total (s) 4646.11 +Epoch -775 +------------------------------ --------------- +2022-05-15 19:20:10.512583 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -774 finished +------------------------------ ---------------- +epoch -774 +replay_buffer/size 999047 +trainer/num train calls 227000 +trainer/QF1 Loss 0.52656 +trainer/QF2 Loss 0.539433 +trainer/Policy Loss 12.7785 +trainer/Q1 Predictions Mean -73.8019 +trainer/Q1 Predictions Std 17.4553 +trainer/Q1 Predictions Max -0.956237 +trainer/Q1 Predictions Min -87.4103 +trainer/Q2 Predictions Mean -73.8066 +trainer/Q2 Predictions Std 17.4663 +trainer/Q2 Predictions Max -0.896349 +trainer/Q2 Predictions Min -87.5002 +trainer/Q Targets Mean -73.7632 +trainer/Q Targets Std 17.1956 +trainer/Q Targets Max -1.36285 +trainer/Q Targets Min -87.4605 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0157941 +trainer/policy/mean Std 0.711324 +trainer/policy/mean Max 0.999206 +trainer/policy/mean Min -0.999066 +trainer/policy/std Mean 0.430227 +trainer/policy/std Std 0.0226583 +trainer/policy/std Max 0.454463 +trainer/policy/std Min 0.39002 +trainer/Advantage Weights Mean 3.31253 +trainer/Advantage Weights Std 15.5934 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.37931e-07 +trainer/Advantage Score Mean -0.35395 +trainer/Advantage Score Std 0.402242 +trainer/Advantage Score Max 1.21575 +trainer/Advantage Score Min -1.57965 +trainer/V1 Predictions Mean -73.5084 +trainer/V1 Predictions Std 17.3668 +trainer/V1 Predictions Max 0.251123 +trainer/V1 Predictions Min -87.209 +trainer/VF Loss 0.0467859 +expl/num steps total 227000 +expl/num paths total 244 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.04723 +expl/Actions Std 0.828669 +expl/Actions Max 2.55684 +expl/Actions Min -2.52653 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 224813 +eval/num paths total 227 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.179329 +eval/Actions Std 0.793371 +eval/Actions Max 0.996219 +eval/Actions Min -0.993027 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.41097e-06 +time/evaluation sampling (s) 2.56974 +time/exploration sampling (s) 2.87387 +time/logging (s) 0.00756347 +time/saving (s) 0.0120786 +time/training (s) 12.8111 +time/epoch (s) 18.2744 +time/total (s) 4664.39 +Epoch -774 +------------------------------ ---------------- +2022-05-15 19:20:28.758074 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -773 finished +------------------------------ ---------------- +epoch -773 +replay_buffer/size 999047 +trainer/num train calls 228000 +trainer/QF1 Loss 0.889154 +trainer/QF2 Loss 1.06796 +trainer/Policy Loss 22.9448 +trainer/Q1 Predictions Mean -72.7585 +trainer/Q1 Predictions Std 19.4577 +trainer/Q1 Predictions Max -2.01825 +trainer/Q1 Predictions Min -87.7007 +trainer/Q2 Predictions Mean -72.5888 +trainer/Q2 Predictions Std 19.5497 +trainer/Q2 Predictions Max -1.63897 +trainer/Q2 Predictions Min -87.6188 +trainer/Q Targets Mean -72.7006 +trainer/Q Targets Std 19.8922 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2819 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0228159 +trainer/policy/mean Std 0.713411 +trainer/policy/mean Max 0.998283 +trainer/policy/mean Min -0.999518 +trainer/policy/std Mean 0.432633 +trainer/policy/std Std 0.022072 +trainer/policy/std Max 0.454334 +trainer/policy/std Min 0.396067 +trainer/Advantage Weights Mean 5.43209 +trainer/Advantage Weights Std 16.6905 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.34665e-14 +trainer/Advantage Score Mean -0.281295 +trainer/Advantage Score Std 0.579815 +trainer/Advantage Score Max 1.07781 +trainer/Advantage Score Min -3.10282 +trainer/V1 Predictions Mean -72.3968 +trainer/V1 Predictions Std 20.1004 +trainer/V1 Predictions Max -0.16567 +trainer/V1 Predictions Min -87.3011 +trainer/VF Loss 0.0593743 +expl/num steps total 228000 +expl/num paths total 245 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.033807 +expl/Actions Std 0.821834 +expl/Actions Max 2.78432 +expl/Actions Min -2.25133 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 225813 +eval/num paths total 228 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.120523 +eval/Actions Std 0.755837 +eval/Actions Max 0.998753 +eval/Actions Min -0.997781 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.2559e-06 +time/evaluation sampling (s) 2.5026 +time/exploration sampling (s) 2.86056 +time/logging (s) 0.00665473 +time/saving (s) 0.0095886 +time/training (s) 12.8574 +time/epoch (s) 18.2368 +time/total (s) 4682.63 +Epoch -773 +------------------------------ ---------------- +2022-05-15 19:20:46.607355 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -772 finished +------------------------------ ---------------- +epoch -772 +replay_buffer/size 999047 +trainer/num train calls 229000 +trainer/QF1 Loss 1.11296 +trainer/QF2 Loss 1.2804 +trainer/Policy Loss 4.31995 +trainer/Q1 Predictions Mean -73.1839 +trainer/Q1 Predictions Std 18.6584 +trainer/Q1 Predictions Max -0.674789 +trainer/Q1 Predictions Min -87.9405 +trainer/Q2 Predictions Mean -73.1128 +trainer/Q2 Predictions Std 18.6599 +trainer/Q2 Predictions Max -1.06893 +trainer/Q2 Predictions Min -87.7766 +trainer/Q Targets Mean -73.1075 +trainer/Q Targets Std 18.457 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4007 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0311586 +trainer/policy/mean Std 0.705696 +trainer/policy/mean Max 0.997931 +trainer/policy/mean Min -0.998403 +trainer/policy/std Mean 0.431368 +trainer/policy/std Std 0.0218366 +trainer/policy/std Max 0.458233 +trainer/policy/std Min 0.394655 +trainer/Advantage Weights Mean 1.69437 +trainer/Advantage Weights Std 10.9522 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.61796e-11 +trainer/Advantage Score Mean -0.421386 +trainer/Advantage Score Std 0.427866 +trainer/Advantage Score Max 1.38293 +trainer/Advantage Score Min -2.30648 +trainer/V1 Predictions Mean -72.8186 +trainer/V1 Predictions Std 18.5446 +trainer/V1 Predictions Max -0.242272 +trainer/V1 Predictions Min -87.3465 +trainer/VF Loss 0.0483234 +expl/num steps total 229000 +expl/num paths total 246 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0381196 +expl/Actions Std 0.81508 +expl/Actions Max 2.2408 +expl/Actions Min -2.5948 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 226571 +eval/num paths total 229 +eval/path length Mean 758 +eval/path length Std 0 +eval/path length Max 758 +eval/path length Min 758 +eval/Rewards Mean 0.00131926 +eval/Rewards Std 0.0362977 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0264456 +eval/Actions Std 0.707356 +eval/Actions Max 0.999617 +eval/Actions Min -0.999148 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.33274e-06 +time/evaluation sampling (s) 2.4808 +time/exploration sampling (s) 2.80595 +time/logging (s) 0.00636008 +time/saving (s) 0.0104968 +time/training (s) 12.539 +time/epoch (s) 17.8426 +time/total (s) 4700.48 +Epoch -772 +------------------------------ ---------------- +2022-05-15 19:21:04.614984 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -771 finished +------------------------------ ---------------- +epoch -771 +replay_buffer/size 999047 +trainer/num train calls 230000 +trainer/QF1 Loss 0.502469 +trainer/QF2 Loss 0.580008 +trainer/Policy Loss 16.8911 +trainer/Q1 Predictions Mean -73.1681 +trainer/Q1 Predictions Std 17.4701 +trainer/Q1 Predictions Max -1.46386 +trainer/Q1 Predictions Min -87.7087 +trainer/Q2 Predictions Mean -73.3194 +trainer/Q2 Predictions Std 17.4881 +trainer/Q2 Predictions Max -1.81725 +trainer/Q2 Predictions Min -87.7015 +trainer/Q Targets Mean -73.3235 +trainer/Q Targets Std 17.3739 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3973 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0132425 +trainer/policy/mean Std 0.709283 +trainer/policy/mean Max 0.998788 +trainer/policy/mean Min -0.999067 +trainer/policy/std Mean 0.430072 +trainer/policy/std Std 0.0222447 +trainer/policy/std Max 0.457963 +trainer/policy/std Min 0.391595 +trainer/Advantage Weights Mean 2.22194 +trainer/Advantage Weights Std 10.7404 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.14352e-09 +trainer/Advantage Score Mean -0.349057 +trainer/Advantage Score Std 0.423121 +trainer/Advantage Score Max 1.73798 +trainer/Advantage Score Min -1.95779 +trainer/V1 Predictions Mean -73.0804 +trainer/V1 Predictions Std 17.5073 +trainer/V1 Predictions Max -1.48829 +trainer/V1 Predictions Min -87.1814 +trainer/VF Loss 0.0450747 +expl/num steps total 230000 +expl/num paths total 247 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0694611 +expl/Actions Std 0.812528 +expl/Actions Max 2.84377 +expl/Actions Min -2.34378 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 227571 +eval/num paths total 230 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.217413 +eval/Actions Std 0.630733 +eval/Actions Max 0.998426 +eval/Actions Min -0.997596 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.59094e-06 +time/evaluation sampling (s) 2.60642 +time/exploration sampling (s) 2.80806 +time/logging (s) 0.0081955 +time/saving (s) 0.0127722 +time/training (s) 12.5669 +time/epoch (s) 18.0023 +time/total (s) 4718.48 +Epoch -771 +------------------------------ ---------------- +2022-05-15 19:21:23.229355 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -770 finished +------------------------------ ---------------- +epoch -770 +replay_buffer/size 999047 +trainer/num train calls 231000 +trainer/QF1 Loss 0.463173 +trainer/QF2 Loss 0.492266 +trainer/Policy Loss 26.7145 +trainer/Q1 Predictions Mean -76.3928 +trainer/Q1 Predictions Std 13.5737 +trainer/Q1 Predictions Max -5.7338 +trainer/Q1 Predictions Min -87.6248 +trainer/Q2 Predictions Mean -76.3986 +trainer/Q2 Predictions Std 13.5628 +trainer/Q2 Predictions Max -5.26505 +trainer/Q2 Predictions Min -87.29 +trainer/Q Targets Mean -76.5511 +trainer/Q Targets Std 13.6288 +trainer/Q Targets Max -6.28452 +trainer/Q Targets Min -87.1389 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0180419 +trainer/policy/mean Std 0.701245 +trainer/policy/mean Max 0.997342 +trainer/policy/mean Min -0.999076 +trainer/policy/std Mean 0.430598 +trainer/policy/std Std 0.0219465 +trainer/policy/std Max 0.4588 +trainer/policy/std Min 0.393183 +trainer/Advantage Weights Mean 5.08948 +trainer/Advantage Weights Std 17.5438 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.10043e-11 +trainer/Advantage Score Mean -0.200961 +trainer/Advantage Score Std 0.447016 +trainer/Advantage Score Max 1.40724 +trainer/Advantage Score Min -2.45863 +trainer/V1 Predictions Mean -76.401 +trainer/V1 Predictions Std 13.6368 +trainer/V1 Predictions Max -6.37576 +trainer/V1 Predictions Min -87.3912 +trainer/VF Loss 0.0453816 +expl/num steps total 231000 +expl/num paths total 248 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0286097 +expl/Actions Std 0.817618 +expl/Actions Max 2.30901 +expl/Actions Min -2.39709 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 228571 +eval/num paths total 231 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00649988 +eval/Actions Std 0.697743 +eval/Actions Max 0.999758 +eval/Actions Min -0.999206 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.11993e-06 +time/evaluation sampling (s) 2.59419 +time/exploration sampling (s) 2.73871 +time/logging (s) 0.00756168 +time/saving (s) 0.0122377 +time/training (s) 13.2534 +time/epoch (s) 18.6061 +time/total (s) 4737.09 +Epoch -770 +------------------------------ ---------------- +2022-05-15 19:21:41.097627 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -769 finished +------------------------------ ---------------- +epoch -769 +replay_buffer/size 999047 +trainer/num train calls 232000 +trainer/QF1 Loss 0.888341 +trainer/QF2 Loss 0.989561 +trainer/Policy Loss 21.6711 +trainer/Q1 Predictions Mean -72.8017 +trainer/Q1 Predictions Std 19.812 +trainer/Q1 Predictions Max -0.588992 +trainer/Q1 Predictions Min -86.9123 +trainer/Q2 Predictions Mean -72.7555 +trainer/Q2 Predictions Std 19.9051 +trainer/Q2 Predictions Max -0.645982 +trainer/Q2 Predictions Min -87.0029 +trainer/Q Targets Mean -73.1096 +trainer/Q Targets Std 20.0998 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9447 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00109118 +trainer/policy/mean Std 0.71147 +trainer/policy/mean Max 0.998087 +trainer/policy/mean Min -0.993434 +trainer/policy/std Mean 0.432552 +trainer/policy/std Std 0.0220617 +trainer/policy/std Max 0.460626 +trainer/policy/std Min 0.394538 +trainer/Advantage Weights Mean 4.25185 +trainer/Advantage Weights Std 16.7925 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.06306e-13 +trainer/Advantage Score Mean -0.301127 +trainer/Advantage Score Std 0.532446 +trainer/Advantage Score Max 1.25142 +trainer/Advantage Score Min -2.81314 +trainer/V1 Predictions Mean -72.758 +trainer/V1 Predictions Std 20.2222 +trainer/V1 Predictions Max -0.186788 +trainer/V1 Predictions Min -87.3914 +trainer/VF Loss 0.0578156 +expl/num steps total 232000 +expl/num paths total 249 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.012474 +expl/Actions Std 0.82791 +expl/Actions Max 2.34842 +expl/Actions Min -2.48093 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 229240 +eval/num paths total 232 +eval/path length Mean 669 +eval/path length Std 0 +eval/path length Max 669 +eval/path length Min 669 +eval/Rewards Mean 0.00149477 +eval/Rewards Std 0.0386333 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0194441 +eval/Actions Std 0.718421 +eval/Actions Max 0.998815 +eval/Actions Min -0.998615 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.68919e-06 +time/evaluation sampling (s) 2.47934 +time/exploration sampling (s) 2.62342 +time/logging (s) 0.00638952 +time/saving (s) 0.0106893 +time/training (s) 12.7394 +time/epoch (s) 17.8592 +time/total (s) 4754.96 +Epoch -769 +------------------------------ ---------------- +2022-05-15 19:21:58.977386 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -768 finished +------------------------------ ---------------- +epoch -768 +replay_buffer/size 999047 +trainer/num train calls 233000 +trainer/QF1 Loss 0.387027 +trainer/QF2 Loss 0.405507 +trainer/Policy Loss 19.1857 +trainer/Q1 Predictions Mean -74.8471 +trainer/Q1 Predictions Std 17.9714 +trainer/Q1 Predictions Max -1.31415 +trainer/Q1 Predictions Min -86.8862 +trainer/Q2 Predictions Mean -74.8139 +trainer/Q2 Predictions Std 18.0015 +trainer/Q2 Predictions Max -1.22183 +trainer/Q2 Predictions Min -86.7176 +trainer/Q Targets Mean -74.7417 +trainer/Q Targets Std 17.8618 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7282 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00841675 +trainer/policy/mean Std 0.718362 +trainer/policy/mean Max 0.997265 +trainer/policy/mean Min -0.997977 +trainer/policy/std Mean 0.432385 +trainer/policy/std Std 0.0226371 +trainer/policy/std Max 0.459009 +trainer/policy/std Min 0.394283 +trainer/Advantage Weights Mean 5.18657 +trainer/Advantage Weights Std 17.7195 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.34391e-09 +trainer/Advantage Score Mean -0.174963 +trainer/Advantage Score Std 0.346474 +trainer/Advantage Score Max 0.680088 +trainer/Advantage Score Min -1.95161 +trainer/V1 Predictions Mean -74.5559 +trainer/V1 Predictions Std 17.8557 +trainer/V1 Predictions Max -1.34692 +trainer/V1 Predictions Min -86.4951 +trainer/VF Loss 0.0287667 +expl/num steps total 233000 +expl/num paths total 251 +expl/path length Mean 500 +expl/path length Std 368 +expl/path length Max 868 +expl/path length Min 132 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0369476 +expl/Actions Std 0.797956 +expl/Actions Max 2.55687 +expl/Actions Min -2.3785 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 230240 +eval/num paths total 233 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0480329 +eval/Actions Std 0.706774 +eval/Actions Max 0.997543 +eval/Actions Min -0.998415 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.21912e-06 +time/evaluation sampling (s) 2.44657 +time/exploration sampling (s) 2.69476 +time/logging (s) 0.00737591 +time/saving (s) 0.0116332 +time/training (s) 12.7139 +time/epoch (s) 17.8742 +time/total (s) 4772.84 +Epoch -768 +------------------------------ ---------------- +2022-05-15 19:22:17.101017 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -767 finished +------------------------------ ---------------- +epoch -767 +replay_buffer/size 999047 +trainer/num train calls 234000 +trainer/QF1 Loss 1.04215 +trainer/QF2 Loss 1.08011 +trainer/Policy Loss 42.0965 +trainer/Q1 Predictions Mean -74.542 +trainer/Q1 Predictions Std 15.7298 +trainer/Q1 Predictions Max -3.01489 +trainer/Q1 Predictions Min -87.0502 +trainer/Q2 Predictions Mean -74.5228 +trainer/Q2 Predictions Std 15.7379 +trainer/Q2 Predictions Max -2.93413 +trainer/Q2 Predictions Min -86.7755 +trainer/Q Targets Mean -74.9528 +trainer/Q Targets Std 15.4624 +trainer/Q Targets Max -5.33209 +trainer/Q Targets Min -86.8835 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00578082 +trainer/policy/mean Std 0.719737 +trainer/policy/mean Max 0.996789 +trainer/policy/mean Min -0.997497 +trainer/policy/std Mean 0.433084 +trainer/policy/std Std 0.0219402 +trainer/policy/std Max 0.457103 +trainer/policy/std Min 0.39758 +trainer/Advantage Weights Mean 7.57625 +trainer/Advantage Weights Std 22.941 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.00736e-14 +trainer/Advantage Score Mean -0.222004 +trainer/Advantage Score Std 0.462267 +trainer/Advantage Score Max 0.959572 +trainer/Advantage Score Min -3.02892 +trainer/V1 Predictions Mean -74.6374 +trainer/V1 Predictions Std 15.7478 +trainer/V1 Predictions Max -2.48232 +trainer/V1 Predictions Min -86.6014 +trainer/VF Loss 0.0549567 +expl/num steps total 234000 +expl/num paths total 252 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0658653 +expl/Actions Std 0.845858 +expl/Actions Max 2.73395 +expl/Actions Min -2.35921 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 231240 +eval/num paths total 234 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0336803 +eval/Actions Std 0.723971 +eval/Actions Max 0.998583 +eval/Actions Min -0.998629 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99839e-06 +time/evaluation sampling (s) 2.60896 +time/exploration sampling (s) 2.85151 +time/logging (s) 0.00672374 +time/saving (s) 0.00939185 +time/training (s) 12.6375 +time/epoch (s) 18.1141 +time/total (s) 4790.95 +Epoch -767 +------------------------------ ---------------- +2022-05-15 19:22:34.639870 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -766 finished +------------------------------ ---------------- +epoch -766 +replay_buffer/size 999047 +trainer/num train calls 235000 +trainer/QF1 Loss 0.627005 +trainer/QF2 Loss 0.690895 +trainer/Policy Loss 12.7893 +trainer/Q1 Predictions Mean -70.8743 +trainer/Q1 Predictions Std 21.7078 +trainer/Q1 Predictions Max -0.367575 +trainer/Q1 Predictions Min -87.1964 +trainer/Q2 Predictions Mean -70.942 +trainer/Q2 Predictions Std 21.7379 +trainer/Q2 Predictions Max -0.364941 +trainer/Q2 Predictions Min -87.1519 +trainer/Q Targets Mean -70.7091 +trainer/Q Targets Std 21.6844 +trainer/Q Targets Max 1.40098 +trainer/Q Targets Min -87.1213 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00836741 +trainer/policy/mean Std 0.722984 +trainer/policy/mean Max 0.998245 +trainer/policy/mean Min -0.99783 +trainer/policy/std Mean 0.432493 +trainer/policy/std Std 0.0219088 +trainer/policy/std Max 0.457471 +trainer/policy/std Min 0.394878 +trainer/Advantage Weights Mean 3.99425 +trainer/Advantage Weights Std 17.6011 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.91591e-15 +trainer/Advantage Score Mean -0.367928 +trainer/Advantage Score Std 0.485719 +trainer/Advantage Score Max 1.10728 +trainer/Advantage Score Min -3.38886 +trainer/V1 Predictions Mean -70.5109 +trainer/V1 Predictions Std 21.6842 +trainer/V1 Predictions Max 2.9919 +trainer/V1 Predictions Min -86.8906 +trainer/VF Loss 0.0561881 +expl/num steps total 235000 +expl/num paths total 253 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0389701 +expl/Actions Std 0.834353 +expl/Actions Max 2.5272 +expl/Actions Min -2.38935 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 232240 +eval/num paths total 235 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0247906 +eval/Actions Std 0.713497 +eval/Actions Max 0.998564 +eval/Actions Min -0.99887 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.34717e-06 +time/evaluation sampling (s) 2.52883 +time/exploration sampling (s) 2.63876 +time/logging (s) 0.00749863 +time/saving (s) 0.0124115 +time/training (s) 12.346 +time/epoch (s) 17.5335 +time/total (s) 4808.49 +Epoch -766 +------------------------------ ---------------- +2022-05-15 19:22:52.506360 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -765 finished +------------------------------ ---------------- +epoch -765 +replay_buffer/size 999047 +trainer/num train calls 236000 +trainer/QF1 Loss 0.654795 +trainer/QF2 Loss 0.648609 +trainer/Policy Loss 14.9994 +trainer/Q1 Predictions Mean -73.9557 +trainer/Q1 Predictions Std 16.7526 +trainer/Q1 Predictions Max -7.40855 +trainer/Q1 Predictions Min -86.9959 +trainer/Q2 Predictions Mean -74.0088 +trainer/Q2 Predictions Std 16.7638 +trainer/Q2 Predictions Max -7.08906 +trainer/Q2 Predictions Min -87.066 +trainer/Q Targets Mean -74.1089 +trainer/Q Targets Std 16.9884 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8235 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0160377 +trainer/policy/mean Std 0.708865 +trainer/policy/mean Max 0.997829 +trainer/policy/mean Min -0.997093 +trainer/policy/std Mean 0.432409 +trainer/policy/std Std 0.0222207 +trainer/policy/std Max 0.45872 +trainer/policy/std Min 0.395051 +trainer/Advantage Weights Mean 2.83601 +trainer/Advantage Weights Std 14.0202 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.01074e-10 +trainer/Advantage Score Mean -0.336995 +trainer/Advantage Score Std 0.402488 +trainer/Advantage Score Max 0.757706 +trainer/Advantage Score Min -2.23273 +trainer/V1 Predictions Mean -73.8972 +trainer/V1 Predictions Std 16.8348 +trainer/V1 Predictions Max -6.20071 +trainer/V1 Predictions Min -86.7736 +trainer/VF Loss 0.0358166 +expl/num steps total 236000 +expl/num paths total 254 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0367644 +expl/Actions Std 0.799809 +expl/Actions Max 2.45403 +expl/Actions Min -2.32114 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 233240 +eval/num paths total 236 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0233409 +eval/Actions Std 0.720007 +eval/Actions Max 0.999026 +eval/Actions Min -0.998275 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.36813e-06 +time/evaluation sampling (s) 2.4905 +time/exploration sampling (s) 2.64065 +time/logging (s) 0.0113159 +time/saving (s) 0.0186401 +time/training (s) 12.7007 +time/epoch (s) 17.8618 +time/total (s) 4826.36 +Epoch -765 +------------------------------ ---------------- +2022-05-15 19:23:10.471420 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -764 finished +------------------------------ ---------------- +epoch -764 +replay_buffer/size 999047 +trainer/num train calls 237000 +trainer/QF1 Loss 3.7289 +trainer/QF2 Loss 4.0158 +trainer/Policy Loss 10.61 +trainer/Q1 Predictions Mean -73.6548 +trainer/Q1 Predictions Std 16.1075 +trainer/Q1 Predictions Max -1.46601 +trainer/Q1 Predictions Min -85.4055 +trainer/Q2 Predictions Mean -73.6224 +trainer/Q2 Predictions Std 16.0117 +trainer/Q2 Predictions Max -2.13716 +trainer/Q2 Predictions Min -85.5044 +trainer/Q Targets Mean -74.097 +trainer/Q Targets Std 16.8818 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.5448 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00888539 +trainer/policy/mean Std 0.724435 +trainer/policy/mean Max 0.998551 +trainer/policy/mean Min -0.996594 +trainer/policy/std Mean 0.433579 +trainer/policy/std Std 0.0227469 +trainer/policy/std Max 0.458972 +trainer/policy/std Min 0.395173 +trainer/Advantage Weights Mean 4.04581 +trainer/Advantage Weights Std 17.9148 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.13738e-22 +trainer/Advantage Score Mean -0.837582 +trainer/Advantage Score Std 0.816357 +trainer/Advantage Score Max 2.19342 +trainer/Advantage Score Min -4.98973 +trainer/V1 Predictions Mean -73.856 +trainer/V1 Predictions Std 16.7967 +trainer/V1 Predictions Max -1.50873 +trainer/V1 Predictions Min -85.6352 +trainer/VF Loss 0.16603 +expl/num steps total 237000 +expl/num paths total 255 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0254368 +expl/Actions Std 0.799726 +expl/Actions Max 2.59272 +expl/Actions Min -2.18589 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 234240 +eval/num paths total 237 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.521356 +eval/Actions Std 0.59271 +eval/Actions Max 0.998831 +eval/Actions Min -0.9987 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.87313e-06 +time/evaluation sampling (s) 2.55177 +time/exploration sampling (s) 2.69069 +time/logging (s) 0.00672587 +time/saving (s) 0.00952915 +time/training (s) 12.692 +time/epoch (s) 17.9507 +time/total (s) 4844.31 +Epoch -764 +------------------------------ ---------------- +2022-05-15 19:23:28.289033 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -763 finished +------------------------------ ---------------- +epoch -763 +replay_buffer/size 999047 +trainer/num train calls 238000 +trainer/QF1 Loss 0.572789 +trainer/QF2 Loss 0.571156 +trainer/Policy Loss 9.32734 +trainer/Q1 Predictions Mean -74.7004 +trainer/Q1 Predictions Std 16.8221 +trainer/Q1 Predictions Max -2.30832 +trainer/Q1 Predictions Min -87.6807 +trainer/Q2 Predictions Mean -74.6767 +trainer/Q2 Predictions Std 16.844 +trainer/Q2 Predictions Max -1.45812 +trainer/Q2 Predictions Min -87.3561 +trainer/Q Targets Mean -74.554 +trainer/Q Targets Std 16.6794 +trainer/Q Targets Max -1.90073 +trainer/Q Targets Min -87.4505 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00589343 +trainer/policy/mean Std 0.713537 +trainer/policy/mean Max 0.99941 +trainer/policy/mean Min -0.997902 +trainer/policy/std Mean 0.431891 +trainer/policy/std Std 0.0223881 +trainer/policy/std Max 0.454586 +trainer/policy/std Min 0.393209 +trainer/Advantage Weights Mean 2.49752 +trainer/Advantage Weights Std 12.5385 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.65134e-16 +trainer/Advantage Score Mean -0.346781 +trainer/Advantage Score Std 0.476563 +trainer/Advantage Score Max 0.908841 +trainer/Advantage Score Min -3.48065 +trainer/V1 Predictions Mean -74.2887 +trainer/V1 Predictions Std 16.8731 +trainer/V1 Predictions Max -0.501237 +trainer/V1 Predictions Min -87.7364 +trainer/VF Loss 0.0432646 +expl/num steps total 238000 +expl/num paths total 256 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0238085 +expl/Actions Std 0.8058 +expl/Actions Max 2.53721 +expl/Actions Min -2.23581 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 235240 +eval/num paths total 238 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0305396 +eval/Actions Std 0.711792 +eval/Actions Max 0.999689 +eval/Actions Min -0.998884 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.12785e-06 +time/evaluation sampling (s) 2.44209 +time/exploration sampling (s) 2.65349 +time/logging (s) 0.00666887 +time/saving (s) 0.0100834 +time/training (s) 12.6989 +time/epoch (s) 17.8113 +time/total (s) 4862.13 +Epoch -763 +------------------------------ ---------------- +2022-05-15 19:23:45.964915 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -762 finished +------------------------------ ---------------- +epoch -762 +replay_buffer/size 999047 +trainer/num train calls 239000 +trainer/QF1 Loss 0.654303 +trainer/QF2 Loss 0.689195 +trainer/Policy Loss 11.3156 +trainer/Q1 Predictions Mean -74.364 +trainer/Q1 Predictions Std 17.8041 +trainer/Q1 Predictions Max -1.01142 +trainer/Q1 Predictions Min -86.6408 +trainer/Q2 Predictions Mean -74.4574 +trainer/Q2 Predictions Std 17.86 +trainer/Q2 Predictions Max -0.717669 +trainer/Q2 Predictions Min -87.0106 +trainer/Q Targets Mean -74.5126 +trainer/Q Targets Std 17.9529 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1092 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00550988 +trainer/policy/mean Std 0.702184 +trainer/policy/mean Max 0.996435 +trainer/policy/mean Min -0.995819 +trainer/policy/std Mean 0.432124 +trainer/policy/std Std 0.0225111 +trainer/policy/std Max 0.454492 +trainer/policy/std Min 0.392552 +trainer/Advantage Weights Mean 2.55834 +trainer/Advantage Weights Std 13.2708 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.61547e-16 +trainer/Advantage Score Mean -0.3371 +trainer/Advantage Score Std 0.523599 +trainer/Advantage Score Max 1.55075 +trainer/Advantage Score Min -3.63617 +trainer/V1 Predictions Mean -74.2932 +trainer/V1 Predictions Std 17.9777 +trainer/V1 Predictions Max -0.704464 +trainer/V1 Predictions Min -86.8788 +trainer/VF Loss 0.0612547 +expl/num steps total 239000 +expl/num paths total 257 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.158891 +expl/Actions Std 0.821902 +expl/Actions Max 2.38347 +expl/Actions Min -2.31979 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 236240 +eval/num paths total 239 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.06163 +eval/Actions Std 0.663742 +eval/Actions Max 0.999008 +eval/Actions Min -0.999223 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.77255e-06 +time/evaluation sampling (s) 2.49979 +time/exploration sampling (s) 2.72658 +time/logging (s) 0.00671035 +time/saving (s) 0.00972557 +time/training (s) 12.4263 +time/epoch (s) 17.6691 +time/total (s) 4879.8 +Epoch -762 +------------------------------ ---------------- +2022-05-15 19:24:03.472686 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -761 finished +------------------------------ ---------------- +epoch -761 +replay_buffer/size 999047 +trainer/num train calls 240000 +trainer/QF1 Loss 0.64267 +trainer/QF2 Loss 0.592561 +trainer/Policy Loss 23.5061 +trainer/Q1 Predictions Mean -73.8383 +trainer/Q1 Predictions Std 18.781 +trainer/Q1 Predictions Max -0.29408 +trainer/Q1 Predictions Min -87.4478 +trainer/Q2 Predictions Mean -73.7449 +trainer/Q2 Predictions Std 18.7724 +trainer/Q2 Predictions Max -0.263524 +trainer/Q2 Predictions Min -86.5672 +trainer/Q Targets Mean -73.9479 +trainer/Q Targets Std 18.7621 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1393 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0108815 +trainer/policy/mean Std 0.708449 +trainer/policy/mean Max 0.997027 +trainer/policy/mean Min -0.997602 +trainer/policy/std Mean 0.431976 +trainer/policy/std Std 0.0227799 +trainer/policy/std Max 0.456778 +trainer/policy/std Min 0.393503 +trainer/Advantage Weights Mean 4.43365 +trainer/Advantage Weights Std 17.3616 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.01736e-13 +trainer/Advantage Score Mean -0.264498 +trainer/Advantage Score Std 0.491237 +trainer/Advantage Score Max 1.17583 +trainer/Advantage Score Min -2.92318 +trainer/V1 Predictions Mean -73.7014 +trainer/V1 Predictions Std 18.9927 +trainer/V1 Predictions Max 0.666145 +trainer/V1 Predictions Min -87.4706 +trainer/VF Loss 0.049573 +expl/num steps total 240000 +expl/num paths total 258 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0198954 +expl/Actions Std 0.812314 +expl/Actions Max 2.34212 +expl/Actions Min -2.29027 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 237240 +eval/num paths total 240 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.295019 +eval/Actions Std 0.67213 +eval/Actions Max 0.999281 +eval/Actions Min -0.996308 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.15113e-06 +time/evaluation sampling (s) 2.50992 +time/exploration sampling (s) 2.69242 +time/logging (s) 0.00912757 +time/saving (s) 0.0167833 +time/training (s) 12.2754 +time/epoch (s) 17.5037 +time/total (s) 4897.31 +Epoch -761 +------------------------------ ---------------- +2022-05-15 19:24:21.132940 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -760 finished +------------------------------ ---------------- +epoch -760 +replay_buffer/size 999047 +trainer/num train calls 241000 +trainer/QF1 Loss 1.55135 +trainer/QF2 Loss 1.56477 +trainer/Policy Loss 2.41332 +trainer/Q1 Predictions Mean -73.6179 +trainer/Q1 Predictions Std 19.4376 +trainer/Q1 Predictions Max -0.521508 +trainer/Q1 Predictions Min -87.4925 +trainer/Q2 Predictions Mean -73.5944 +trainer/Q2 Predictions Std 19.3964 +trainer/Q2 Predictions Max -0.779841 +trainer/Q2 Predictions Min -87.1874 +trainer/Q Targets Mean -72.6975 +trainer/Q Targets Std 19.8115 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1196 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0325868 +trainer/policy/mean Std 0.704841 +trainer/policy/mean Max 0.996467 +trainer/policy/mean Min -0.996565 +trainer/policy/std Mean 0.432337 +trainer/policy/std Std 0.0230487 +trainer/policy/std Max 0.458525 +trainer/policy/std Min 0.394271 +trainer/Advantage Weights Mean 0.442703 +trainer/Advantage Weights Std 6.23842 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.0156e-19 +trainer/Advantage Score Mean -0.797344 +trainer/Advantage Score Std 0.612941 +trainer/Advantage Score Max 1.50223 +trainer/Advantage Score Min -4.37336 +trainer/V1 Predictions Mean -72.5451 +trainer/V1 Predictions Std 19.7094 +trainer/V1 Predictions Max 1.14607 +trainer/V1 Predictions Min -87.0038 +trainer/VF Loss 0.108232 +expl/num steps total 241000 +expl/num paths total 260 +expl/path length Mean 500 +expl/path length Std 327 +expl/path length Max 827 +expl/path length Min 173 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0292059 +expl/Actions Std 0.807473 +expl/Actions Max 2.42448 +expl/Actions Min -2.24848 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 238240 +eval/num paths total 241 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.024338 +eval/Actions Std 0.723944 +eval/Actions Max 0.99937 +eval/Actions Min -0.999669 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.77814e-06 +time/evaluation sampling (s) 2.52001 +time/exploration sampling (s) 2.70609 +time/logging (s) 0.00723852 +time/saving (s) 0.0115797 +time/training (s) 12.4035 +time/epoch (s) 17.6485 +time/total (s) 4914.96 +Epoch -760 +------------------------------ ---------------- +2022-05-15 19:24:39.435279 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -759 finished +------------------------------ ---------------- +epoch -759 +replay_buffer/size 999047 +trainer/num train calls 242000 +trainer/QF1 Loss 0.623157 +trainer/QF2 Loss 0.66173 +trainer/Policy Loss 24.5728 +trainer/Q1 Predictions Mean -73.3584 +trainer/Q1 Predictions Std 18.4043 +trainer/Q1 Predictions Max -2.83303 +trainer/Q1 Predictions Min -86.1787 +trainer/Q2 Predictions Mean -73.406 +trainer/Q2 Predictions Std 18.3489 +trainer/Q2 Predictions Max -3.05537 +trainer/Q2 Predictions Min -86.5643 +trainer/Q Targets Mean -73.521 +trainer/Q Targets Std 18.3005 +trainer/Q Targets Max -2.04348 +trainer/Q Targets Min -86.5658 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.011252 +trainer/policy/mean Std 0.716476 +trainer/policy/mean Max 0.998261 +trainer/policy/mean Min -0.997757 +trainer/policy/std Mean 0.43065 +trainer/policy/std Std 0.021931 +trainer/policy/std Max 0.45823 +trainer/policy/std Min 0.396406 +trainer/Advantage Weights Mean 5.79227 +trainer/Advantage Weights Std 19.2991 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.52712e-09 +trainer/Advantage Score Mean -0.219501 +trainer/Advantage Score Std 0.443704 +trainer/Advantage Score Max 0.791924 +trainer/Advantage Score Min -1.92132 +trainer/V1 Predictions Mean -73.2511 +trainer/V1 Predictions Std 18.4838 +trainer/V1 Predictions Max -2.86578 +trainer/V1 Predictions Min -86.3543 +trainer/VF Loss 0.0418 +expl/num steps total 242000 +expl/num paths total 261 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.111457 +expl/Actions Std 0.83126 +expl/Actions Max 2.57531 +expl/Actions Min -2.2497 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 239240 +eval/num paths total 242 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0431204 +eval/Actions Std 0.714614 +eval/Actions Max 0.999336 +eval/Actions Min -0.999185 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.03192e-06 +time/evaluation sampling (s) 2.49958 +time/exploration sampling (s) 2.74376 +time/logging (s) 0.00742142 +time/saving (s) 0.0111163 +time/training (s) 13.0324 +time/epoch (s) 18.2943 +time/total (s) 4933.26 +Epoch -759 +------------------------------ ---------------- +2022-05-15 19:24:57.746937 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -758 finished +------------------------------ ---------------- +epoch -758 +replay_buffer/size 999047 +trainer/num train calls 243000 +trainer/QF1 Loss 0.749774 +trainer/QF2 Loss 0.853212 +trainer/Policy Loss 8.01376 +trainer/Q1 Predictions Mean -73.7017 +trainer/Q1 Predictions Std 17.8883 +trainer/Q1 Predictions Max -2.25607 +trainer/Q1 Predictions Min -86.5409 +trainer/Q2 Predictions Mean -73.7864 +trainer/Q2 Predictions Std 17.849 +trainer/Q2 Predictions Max -2.89374 +trainer/Q2 Predictions Min -86.488 +trainer/Q Targets Mean -73.571 +trainer/Q Targets Std 18.2344 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6005 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0132954 +trainer/policy/mean Std 0.713022 +trainer/policy/mean Max 0.997954 +trainer/policy/mean Min -0.995808 +trainer/policy/std Mean 0.430266 +trainer/policy/std Std 0.0215455 +trainer/policy/std Max 0.457652 +trainer/policy/std Min 0.392688 +trainer/Advantage Weights Mean 1.51289 +trainer/Advantage Weights Std 9.54356 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.1636e-16 +trainer/Advantage Score Mean -0.537441 +trainer/Advantage Score Std 0.577904 +trainer/Advantage Score Max 0.647819 +trainer/Advantage Score Min -3.56896 +trainer/V1 Predictions Mean -73.3313 +trainer/V1 Predictions Std 18.3511 +trainer/V1 Predictions Max -1.01214 +trainer/V1 Predictions Min -86.4575 +trainer/VF Loss 0.0666957 +expl/num steps total 243000 +expl/num paths total 263 +expl/path length Mean 500 +expl/path length Std 232 +expl/path length Max 732 +expl/path length Min 268 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0152201 +expl/Actions Std 0.810566 +expl/Actions Max 2.30382 +expl/Actions Min -2.4115 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 240240 +eval/num paths total 243 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0190442 +eval/Actions Std 0.726566 +eval/Actions Max 0.998104 +eval/Actions Min -0.999417 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.00026e-06 +time/evaluation sampling (s) 2.58358 +time/exploration sampling (s) 2.8127 +time/logging (s) 0.006882 +time/saving (s) 0.00981755 +time/training (s) 12.8914 +time/epoch (s) 18.3044 +time/total (s) 4951.57 +Epoch -758 +------------------------------ ---------------- +2022-05-15 19:25:15.806279 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -757 finished +------------------------------ ---------------- +epoch -757 +replay_buffer/size 999047 +trainer/num train calls 244000 +trainer/QF1 Loss 0.602548 +trainer/QF2 Loss 0.631946 +trainer/Policy Loss 6.29063 +trainer/Q1 Predictions Mean -73.8572 +trainer/Q1 Predictions Std 17.2647 +trainer/Q1 Predictions Max -1.54997 +trainer/Q1 Predictions Min -86.8798 +trainer/Q2 Predictions Mean -73.9943 +trainer/Q2 Predictions Std 17.222 +trainer/Q2 Predictions Max -1.28894 +trainer/Q2 Predictions Min -87.3108 +trainer/Q Targets Mean -73.9751 +trainer/Q Targets Std 17.1161 +trainer/Q Targets Max -3.3226 +trainer/Q Targets Min -87.4076 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00485191 +trainer/policy/mean Std 0.712592 +trainer/policy/mean Max 0.998892 +trainer/policy/mean Min -0.997263 +trainer/policy/std Mean 0.43075 +trainer/policy/std Std 0.0215247 +trainer/policy/std Max 0.455588 +trainer/policy/std Min 0.395447 +trainer/Advantage Weights Mean 2.04796 +trainer/Advantage Weights Std 12.5912 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.02786e-11 +trainer/Advantage Score Mean -0.547922 +trainer/Advantage Score Std 0.514113 +trainer/Advantage Score Max 1.41684 +trainer/Advantage Score Min -2.42206 +trainer/V1 Predictions Mean -73.7423 +trainer/V1 Predictions Std 17.1454 +trainer/V1 Predictions Max -3.04545 +trainer/V1 Predictions Min -87.3406 +trainer/VF Loss 0.0741976 +expl/num steps total 244000 +expl/num paths total 264 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0309851 +expl/Actions Std 0.82689 +expl/Actions Max 2.43691 +expl/Actions Min -2.53403 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 241240 +eval/num paths total 244 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0183365 +eval/Actions Std 0.722437 +eval/Actions Max 0.999909 +eval/Actions Min -0.998944 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.78419e-06 +time/evaluation sampling (s) 2.44026 +time/exploration sampling (s) 2.62296 +time/logging (s) 0.00800738 +time/saving (s) 0.0129774 +time/training (s) 12.9696 +time/epoch (s) 18.0538 +time/total (s) 4969.63 +Epoch -757 +------------------------------ ---------------- +2022-05-15 19:25:33.619261 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -756 finished +------------------------------ ---------------- +epoch -756 +replay_buffer/size 999047 +trainer/num train calls 245000 +trainer/QF1 Loss 0.498262 +trainer/QF2 Loss 0.491007 +trainer/Policy Loss 37.4189 +trainer/Q1 Predictions Mean -73.1578 +trainer/Q1 Predictions Std 19.0611 +trainer/Q1 Predictions Max -0.324043 +trainer/Q1 Predictions Min -87.0983 +trainer/Q2 Predictions Mean -73.2293 +trainer/Q2 Predictions Std 19.1359 +trainer/Q2 Predictions Max -0.263206 +trainer/Q2 Predictions Min -87.3301 +trainer/Q Targets Mean -73.0968 +trainer/Q Targets Std 19.3369 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8362 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00754054 +trainer/policy/mean Std 0.703962 +trainer/policy/mean Max 0.997315 +trainer/policy/mean Min -0.995682 +trainer/policy/std Mean 0.431057 +trainer/policy/std Std 0.0219857 +trainer/policy/std Max 0.45514 +trainer/policy/std Min 0.396547 +trainer/Advantage Weights Mean 6.53021 +trainer/Advantage Weights Std 20.2002 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.91942e-13 +trainer/Advantage Score Mean -0.221823 +trainer/Advantage Score Std 0.480061 +trainer/Advantage Score Max 1.44504 +trainer/Advantage Score Min -2.83404 +trainer/V1 Predictions Mean -72.9179 +trainer/V1 Predictions Std 19.1595 +trainer/V1 Predictions Max -0.315891 +trainer/V1 Predictions Min -86.9099 +trainer/VF Loss 0.0523961 +expl/num steps total 245000 +expl/num paths total 266 +expl/path length Mean 500 +expl/path length Std 146 +expl/path length Max 646 +expl/path length Min 354 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0292432 +expl/Actions Std 0.826991 +expl/Actions Max 2.35054 +expl/Actions Min -2.25595 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 242179 +eval/num paths total 245 +eval/path length Mean 939 +eval/path length Std 0 +eval/path length Max 939 +eval/path length Min 939 +eval/Rewards Mean 0.00106496 +eval/Rewards Std 0.0326164 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0587301 +eval/Actions Std 0.717467 +eval/Actions Max 0.99885 +eval/Actions Min -0.999521 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.48104e-06 +time/evaluation sampling (s) 2.43298 +time/exploration sampling (s) 2.62491 +time/logging (s) 0.00780209 +time/saving (s) 0.00959562 +time/training (s) 12.7295 +time/epoch (s) 17.8048 +time/total (s) 4987.44 +Epoch -756 +------------------------------ ---------------- +2022-05-15 19:25:51.602206 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -755 finished +------------------------------ ---------------- +epoch -755 +replay_buffer/size 999047 +trainer/num train calls 246000 +trainer/QF1 Loss 1.13568 +trainer/QF2 Loss 1.13983 +trainer/Policy Loss 0.219364 +trainer/Q1 Predictions Mean -72.5818 +trainer/Q1 Predictions Std 21.182 +trainer/Q1 Predictions Max -0.348988 +trainer/Q1 Predictions Min -87.1625 +trainer/Q2 Predictions Mean -72.627 +trainer/Q2 Predictions Std 21.1675 +trainer/Q2 Predictions Max -0.473316 +trainer/Q2 Predictions Min -87.4246 +trainer/Q Targets Mean -72.1853 +trainer/Q Targets Std 20.7883 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3503 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00252616 +trainer/policy/mean Std 0.72836 +trainer/policy/mean Max 0.998984 +trainer/policy/mean Min -0.998927 +trainer/policy/std Mean 0.43204 +trainer/policy/std Std 0.0218136 +trainer/policy/std Max 0.454703 +trainer/policy/std Min 0.397401 +trainer/Advantage Weights Mean 0.0758395 +trainer/Advantage Weights Std 0.922828 +trainer/Advantage Weights Max 14.7436 +trainer/Advantage Weights Min 2.82595e-14 +trainer/Advantage Score Mean -0.777734 +trainer/Advantage Score Std 0.442071 +trainer/Advantage Score Max 0.269081 +trainer/Advantage Score Min -3.11973 +trainer/V1 Predictions Mean -71.8881 +trainer/V1 Predictions Std 21.0236 +trainer/V1 Predictions Max -0.0962909 +trainer/V1 Predictions Min -86.4179 +trainer/VF Loss 0.0802577 +expl/num steps total 246000 +expl/num paths total 267 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0401396 +expl/Actions Std 0.792038 +expl/Actions Max 2.37762 +expl/Actions Min -2.32232 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 243179 +eval/num paths total 246 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0396831 +eval/Actions Std 0.700977 +eval/Actions Max 0.999461 +eval/Actions Min -0.998125 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66917e-06 +time/evaluation sampling (s) 2.45435 +time/exploration sampling (s) 2.61964 +time/logging (s) 0.0111787 +time/saving (s) 0.0155716 +time/training (s) 12.8791 +time/epoch (s) 17.9798 +time/total (s) 5005.42 +Epoch -755 +------------------------------ ---------------- +2022-05-15 19:26:09.539031 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -754 finished +------------------------------ ---------------- +epoch -754 +replay_buffer/size 999047 +trainer/num train calls 247000 +trainer/QF1 Loss 0.574728 +trainer/QF2 Loss 0.510149 +trainer/Policy Loss 3.75117 +trainer/Q1 Predictions Mean -74.941 +trainer/Q1 Predictions Std 17.5909 +trainer/Q1 Predictions Max -1.28448 +trainer/Q1 Predictions Min -87.3737 +trainer/Q2 Predictions Mean -74.8407 +trainer/Q2 Predictions Std 17.6424 +trainer/Q2 Predictions Max -1.20751 +trainer/Q2 Predictions Min -87.4285 +trainer/Q Targets Mean -74.8233 +trainer/Q Targets Std 17.5169 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9638 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0205378 +trainer/policy/mean Std 0.710344 +trainer/policy/mean Max 0.999278 +trainer/policy/mean Min -0.997682 +trainer/policy/std Mean 0.431647 +trainer/policy/std Std 0.0216076 +trainer/policy/std Max 0.45502 +trainer/policy/std Min 0.397247 +trainer/Advantage Weights Mean 1.03263 +trainer/Advantage Weights Std 7.16583 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.02472e-12 +trainer/Advantage Score Mean -0.447297 +trainer/Advantage Score Std 0.398618 +trainer/Advantage Score Max 0.748515 +trainer/Advantage Score Min -2.62386 +trainer/V1 Predictions Mean -74.5521 +trainer/V1 Predictions Std 17.7322 +trainer/V1 Predictions Max -0.648274 +trainer/V1 Predictions Min -86.8298 +trainer/VF Loss 0.0393542 +expl/num steps total 247000 +expl/num paths total 268 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0231374 +expl/Actions Std 0.826142 +expl/Actions Max 2.27426 +expl/Actions Min -2.30147 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 244179 +eval/num paths total 247 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0432807 +eval/Actions Std 0.731867 +eval/Actions Max 0.998519 +eval/Actions Min -0.998727 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.119e-06 +time/evaluation sampling (s) 2.46003 +time/exploration sampling (s) 2.6479 +time/logging (s) 0.00687495 +time/saving (s) 0.0097378 +time/training (s) 12.7998 +time/epoch (s) 17.9243 +time/total (s) 5023.35 +Epoch -754 +------------------------------ ---------------- +2022-05-15 19:26:27.693401 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -753 finished +------------------------------ ---------------- +epoch -753 +replay_buffer/size 999047 +trainer/num train calls 248000 +trainer/QF1 Loss 0.558604 +trainer/QF2 Loss 0.526189 +trainer/Policy Loss 11.2312 +trainer/Q1 Predictions Mean -74.8605 +trainer/Q1 Predictions Std 16.9179 +trainer/Q1 Predictions Max -1.86102 +trainer/Q1 Predictions Min -87.4609 +trainer/Q2 Predictions Mean -74.9431 +trainer/Q2 Predictions Std 16.8503 +trainer/Q2 Predictions Max -1.6796 +trainer/Q2 Predictions Min -87.3782 +trainer/Q Targets Mean -74.7854 +trainer/Q Targets Std 16.8051 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8064 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00653627 +trainer/policy/mean Std 0.720195 +trainer/policy/mean Max 0.999201 +trainer/policy/mean Min -0.997482 +trainer/policy/std Mean 0.430487 +trainer/policy/std Std 0.0214674 +trainer/policy/std Max 0.457807 +trainer/policy/std Min 0.396085 +trainer/Advantage Weights Mean 2.72811 +trainer/Advantage Weights Std 13.9202 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.75055e-10 +trainer/Advantage Score Mean -0.333008 +trainer/Advantage Score Std 0.356562 +trainer/Advantage Score Max 0.598892 +trainer/Advantage Score Min -2.14676 +trainer/V1 Predictions Mean -74.5858 +trainer/V1 Predictions Std 16.7445 +trainer/V1 Predictions Max -2.2359 +trainer/V1 Predictions Min -86.6815 +trainer/VF Loss 0.030582 +expl/num steps total 248000 +expl/num paths total 270 +expl/path length Mean 500 +expl/path length Std 470 +expl/path length Max 970 +expl/path length Min 30 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0283727 +expl/Actions Std 0.797601 +expl/Actions Max 2.31535 +expl/Actions Min -2.29606 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 245179 +eval/num paths total 248 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0212447 +eval/Actions Std 0.73089 +eval/Actions Max 0.998826 +eval/Actions Min -0.999282 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.82191e-06 +time/evaluation sampling (s) 2.40645 +time/exploration sampling (s) 2.6555 +time/logging (s) 0.00739511 +time/saving (s) 0.0104306 +time/training (s) 13.0686 +time/epoch (s) 18.1484 +time/total (s) 5041.5 +Epoch -753 +------------------------------ ---------------- +2022-05-15 19:26:45.722514 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -752 finished +------------------------------ ---------------- +epoch -752 +replay_buffer/size 999047 +trainer/num train calls 249000 +trainer/QF1 Loss 0.843079 +trainer/QF2 Loss 0.804246 +trainer/Policy Loss 19.4216 +trainer/Q1 Predictions Mean -71.7453 +trainer/Q1 Predictions Std 20.5011 +trainer/Q1 Predictions Max -0.305269 +trainer/Q1 Predictions Min -87.0569 +trainer/Q2 Predictions Mean -71.7397 +trainer/Q2 Predictions Std 20.5077 +trainer/Q2 Predictions Max -0.26726 +trainer/Q2 Predictions Min -86.9822 +trainer/Q Targets Mean -71.9717 +trainer/Q Targets Std 20.3489 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8866 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0107721 +trainer/policy/mean Std 0.719367 +trainer/policy/mean Max 0.998975 +trainer/policy/mean Min -0.996431 +trainer/policy/std Mean 0.432434 +trainer/policy/std Std 0.0211428 +trainer/policy/std Max 0.457144 +trainer/policy/std Min 0.39775 +trainer/Advantage Weights Mean 4.11467 +trainer/Advantage Weights Std 17.9548 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.04528e-15 +trainer/Advantage Score Mean -0.433402 +trainer/Advantage Score Std 0.599351 +trainer/Advantage Score Max 1.83627 +trainer/Advantage Score Min -3.38232 +trainer/V1 Predictions Mean -71.6926 +trainer/V1 Predictions Std 20.5602 +trainer/V1 Predictions Max 0.687963 +trainer/V1 Predictions Min -87.5344 +trainer/VF Loss 0.0880167 +expl/num steps total 249000 +expl/num paths total 271 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.097333 +expl/Actions Std 0.844679 +expl/Actions Max 2.3938 +expl/Actions Min -2.38669 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 246179 +eval/num paths total 249 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.049906 +eval/Actions Std 0.715687 +eval/Actions Max 0.999912 +eval/Actions Min -0.999382 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.12226e-06 +time/evaluation sampling (s) 2.37237 +time/exploration sampling (s) 2.71504 +time/logging (s) 0.00669309 +time/saving (s) 0.00975286 +time/training (s) 12.9179 +time/epoch (s) 18.0218 +time/total (s) 5059.53 +Epoch -752 +------------------------------ ---------------- +2022-05-15 19:27:02.680152 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -751 finished +------------------------------ ---------------- +epoch -751 +replay_buffer/size 999047 +trainer/num train calls 250000 +trainer/QF1 Loss 0.860129 +trainer/QF2 Loss 0.711119 +trainer/Policy Loss 2.06239 +trainer/Q1 Predictions Mean -72.1608 +trainer/Q1 Predictions Std 20.642 +trainer/Q1 Predictions Max -0.719713 +trainer/Q1 Predictions Min -86.5995 +trainer/Q2 Predictions Mean -72.0826 +trainer/Q2 Predictions Std 20.6452 +trainer/Q2 Predictions Max -0.566998 +trainer/Q2 Predictions Min -86.7458 +trainer/Q Targets Mean -71.8885 +trainer/Q Targets Std 20.5535 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4352 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0156263 +trainer/policy/mean Std 0.711574 +trainer/policy/mean Max 0.997694 +trainer/policy/mean Min -0.999399 +trainer/policy/std Mean 0.431965 +trainer/policy/std Std 0.0214944 +trainer/policy/std Max 0.457216 +trainer/policy/std Min 0.396046 +trainer/Advantage Weights Mean 1.00141 +trainer/Advantage Weights Std 8.82333 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.01284e-13 +trainer/Advantage Score Mean -0.50445 +trainer/Advantage Score Std 0.458827 +trainer/Advantage Score Max 0.550156 +trainer/Advantage Score Min -2.99208 +trainer/V1 Predictions Mean -71.653 +trainer/V1 Predictions Std 20.6404 +trainer/V1 Predictions Max -0.949932 +trainer/V1 Predictions Min -86.1035 +trainer/VF Loss 0.048704 +expl/num steps total 250000 +expl/num paths total 272 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0468695 +expl/Actions Std 0.8053 +expl/Actions Max 2.41599 +expl/Actions Min -2.38844 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 247179 +eval/num paths total 250 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0623881 +eval/Actions Std 0.707085 +eval/Actions Max 0.997793 +eval/Actions Min -0.999595 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.72738e-06 +time/evaluation sampling (s) 2.27681 +time/exploration sampling (s) 2.68836 +time/logging (s) 0.00671792 +time/saving (s) 0.0097512 +time/training (s) 11.9691 +time/epoch (s) 16.9507 +time/total (s) 5076.48 +Epoch -751 +------------------------------ ---------------- +2022-05-15 19:27:20.169288 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -750 finished +------------------------------ ---------------- +epoch -750 +replay_buffer/size 999047 +trainer/num train calls 251000 +trainer/QF1 Loss 1.22535 +trainer/QF2 Loss 1.12385 +trainer/Policy Loss 12.2275 +trainer/Q1 Predictions Mean -71.457 +trainer/Q1 Predictions Std 20.4737 +trainer/Q1 Predictions Max -0.68988 +trainer/Q1 Predictions Min -87.1066 +trainer/Q2 Predictions Mean -71.4858 +trainer/Q2 Predictions Std 20.5449 +trainer/Q2 Predictions Max -0.507579 +trainer/Q2 Predictions Min -86.9462 +trainer/Q Targets Mean -71.6183 +trainer/Q Targets Std 20.6261 +trainer/Q Targets Max 0.128438 +trainer/Q Targets Min -87.1193 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0278693 +trainer/policy/mean Std 0.707455 +trainer/policy/mean Max 0.998289 +trainer/policy/mean Min -0.999246 +trainer/policy/std Mean 0.431865 +trainer/policy/std Std 0.0209454 +trainer/policy/std Max 0.457287 +trainer/policy/std Min 0.398242 +trainer/Advantage Weights Mean 2.5503 +trainer/Advantage Weights Std 12.4812 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.58135e-18 +trainer/Advantage Score Mean -0.376147 +trainer/Advantage Score Std 0.597696 +trainer/Advantage Score Max 0.989456 +trainer/Advantage Score Min -4.04982 +trainer/V1 Predictions Mean -71.3657 +trainer/V1 Predictions Std 20.7938 +trainer/V1 Predictions Max 0.433948 +trainer/V1 Predictions Min -86.9966 +trainer/VF Loss 0.0587674 +expl/num steps total 251000 +expl/num paths total 273 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0392949 +expl/Actions Std 0.814508 +expl/Actions Max 2.44031 +expl/Actions Min -2.74691 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 248085 +eval/num paths total 251 +eval/path length Mean 906 +eval/path length Std 0 +eval/path length Max 906 +eval/path length Min 906 +eval/Rewards Mean 0.00110375 +eval/Rewards Std 0.0332044 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0337971 +eval/Actions Std 0.72706 +eval/Actions Max 0.998391 +eval/Actions Min -0.999438 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.86014e-06 +time/evaluation sampling (s) 2.34911 +time/exploration sampling (s) 2.67464 +time/logging (s) 0.00653045 +time/saving (s) 0.0188308 +time/training (s) 12.4333 +time/epoch (s) 17.4824 +time/total (s) 5093.97 +Epoch -750 +------------------------------ ---------------- +2022-05-15 19:27:37.942178 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -749 finished +------------------------------ ---------------- +epoch -749 +replay_buffer/size 999047 +trainer/num train calls 252000 +trainer/QF1 Loss 0.831005 +trainer/QF2 Loss 0.837662 +trainer/Policy Loss 28.3632 +trainer/Q1 Predictions Mean -73.9883 +trainer/Q1 Predictions Std 17.4313 +trainer/Q1 Predictions Max -1.46174 +trainer/Q1 Predictions Min -86.4196 +trainer/Q2 Predictions Mean -74.0319 +trainer/Q2 Predictions Std 17.4384 +trainer/Q2 Predictions Max -1.09095 +trainer/Q2 Predictions Min -86.6031 +trainer/Q Targets Mean -73.985 +trainer/Q Targets Std 17.4861 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8111 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.036075 +trainer/policy/mean Std 0.708023 +trainer/policy/mean Max 0.998792 +trainer/policy/mean Min -0.996358 +trainer/policy/std Mean 0.430251 +trainer/policy/std Std 0.0213693 +trainer/policy/std Max 0.452852 +trainer/policy/std Min 0.393913 +trainer/Advantage Weights Mean 5.15169 +trainer/Advantage Weights Std 18.4384 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.4934e-19 +trainer/Advantage Score Mean -0.291954 +trainer/Advantage Score Std 0.58285 +trainer/Advantage Score Max 1.48148 +trainer/Advantage Score Min -4.33481 +trainer/V1 Predictions Mean -73.7486 +trainer/V1 Predictions Std 17.6329 +trainer/V1 Predictions Max -1.06292 +trainer/V1 Predictions Min -86.6353 +trainer/VF Loss 0.0717303 +expl/num steps total 252000 +expl/num paths total 274 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0387517 +expl/Actions Std 0.835938 +expl/Actions Max 2.35431 +expl/Actions Min -2.3392 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 249085 +eval/num paths total 252 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.275376 +eval/Actions Std 0.72762 +eval/Actions Max 0.998726 +eval/Actions Min -0.998555 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.13576e-06 +time/evaluation sampling (s) 2.44179 +time/exploration sampling (s) 2.71384 +time/logging (s) 0.0110517 +time/saving (s) 0.0152713 +time/training (s) 12.5891 +time/epoch (s) 17.7711 +time/total (s) 5111.75 +Epoch -749 +------------------------------ ---------------- +2022-05-15 19:27:55.922222 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -748 finished +------------------------------ ---------------- +epoch -748 +replay_buffer/size 999047 +trainer/num train calls 253000 +trainer/QF1 Loss 0.481858 +trainer/QF2 Loss 0.470351 +trainer/Policy Loss 11.0859 +trainer/Q1 Predictions Mean -74.3523 +trainer/Q1 Predictions Std 17.263 +trainer/Q1 Predictions Max -0.413904 +trainer/Q1 Predictions Min -86.2362 +trainer/Q2 Predictions Mean -74.4406 +trainer/Q2 Predictions Std 17.2228 +trainer/Q2 Predictions Max -0.499177 +trainer/Q2 Predictions Min -86.9096 +trainer/Q Targets Mean -74.2276 +trainer/Q Targets Std 17.3626 +trainer/Q Targets Max 0.529781 +trainer/Q Targets Min -86.4027 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.038105 +trainer/policy/mean Std 0.717863 +trainer/policy/mean Max 0.998667 +trainer/policy/mean Min -0.998191 +trainer/policy/std Mean 0.431359 +trainer/policy/std Std 0.0221925 +trainer/policy/std Max 0.455318 +trainer/policy/std Min 0.394521 +trainer/Advantage Weights Mean 2.00883 +trainer/Advantage Weights Std 11.5346 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.78985e-22 +trainer/Advantage Score Mean -0.523429 +trainer/Advantage Score Std 0.560499 +trainer/Advantage Score Max 0.585899 +trainer/Advantage Score Min -5.00747 +trainer/V1 Predictions Mean -74.0014 +trainer/V1 Predictions Std 17.4766 +trainer/V1 Predictions Max 1.50551 +trainer/V1 Predictions Min -86.464 +trainer/VF Loss 0.0635922 +expl/num steps total 253000 +expl/num paths total 275 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0712664 +expl/Actions Std 0.836678 +expl/Actions Max 2.31369 +expl/Actions Min -2.34954 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 250085 +eval/num paths total 253 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0496254 +eval/Actions Std 0.702155 +eval/Actions Max 0.999038 +eval/Actions Min -0.998865 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83308e-06 +time/evaluation sampling (s) 2.32774 +time/exploration sampling (s) 2.69271 +time/logging (s) 0.0143917 +time/saving (s) 0.0197544 +time/training (s) 12.9205 +time/epoch (s) 17.9751 +time/total (s) 5129.73 +Epoch -748 +------------------------------ ---------------- +2022-05-15 19:28:13.607428 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -747 finished +------------------------------ ---------------- +epoch -747 +replay_buffer/size 999047 +trainer/num train calls 254000 +trainer/QF1 Loss 0.914444 +trainer/QF2 Loss 0.800938 +trainer/Policy Loss 17.0311 +trainer/Q1 Predictions Mean -72.1016 +trainer/Q1 Predictions Std 20.4075 +trainer/Q1 Predictions Max -0.43466 +trainer/Q1 Predictions Min -86.597 +trainer/Q2 Predictions Mean -72.2081 +trainer/Q2 Predictions Std 20.4028 +trainer/Q2 Predictions Max -0.493681 +trainer/Q2 Predictions Min -86.3773 +trainer/Q Targets Mean -72.4122 +trainer/Q Targets Std 20.1446 +trainer/Q Targets Max -0.548784 +trainer/Q Targets Min -86.7718 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0163861 +trainer/policy/mean Std 0.719666 +trainer/policy/mean Max 0.997929 +trainer/policy/mean Min -0.999781 +trainer/policy/std Mean 0.431883 +trainer/policy/std Std 0.0230019 +trainer/policy/std Max 0.458151 +trainer/policy/std Min 0.394638 +trainer/Advantage Weights Mean 5.68284 +trainer/Advantage Weights Std 20.094 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.88689e-09 +trainer/Advantage Score Mean -0.271695 +trainer/Advantage Score Std 0.457807 +trainer/Advantage Score Max 1.37068 +trainer/Advantage Score Min -1.96631 +trainer/V1 Predictions Mean -72.1176 +trainer/V1 Predictions Std 20.3485 +trainer/V1 Predictions Max 1.21435 +trainer/V1 Predictions Min -86.9117 +trainer/VF Loss 0.0488771 +expl/num steps total 254000 +expl/num paths total 276 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.152707 +expl/Actions Std 0.819143 +expl/Actions Max 2.37515 +expl/Actions Min -2.15711 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 250774 +eval/num paths total 254 +eval/path length Mean 689 +eval/path length Std 0 +eval/path length Max 689 +eval/path length Min 689 +eval/Rewards Mean 0.00145138 +eval/Rewards Std 0.0380693 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0257932 +eval/Actions Std 0.723354 +eval/Actions Max 0.998389 +eval/Actions Min -0.999552 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.89315e-06 +time/evaluation sampling (s) 2.36057 +time/exploration sampling (s) 2.77948 +time/logging (s) 0.00566694 +time/saving (s) 0.00940362 +time/training (s) 12.5072 +time/epoch (s) 17.6623 +time/total (s) 5147.4 +Epoch -747 +------------------------------ ---------------- +2022-05-15 19:28:31.394198 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -746 finished +------------------------------ ---------------- +epoch -746 +replay_buffer/size 999047 +trainer/num train calls 255000 +trainer/QF1 Loss 0.613255 +trainer/QF2 Loss 0.577687 +trainer/Policy Loss 22.0741 +trainer/Q1 Predictions Mean -73.6934 +trainer/Q1 Predictions Std 17.1434 +trainer/Q1 Predictions Max -3.71919 +trainer/Q1 Predictions Min -86.9135 +trainer/Q2 Predictions Mean -73.7351 +trainer/Q2 Predictions Std 17.2211 +trainer/Q2 Predictions Max -3.69229 +trainer/Q2 Predictions Min -87.2019 +trainer/Q Targets Mean -73.7611 +trainer/Q Targets Std 17.2692 +trainer/Q Targets Max -2.82917 +trainer/Q Targets Min -87.2966 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.009028 +trainer/policy/mean Std 0.698751 +trainer/policy/mean Max 0.999313 +trainer/policy/mean Min -0.999351 +trainer/policy/std Mean 0.430415 +trainer/policy/std Std 0.0230946 +trainer/policy/std Max 0.458033 +trainer/policy/std Min 0.391827 +trainer/Advantage Weights Mean 4.0633 +trainer/Advantage Weights Std 16.7399 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.59967e-17 +trainer/Advantage Score Mean -0.320395 +trainer/Advantage Score Std 0.458079 +trainer/Advantage Score Max 0.938459 +trainer/Advantage Score Min -3.72569 +trainer/V1 Predictions Mean -73.5289 +trainer/V1 Predictions Std 17.3663 +trainer/V1 Predictions Max -2.75009 +trainer/V1 Predictions Min -87.219 +trainer/VF Loss 0.0440876 +expl/num steps total 255000 +expl/num paths total 277 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0342909 +expl/Actions Std 0.827924 +expl/Actions Max 2.21691 +expl/Actions Min -2.58709 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 251774 +eval/num paths total 255 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0175738 +eval/Actions Std 0.725856 +eval/Actions Max 0.999936 +eval/Actions Min -0.998753 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.14694e-06 +time/evaluation sampling (s) 2.28971 +time/exploration sampling (s) 2.78853 +time/logging (s) 0.00772283 +time/saving (s) 0.0106553 +time/training (s) 12.6862 +time/epoch (s) 17.7828 +time/total (s) 5165.18 +Epoch -746 +------------------------------ ---------------- +2022-05-15 19:28:49.780145 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -745 finished +------------------------------ ---------------- +epoch -745 +replay_buffer/size 999047 +trainer/num train calls 256000 +trainer/QF1 Loss 1.38508 +trainer/QF2 Loss 1.25359 +trainer/Policy Loss 32.8871 +trainer/Q1 Predictions Mean -74.2239 +trainer/Q1 Predictions Std 17.4474 +trainer/Q1 Predictions Max -0.718614 +trainer/Q1 Predictions Min -86.896 +trainer/Q2 Predictions Mean -74.2094 +trainer/Q2 Predictions Std 17.4376 +trainer/Q2 Predictions Max -0.749992 +trainer/Q2 Predictions Min -86.9391 +trainer/Q Targets Mean -74.7623 +trainer/Q Targets Std 17.857 +trainer/Q Targets Max 1.27249 +trainer/Q Targets Min -87.2527 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00920611 +trainer/policy/mean Std 0.715435 +trainer/policy/mean Max 0.998947 +trainer/policy/mean Min -0.997024 +trainer/policy/std Mean 0.432059 +trainer/policy/std Std 0.0228297 +trainer/policy/std Max 0.457495 +trainer/policy/std Min 0.393294 +trainer/Advantage Weights Mean 7.18959 +trainer/Advantage Weights Std 20.825 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.73156e-09 +trainer/Advantage Score Mean -0.103523 +trainer/Advantage Score Std 0.403078 +trainer/Advantage Score Max 1.10789 +trainer/Advantage Score Min -2.01742 +trainer/V1 Predictions Mean -74.5572 +trainer/V1 Predictions Std 17.7802 +trainer/V1 Predictions Max -0.0482664 +trainer/V1 Predictions Min -87.1235 +trainer/VF Loss 0.0460414 +expl/num steps total 256000 +expl/num paths total 278 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0333057 +expl/Actions Std 0.834508 +expl/Actions Max 2.3237 +expl/Actions Min -2.54261 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 252525 +eval/num paths total 256 +eval/path length Mean 751 +eval/path length Std 0 +eval/path length Max 751 +eval/path length Min 751 +eval/Rewards Mean 0.00133156 +eval/Rewards Std 0.0364662 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0462456 +eval/Actions Std 0.736036 +eval/Actions Max 0.998919 +eval/Actions Min -0.998071 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.01609e-06 +time/evaluation sampling (s) 2.31434 +time/exploration sampling (s) 2.78245 +time/logging (s) 0.00707941 +time/saving (s) 0.0123034 +time/training (s) 13.2625 +time/epoch (s) 18.3787 +time/total (s) 5183.57 +Epoch -745 +------------------------------ ---------------- +2022-05-15 19:29:07.724218 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -744 finished +------------------------------ ---------------- +epoch -744 +replay_buffer/size 999047 +trainer/num train calls 257000 +trainer/QF1 Loss 1.32483 +trainer/QF2 Loss 1.33977 +trainer/Policy Loss 22.2223 +trainer/Q1 Predictions Mean -73.7797 +trainer/Q1 Predictions Std 18.5333 +trainer/Q1 Predictions Max -1.36887 +trainer/Q1 Predictions Min -86.3984 +trainer/Q2 Predictions Mean -73.8594 +trainer/Q2 Predictions Std 18.5063 +trainer/Q2 Predictions Max -1.4856 +trainer/Q2 Predictions Min -86.3434 +trainer/Q Targets Mean -74.146 +trainer/Q Targets Std 18.8949 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4885 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.020578 +trainer/policy/mean Std 0.705274 +trainer/policy/mean Max 0.998221 +trainer/policy/mean Min -0.997518 +trainer/policy/std Mean 0.43209 +trainer/policy/std Std 0.022819 +trainer/policy/std Max 0.458644 +trainer/policy/std Min 0.393689 +trainer/Advantage Weights Mean 5.48071 +trainer/Advantage Weights Std 17.1457 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.20786e-12 +trainer/Advantage Score Mean -0.185574 +trainer/Advantage Score Std 0.451082 +trainer/Advantage Score Max 1.1114 +trainer/Advantage Score Min -2.64654 +trainer/V1 Predictions Mean -73.9564 +trainer/V1 Predictions Std 18.7487 +trainer/V1 Predictions Max -1.37079 +trainer/V1 Predictions Min -86.5868 +trainer/VF Loss 0.0432295 +expl/num steps total 257000 +expl/num paths total 280 +expl/path length Mean 500 +expl/path length Std 479 +expl/path length Max 979 +expl/path length Min 21 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0319501 +expl/Actions Std 0.824956 +expl/Actions Max 2.43745 +expl/Actions Min -2.27348 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 253525 +eval/num paths total 257 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.230058 +eval/Actions Std 0.645624 +eval/Actions Max 0.998469 +eval/Actions Min -0.997469 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.7339e-06 +time/evaluation sampling (s) 2.37599 +time/exploration sampling (s) 2.86513 +time/logging (s) 0.00741721 +time/saving (s) 0.0106476 +time/training (s) 12.6774 +time/epoch (s) 17.9365 +time/total (s) 5201.51 +Epoch -744 +------------------------------ ---------------- +2022-05-15 19:29:26.230653 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -743 finished +------------------------------ ---------------- +epoch -743 +replay_buffer/size 999047 +trainer/num train calls 258000 +trainer/QF1 Loss 1.56491 +trainer/QF2 Loss 1.65013 +trainer/Policy Loss 48.599 +trainer/Q1 Predictions Mean -72.6307 +trainer/Q1 Predictions Std 18.8807 +trainer/Q1 Predictions Max -0.903055 +trainer/Q1 Predictions Min -87.6204 +trainer/Q2 Predictions Mean -72.5938 +trainer/Q2 Predictions Std 18.8317 +trainer/Q2 Predictions Max -1.31658 +trainer/Q2 Predictions Min -87.6025 +trainer/Q Targets Mean -72.5191 +trainer/Q Targets Std 18.5258 +trainer/Q Targets Max -3.31731 +trainer/Q Targets Min -86.4521 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0143502 +trainer/policy/mean Std 0.710743 +trainer/policy/mean Max 0.997774 +trainer/policy/mean Min -0.997191 +trainer/policy/std Mean 0.432292 +trainer/policy/std Std 0.0223927 +trainer/policy/std Max 0.455604 +trainer/policy/std Min 0.393913 +trainer/Advantage Weights Mean 10.5655 +trainer/Advantage Weights Std 27.2941 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.37405e-14 +trainer/Advantage Score Mean -0.367348 +trainer/Advantage Score Std 0.645341 +trainer/Advantage Score Max 1.75731 +trainer/Advantage Score Min -3.13716 +trainer/V1 Predictions Mean -72.3017 +trainer/V1 Predictions Std 18.6136 +trainer/V1 Predictions Max -1.62016 +trainer/V1 Predictions Min -86.3038 +trainer/VF Loss 0.116936 +expl/num steps total 258000 +expl/num paths total 282 +expl/path length Mean 500 +expl/path length Std 465 +expl/path length Max 965 +expl/path length Min 35 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.052561 +expl/Actions Std 0.825851 +expl/Actions Max 2.35143 +expl/Actions Min -2.23398 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 254472 +eval/num paths total 258 +eval/path length Mean 947 +eval/path length Std 0 +eval/path length Max 947 +eval/path length Min 947 +eval/Rewards Mean 0.00105597 +eval/Rewards Std 0.0324785 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0302633 +eval/Actions Std 0.694013 +eval/Actions Max 0.999258 +eval/Actions Min -0.999473 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.88477e-06 +time/evaluation sampling (s) 2.35245 +time/exploration sampling (s) 2.86963 +time/logging (s) 0.00869739 +time/saving (s) 0.0140396 +time/training (s) 13.2544 +time/epoch (s) 18.4992 +time/total (s) 5220.01 +Epoch -743 +------------------------------ ---------------- +2022-05-15 19:29:44.351714 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -742 finished +------------------------------ ---------------- +epoch -742 +replay_buffer/size 999047 +trainer/num train calls 259000 +trainer/QF1 Loss 1.00333 +trainer/QF2 Loss 1.00124 +trainer/Policy Loss 8.11689 +trainer/Q1 Predictions Mean -72.0428 +trainer/Q1 Predictions Std 20.4619 +trainer/Q1 Predictions Max -1.22096 +trainer/Q1 Predictions Min -87.9381 +trainer/Q2 Predictions Mean -72.0791 +trainer/Q2 Predictions Std 20.3654 +trainer/Q2 Predictions Max -1.06472 +trainer/Q2 Predictions Min -87.1974 +trainer/Q Targets Mean -72.2252 +trainer/Q Targets Std 20.5422 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9951 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00403227 +trainer/policy/mean Std 0.721255 +trainer/policy/mean Max 0.998166 +trainer/policy/mean Min -0.997251 +trainer/policy/std Mean 0.431811 +trainer/policy/std Std 0.0217436 +trainer/policy/std Max 0.455416 +trainer/policy/std Min 0.393526 +trainer/Advantage Weights Mean 2.04114 +trainer/Advantage Weights Std 10.9843 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.07048e-15 +trainer/Advantage Score Mean -0.400818 +trainer/Advantage Score Std 0.522385 +trainer/Advantage Score Max 0.876167 +trainer/Advantage Score Min -3.3811 +trainer/V1 Predictions Mean -72.0298 +trainer/V1 Predictions Std 20.402 +trainer/V1 Predictions Max -0.0947652 +trainer/V1 Predictions Min -86.7538 +trainer/VF Loss 0.0506153 +expl/num steps total 259000 +expl/num paths total 283 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.061803 +expl/Actions Std 0.80551 +expl/Actions Max 2.34852 +expl/Actions Min -2.26758 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 255472 +eval/num paths total 259 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0965979 +eval/Actions Std 0.61235 +eval/Actions Max 0.998501 +eval/Actions Min -0.998921 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66312e-06 +time/evaluation sampling (s) 2.30845 +time/exploration sampling (s) 2.90186 +time/logging (s) 0.00725292 +time/saving (s) 0.0111318 +time/training (s) 12.8841 +time/epoch (s) 18.1128 +time/total (s) 5238.13 +Epoch -742 +------------------------------ ---------------- +2022-05-15 19:30:01.885930 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -741 finished +------------------------------ ---------------- +epoch -741 +replay_buffer/size 999047 +trainer/num train calls 260000 +trainer/QF1 Loss 0.968741 +trainer/QF2 Loss 1.07816 +trainer/Policy Loss 22.1727 +trainer/Q1 Predictions Mean -75.0814 +trainer/Q1 Predictions Std 16.3031 +trainer/Q1 Predictions Max -0.608583 +trainer/Q1 Predictions Min -86.4766 +trainer/Q2 Predictions Mean -75.0504 +trainer/Q2 Predictions Std 16.3847 +trainer/Q2 Predictions Max -0.807114 +trainer/Q2 Predictions Min -86.6641 +trainer/Q Targets Mean -75.6001 +trainer/Q Targets Std 16.3594 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3081 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00414109 +trainer/policy/mean Std 0.712239 +trainer/policy/mean Max 0.997133 +trainer/policy/mean Min -0.997798 +trainer/policy/std Mean 0.431222 +trainer/policy/std Std 0.0219102 +trainer/policy/std Max 0.453688 +trainer/policy/std Min 0.394056 +trainer/Advantage Weights Mean 4.33209 +trainer/Advantage Weights Std 14.2011 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.98037e-09 +trainer/Advantage Score Mean -0.223088 +trainer/Advantage Score Std 0.431884 +trainer/Advantage Score Max 1.80503 +trainer/Advantage Score Min -1.89348 +trainer/V1 Predictions Mean -75.3777 +trainer/V1 Predictions Std 16.4577 +trainer/V1 Predictions Max -1.16072 +trainer/V1 Predictions Min -87.4526 +trainer/VF Loss 0.0455421 +expl/num steps total 260000 +expl/num paths total 284 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0495765 +expl/Actions Std 0.827933 +expl/Actions Max 2.38006 +expl/Actions Min -2.38596 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 256472 +eval/num paths total 260 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.173718 +eval/Actions Std 0.658705 +eval/Actions Max 0.998213 +eval/Actions Min -0.998734 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.96021e-06 +time/evaluation sampling (s) 2.39036 +time/exploration sampling (s) 2.95965 +time/logging (s) 0.0075787 +time/saving (s) 0.0127323 +time/training (s) 12.1575 +time/epoch (s) 17.5278 +time/total (s) 5255.66 +Epoch -741 +------------------------------ ---------------- +2022-05-15 19:30:20.503360 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -740 finished +------------------------------ ---------------- +epoch -740 +replay_buffer/size 999047 +trainer/num train calls 261000 +trainer/QF1 Loss 1.52211 +trainer/QF2 Loss 1.535 +trainer/Policy Loss 4.61295 +trainer/Q1 Predictions Mean -73.2016 +trainer/Q1 Predictions Std 20.6763 +trainer/Q1 Predictions Max -0.794148 +trainer/Q1 Predictions Min -86.596 +trainer/Q2 Predictions Mean -73.2542 +trainer/Q2 Predictions Std 20.6676 +trainer/Q2 Predictions Max -1.1349 +trainer/Q2 Predictions Min -86.5326 +trainer/Q Targets Mean -72.608 +trainer/Q Targets Std 21.0003 +trainer/Q Targets Max 1.21699 +trainer/Q Targets Min -86.9167 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.010617 +trainer/policy/mean Std 0.709838 +trainer/policy/mean Max 0.996947 +trainer/policy/mean Min -0.998172 +trainer/policy/std Mean 0.4293 +trainer/policy/std Std 0.0224792 +trainer/policy/std Max 0.452964 +trainer/policy/std Min 0.389158 +trainer/Advantage Weights Mean 0.958114 +trainer/Advantage Weights Std 6.90542 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.56943e-15 +trainer/Advantage Score Mean -0.547984 +trainer/Advantage Score Std 0.578697 +trainer/Advantage Score Max 0.535481 +trainer/Advantage Score Min -3.40881 +trainer/V1 Predictions Mean -72.3164 +trainer/V1 Predictions Std 21.0415 +trainer/V1 Predictions Max 0.913885 +trainer/V1 Predictions Min -85.8928 +trainer/VF Loss 0.0660837 +expl/num steps total 261000 +expl/num paths total 286 +expl/path length Mean 500 +expl/path length Std 193 +expl/path length Max 693 +expl/path length Min 307 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0277078 +expl/Actions Std 0.823736 +expl/Actions Max 2.66094 +expl/Actions Min -2.4626 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 257472 +eval/num paths total 261 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.279989 +eval/Actions Std 0.670818 +eval/Actions Max 0.998579 +eval/Actions Min -0.998708 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.26987e-06 +time/evaluation sampling (s) 2.71916 +time/exploration sampling (s) 3.35056 +time/logging (s) 0.006838 +time/saving (s) 0.00963912 +time/training (s) 12.5224 +time/epoch (s) 18.6086 +time/total (s) 5274.27 +Epoch -740 +------------------------------ ---------------- +2022-05-15 19:30:38.801765 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -739 finished +------------------------------ ---------------- +epoch -739 +replay_buffer/size 999047 +trainer/num train calls 262000 +trainer/QF1 Loss 0.543189 +trainer/QF2 Loss 0.559864 +trainer/Policy Loss 5.64003 +trainer/Q1 Predictions Mean -73.5782 +trainer/Q1 Predictions Std 18.0496 +trainer/Q1 Predictions Max -3.19563 +trainer/Q1 Predictions Min -87.1054 +trainer/Q2 Predictions Mean -73.5416 +trainer/Q2 Predictions Std 18.01 +trainer/Q2 Predictions Max -3.42263 +trainer/Q2 Predictions Min -87.3035 +trainer/Q Targets Mean -73.3405 +trainer/Q Targets Std 18.0858 +trainer/Q Targets Max -4.8249 +trainer/Q Targets Min -87.1488 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.03595 +trainer/policy/mean Std 0.713512 +trainer/policy/mean Max 0.998197 +trainer/policy/mean Min -0.996759 +trainer/policy/std Mean 0.431285 +trainer/policy/std Std 0.0214342 +trainer/policy/std Max 0.455044 +trainer/policy/std Min 0.394137 +trainer/Advantage Weights Mean 1.50225 +trainer/Advantage Weights Std 9.50401 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.09002e-21 +trainer/Advantage Score Mean -0.512786 +trainer/Advantage Score Std 0.622834 +trainer/Advantage Score Max 0.582901 +trainer/Advantage Score Min -4.82681 +trainer/V1 Predictions Mean -73.0938 +trainer/V1 Predictions Std 18.2649 +trainer/V1 Predictions Max -1.57651 +trainer/V1 Predictions Min -87.0311 +trainer/VF Loss 0.068904 +expl/num steps total 262000 +expl/num paths total 287 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0535475 +expl/Actions Std 0.802057 +expl/Actions Max 2.2871 +expl/Actions Min -2.53436 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 258472 +eval/num paths total 262 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0406353 +eval/Actions Std 0.742601 +eval/Actions Max 0.998903 +eval/Actions Min -0.999582 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.78978e-06 +time/evaluation sampling (s) 2.49625 +time/exploration sampling (s) 2.90318 +time/logging (s) 0.00871012 +time/saving (s) 0.0127895 +time/training (s) 12.8728 +time/epoch (s) 18.2938 +time/total (s) 5292.57 +Epoch -739 +------------------------------ ---------------- +2022-05-15 19:30:56.794862 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -738 finished +------------------------------ ---------------- +epoch -738 +replay_buffer/size 999047 +trainer/num train calls 263000 +trainer/QF1 Loss 0.665691 +trainer/QF2 Loss 0.582905 +trainer/Policy Loss 11.2106 +trainer/Q1 Predictions Mean -74.1085 +trainer/Q1 Predictions Std 17.236 +trainer/Q1 Predictions Max -0.584691 +trainer/Q1 Predictions Min -86.1466 +trainer/Q2 Predictions Mean -74.1171 +trainer/Q2 Predictions Std 17.2046 +trainer/Q2 Predictions Max -0.490098 +trainer/Q2 Predictions Min -86.4121 +trainer/Q Targets Mean -74.2005 +trainer/Q Targets Std 17.0444 +trainer/Q Targets Max -0.812045 +trainer/Q Targets Min -86.1624 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00714848 +trainer/policy/mean Std 0.704872 +trainer/policy/mean Max 0.997969 +trainer/policy/mean Min -0.997567 +trainer/policy/std Mean 0.429087 +trainer/policy/std Std 0.021651 +trainer/policy/std Max 0.452834 +trainer/policy/std Min 0.392668 +trainer/Advantage Weights Mean 3.23197 +trainer/Advantage Weights Std 15.408 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.00109e-13 +trainer/Advantage Score Mean -0.379657 +trainer/Advantage Score Std 0.460082 +trainer/Advantage Score Max 0.782425 +trainer/Advantage Score Min -2.92399 +trainer/V1 Predictions Mean -73.9582 +trainer/V1 Predictions Std 17.2188 +trainer/V1 Predictions Max 1.82282 +trainer/V1 Predictions Min -86.0399 +trainer/VF Loss 0.0468639 +expl/num steps total 263000 +expl/num paths total 288 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0407608 +expl/Actions Std 0.878725 +expl/Actions Max 2.53994 +expl/Actions Min -2.44232 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 259472 +eval/num paths total 263 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.259706 +eval/Actions Std 0.706349 +eval/Actions Max 0.998996 +eval/Actions Min -0.998474 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.01097e-06 +time/evaluation sampling (s) 2.56682 +time/exploration sampling (s) 2.97193 +time/logging (s) 0.0074351 +time/saving (s) 0.0118272 +time/training (s) 12.4255 +time/epoch (s) 17.9836 +time/total (s) 5310.56 +Epoch -738 +------------------------------ ---------------- +2022-05-15 19:31:15.124732 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -737 finished +------------------------------ ---------------- +epoch -737 +replay_buffer/size 999047 +trainer/num train calls 264000 +trainer/QF1 Loss 1.08867 +trainer/QF2 Loss 0.999308 +trainer/Policy Loss 45.8052 +trainer/Q1 Predictions Mean -73.4415 +trainer/Q1 Predictions Std 17.947 +trainer/Q1 Predictions Max -1.53669 +trainer/Q1 Predictions Min -86.6683 +trainer/Q2 Predictions Mean -73.4746 +trainer/Q2 Predictions Std 17.9526 +trainer/Q2 Predictions Max -2.31968 +trainer/Q2 Predictions Min -86.8637 +trainer/Q Targets Mean -73.6489 +trainer/Q Targets Std 18.498 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1533 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0148066 +trainer/policy/mean Std 0.707873 +trainer/policy/mean Max 0.996568 +trainer/policy/mean Min -0.995694 +trainer/policy/std Mean 0.428951 +trainer/policy/std Std 0.0208963 +trainer/policy/std Max 0.45448 +trainer/policy/std Min 0.395488 +trainer/Advantage Weights Mean 7.64673 +trainer/Advantage Weights Std 18.5097 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.24149e-16 +trainer/Advantage Score Mean -0.211042 +trainer/Advantage Score Std 0.685634 +trainer/Advantage Score Max 1.36469 +trainer/Advantage Score Min -3.56653 +trainer/V1 Predictions Mean -73.3806 +trainer/V1 Predictions Std 18.7505 +trainer/V1 Predictions Max 0.154294 +trainer/V1 Predictions Min -86.9672 +trainer/VF Loss 0.0790029 +expl/num steps total 264000 +expl/num paths total 289 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.136308 +expl/Actions Std 0.807669 +expl/Actions Max 2.34609 +expl/Actions Min -2.33681 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 260472 +eval/num paths total 264 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0529477 +eval/Actions Std 0.64356 +eval/Actions Max 0.998812 +eval/Actions Min -0.998856 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.65986e-06 +time/evaluation sampling (s) 2.54428 +time/exploration sampling (s) 3.12963 +time/logging (s) 0.0122741 +time/saving (s) 0.011728 +time/training (s) 12.6293 +time/epoch (s) 18.3272 +time/total (s) 5328.89 +Epoch -737 +------------------------------ ---------------- +2022-05-15 19:31:33.458861 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -736 finished +------------------------------ ---------------- +epoch -736 +replay_buffer/size 999047 +trainer/num train calls 265000 +trainer/QF1 Loss 0.613537 +trainer/QF2 Loss 0.664227 +trainer/Policy Loss 19.6481 +trainer/Q1 Predictions Mean -72.9921 +trainer/Q1 Predictions Std 20.3058 +trainer/Q1 Predictions Max -0.455537 +trainer/Q1 Predictions Min -87.1448 +trainer/Q2 Predictions Mean -73.0282 +trainer/Q2 Predictions Std 20.3671 +trainer/Q2 Predictions Max -0.379796 +trainer/Q2 Predictions Min -87.0704 +trainer/Q Targets Mean -73.1047 +trainer/Q Targets Std 20.0668 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1075 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0071065 +trainer/policy/mean Std 0.716425 +trainer/policy/mean Max 0.996519 +trainer/policy/mean Min -0.994533 +trainer/policy/std Mean 0.431058 +trainer/policy/std Std 0.0213424 +trainer/policy/std Max 0.452912 +trainer/policy/std Min 0.396278 +trainer/Advantage Weights Mean 4.4049 +trainer/Advantage Weights Std 19.3147 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.93475e-11 +trainer/Advantage Score Mean -0.324176 +trainer/Advantage Score Std 0.415432 +trainer/Advantage Score Max 1.46541 +trainer/Advantage Score Min -2.39586 +trainer/V1 Predictions Mean -72.8216 +trainer/V1 Predictions Std 20.2339 +trainer/V1 Predictions Max 0.44579 +trainer/V1 Predictions Min -86.9035 +trainer/VF Loss 0.0463753 +expl/num steps total 265000 +expl/num paths total 290 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.040789 +expl/Actions Std 0.816093 +expl/Actions Max 2.3575 +expl/Actions Min -2.5981 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 261472 +eval/num paths total 265 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.281499 +eval/Actions Std 0.683747 +eval/Actions Max 0.998262 +eval/Actions Min -0.998996 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.05101e-06 +time/evaluation sampling (s) 2.51006 +time/exploration sampling (s) 2.94181 +time/logging (s) 0.00716253 +time/saving (s) 0.0101725 +time/training (s) 12.8468 +time/epoch (s) 18.316 +time/total (s) 5347.21 +Epoch -736 +------------------------------ ---------------- +2022-05-15 19:31:51.665260 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -735 finished +------------------------------ ---------------- +epoch -735 +replay_buffer/size 999047 +trainer/num train calls 266000 +trainer/QF1 Loss 1.11852 +trainer/QF2 Loss 1.15892 +trainer/Policy Loss 16.5646 +trainer/Q1 Predictions Mean -72.922 +trainer/Q1 Predictions Std 19.0201 +trainer/Q1 Predictions Max -1.04923 +trainer/Q1 Predictions Min -87.6973 +trainer/Q2 Predictions Mean -72.9103 +trainer/Q2 Predictions Std 19.0804 +trainer/Q2 Predictions Max -0.738754 +trainer/Q2 Predictions Min -87.4647 +trainer/Q Targets Mean -73.0926 +trainer/Q Targets Std 18.6553 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6982 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0261237 +trainer/policy/mean Std 0.723241 +trainer/policy/mean Max 0.998883 +trainer/policy/mean Min -0.998808 +trainer/policy/std Mean 0.430242 +trainer/policy/std Std 0.0210577 +trainer/policy/std Max 0.454397 +trainer/policy/std Min 0.395306 +trainer/Advantage Weights Mean 3.61689 +trainer/Advantage Weights Std 16.0106 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.5472e-12 +trainer/Advantage Score Mean -0.36159 +trainer/Advantage Score Std 0.514158 +trainer/Advantage Score Max 1.99082 +trainer/Advantage Score Min -2.71946 +trainer/V1 Predictions Mean -72.7808 +trainer/V1 Predictions Std 18.8313 +trainer/V1 Predictions Max -1.48772 +trainer/V1 Predictions Min -86.2743 +trainer/VF Loss 0.0757587 +expl/num steps total 266000 +expl/num paths total 291 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0470172 +expl/Actions Std 0.807497 +expl/Actions Max 2.33018 +expl/Actions Min -2.49993 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 262472 +eval/num paths total 266 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.149226 +eval/Actions Std 0.717336 +eval/Actions Max 0.998529 +eval/Actions Min -0.999597 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.29968e-06 +time/evaluation sampling (s) 2.45993 +time/exploration sampling (s) 2.90356 +time/logging (s) 0.00896689 +time/saving (s) 0.0149125 +time/training (s) 12.8142 +time/epoch (s) 18.2015 +time/total (s) 5365.42 +Epoch -735 +------------------------------ ---------------- +2022-05-15 19:32:09.741819 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -734 finished +------------------------------ ---------------- +epoch -734 +replay_buffer/size 999047 +trainer/num train calls 267000 +trainer/QF1 Loss 0.50335 +trainer/QF2 Loss 0.573152 +trainer/Policy Loss 59.329 +trainer/Q1 Predictions Mean -75.1856 +trainer/Q1 Predictions Std 16.6817 +trainer/Q1 Predictions Max -0.319192 +trainer/Q1 Predictions Min -88.1894 +trainer/Q2 Predictions Mean -75.1015 +trainer/Q2 Predictions Std 16.6602 +trainer/Q2 Predictions Max -0.383586 +trainer/Q2 Predictions Min -88.1963 +trainer/Q Targets Mean -75.5848 +trainer/Q Targets Std 16.8046 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.8846 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00367192 +trainer/policy/mean Std 0.714512 +trainer/policy/mean Max 0.998885 +trainer/policy/mean Min -0.996499 +trainer/policy/std Mean 0.430556 +trainer/policy/std Std 0.0210593 +trainer/policy/std Max 0.452894 +trainer/policy/std Min 0.397787 +trainer/Advantage Weights Mean 11.3969 +trainer/Advantage Weights Std 25.6311 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.78908e-15 +trainer/Advantage Score Mean -0.0850289 +trainer/Advantage Score Std 0.526095 +trainer/Advantage Score Max 0.831184 +trainer/Advantage Score Min -3.27828 +trainer/V1 Predictions Mean -75.3458 +trainer/V1 Predictions Std 17.0443 +trainer/V1 Predictions Max -0.928152 +trainer/V1 Predictions Min -88.9856 +trainer/VF Loss 0.0611522 +expl/num steps total 267000 +expl/num paths total 292 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0162252 +expl/Actions Std 0.835746 +expl/Actions Max 2.43284 +expl/Actions Min -2.36369 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 263148 +eval/num paths total 267 +eval/path length Mean 676 +eval/path length Std 0 +eval/path length Max 676 +eval/path length Min 676 +eval/Rewards Mean 0.00147929 +eval/Rewards Std 0.0384331 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0312221 +eval/Actions Std 0.724861 +eval/Actions Max 0.999534 +eval/Actions Min -0.999482 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.68221e-06 +time/evaluation sampling (s) 2.58033 +time/exploration sampling (s) 3.02902 +time/logging (s) 0.00641756 +time/saving (s) 0.00936303 +time/training (s) 12.44 +time/epoch (s) 18.0651 +time/total (s) 5383.49 +Epoch -734 +------------------------------ ---------------- +2022-05-15 19:32:27.806618 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -733 finished +------------------------------ ---------------- +epoch -733 +replay_buffer/size 999047 +trainer/num train calls 268000 +trainer/QF1 Loss 0.446643 +trainer/QF2 Loss 0.42637 +trainer/Policy Loss 35.0124 +trainer/Q1 Predictions Mean -74.5452 +trainer/Q1 Predictions Std 16.8587 +trainer/Q1 Predictions Max -1.59482 +trainer/Q1 Predictions Min -87.3998 +trainer/Q2 Predictions Mean -74.5988 +trainer/Q2 Predictions Std 16.8783 +trainer/Q2 Predictions Max -1.63918 +trainer/Q2 Predictions Min -87.517 +trainer/Q Targets Mean -74.7803 +trainer/Q Targets Std 16.7591 +trainer/Q Targets Max -1.78207 +trainer/Q Targets Min -87.5234 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0219454 +trainer/policy/mean Std 0.72354 +trainer/policy/mean Max 0.998309 +trainer/policy/mean Min -0.997596 +trainer/policy/std Mean 0.430071 +trainer/policy/std Std 0.0223617 +trainer/policy/std Max 0.455995 +trainer/policy/std Min 0.391414 +trainer/Advantage Weights Mean 9.31635 +trainer/Advantage Weights Std 25.6 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.54209e-15 +trainer/Advantage Score Mean -0.162342 +trainer/Advantage Score Std 0.513569 +trainer/Advantage Score Max 1.70145 +trainer/Advantage Score Min -3.32741 +trainer/V1 Predictions Mean -74.5799 +trainer/V1 Predictions Std 16.8864 +trainer/V1 Predictions Max -0.931895 +trainer/V1 Predictions Min -87.382 +trainer/VF Loss 0.0747159 +expl/num steps total 268000 +expl/num paths total 293 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.158559 +expl/Actions Std 0.858307 +expl/Actions Max 2.48207 +expl/Actions Min -2.501 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 264148 +eval/num paths total 268 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.206187 +eval/Actions Std 0.625672 +eval/Actions Max 0.999296 +eval/Actions Min -0.998355 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.11388e-06 +time/evaluation sampling (s) 2.52306 +time/exploration sampling (s) 2.93957 +time/logging (s) 0.00678317 +time/saving (s) 0.00965259 +time/training (s) 12.5799 +time/epoch (s) 18.059 +time/total (s) 5401.55 +Epoch -733 +------------------------------ ---------------- +2022-05-15 19:32:45.285097 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -732 finished +------------------------------ ---------------- +epoch -732 +replay_buffer/size 999047 +trainer/num train calls 269000 +trainer/QF1 Loss 1.03687 +trainer/QF2 Loss 0.979225 +trainer/Policy Loss 5.26164 +trainer/Q1 Predictions Mean -72.745 +trainer/Q1 Predictions Std 19.305 +trainer/Q1 Predictions Max -0.855476 +trainer/Q1 Predictions Min -87.0604 +trainer/Q2 Predictions Mean -72.7727 +trainer/Q2 Predictions Std 19.2114 +trainer/Q2 Predictions Max -0.803115 +trainer/Q2 Predictions Min -86.8258 +trainer/Q Targets Mean -72.741 +trainer/Q Targets Std 19.1071 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3094 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.032842 +trainer/policy/mean Std 0.709791 +trainer/policy/mean Max 0.998461 +trainer/policy/mean Min -0.997104 +trainer/policy/std Mean 0.42884 +trainer/policy/std Std 0.0213404 +trainer/policy/std Max 0.453884 +trainer/policy/std Min 0.394077 +trainer/Advantage Weights Mean 1.44666 +trainer/Advantage Weights Std 9.89901 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.78756e-20 +trainer/Advantage Score Mean -0.590625 +trainer/Advantage Score Std 0.563179 +trainer/Advantage Score Max 0.731892 +trainer/Advantage Score Min -4.54709 +trainer/V1 Predictions Mean -72.4642 +trainer/V1 Predictions Std 19.1957 +trainer/V1 Predictions Max -0.424709 +trainer/V1 Predictions Min -86.3158 +trainer/VF Loss 0.0709823 +expl/num steps total 269000 +expl/num paths total 295 +expl/path length Mean 500 +expl/path length Std 420 +expl/path length Max 920 +expl/path length Min 80 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0312811 +expl/Actions Std 0.819472 +expl/Actions Max 2.44686 +expl/Actions Min -2.29925 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 265148 +eval/num paths total 269 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.000473001 +eval/Actions Std 0.727525 +eval/Actions Max 0.999782 +eval/Actions Min -0.998976 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.31504e-06 +time/evaluation sampling (s) 2.48893 +time/exploration sampling (s) 2.90771 +time/logging (s) 0.00832807 +time/saving (s) 0.0128428 +time/training (s) 12.0557 +time/epoch (s) 17.4736 +time/total (s) 5419.03 +Epoch -732 +------------------------------ ---------------- +2022-05-15 19:33:03.531539 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -731 finished +------------------------------ ---------------- +epoch -731 +replay_buffer/size 999047 +trainer/num train calls 270000 +trainer/QF1 Loss 0.428363 +trainer/QF2 Loss 0.442348 +trainer/Policy Loss 17.8043 +trainer/Q1 Predictions Mean -74.5185 +trainer/Q1 Predictions Std 16.9387 +trainer/Q1 Predictions Max -5.22623 +trainer/Q1 Predictions Min -86.4154 +trainer/Q2 Predictions Mean -74.5093 +trainer/Q2 Predictions Std 16.951 +trainer/Q2 Predictions Max -5.45213 +trainer/Q2 Predictions Min -87.0615 +trainer/Q Targets Mean -74.5107 +trainer/Q Targets Std 16.7214 +trainer/Q Targets Max -5.04582 +trainer/Q Targets Min -86.409 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00673869 +trainer/policy/mean Std 0.707465 +trainer/policy/mean Max 0.99667 +trainer/policy/mean Min -0.997048 +trainer/policy/std Mean 0.428079 +trainer/policy/std Std 0.0200177 +trainer/policy/std Max 0.451435 +trainer/policy/std Min 0.395285 +trainer/Advantage Weights Mean 3.84982 +trainer/Advantage Weights Std 16.387 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.94862e-11 +trainer/Advantage Score Mean -0.252507 +trainer/Advantage Score Std 0.405468 +trainer/Advantage Score Max 1.12136 +trainer/Advantage Score Min -2.33899 +trainer/V1 Predictions Mean -74.2559 +trainer/V1 Predictions Std 16.9456 +trainer/V1 Predictions Max -5.04515 +trainer/V1 Predictions Min -87.0093 +trainer/VF Loss 0.0389298 +expl/num steps total 270000 +expl/num paths total 296 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0460463 +expl/Actions Std 0.81684 +expl/Actions Max 2.2142 +expl/Actions Min -2.24705 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 266148 +eval/num paths total 270 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0372976 +eval/Actions Std 0.729431 +eval/Actions Max 0.99903 +eval/Actions Min -0.99907 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94112e-06 +time/evaluation sampling (s) 2.47807 +time/exploration sampling (s) 2.94253 +time/logging (s) 0.00811224 +time/saving (s) 0.0108822 +time/training (s) 12.7991 +time/epoch (s) 18.2387 +time/total (s) 5437.27 +Epoch -731 +------------------------------ ---------------- +2022-05-15 19:33:22.113981 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -730 finished +------------------------------ ---------------- +epoch -730 +replay_buffer/size 999047 +trainer/num train calls 271000 +trainer/QF1 Loss 0.716753 +trainer/QF2 Loss 0.706823 +trainer/Policy Loss 13.5671 +trainer/Q1 Predictions Mean -75.4017 +trainer/Q1 Predictions Std 15.8692 +trainer/Q1 Predictions Max -0.311362 +trainer/Q1 Predictions Min -87.1467 +trainer/Q2 Predictions Mean -75.409 +trainer/Q2 Predictions Std 15.8483 +trainer/Q2 Predictions Max -0.406634 +trainer/Q2 Predictions Min -87.2894 +trainer/Q Targets Mean -75.2822 +trainer/Q Targets Std 16.1462 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.2718 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.016872 +trainer/policy/mean Std 0.711751 +trainer/policy/mean Max 0.99855 +trainer/policy/mean Min -0.992536 +trainer/policy/std Mean 0.429359 +trainer/policy/std Std 0.0209025 +trainer/policy/std Max 0.450624 +trainer/policy/std Min 0.392891 +trainer/Advantage Weights Mean 2.50909 +trainer/Advantage Weights Std 10.8827 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.48185e-26 +trainer/Advantage Score Mean -0.316742 +trainer/Advantage Score Std 0.638204 +trainer/Advantage Score Max 0.764656 +trainer/Advantage Score Min -5.89582 +trainer/V1 Predictions Mean -74.9543 +trainer/V1 Predictions Std 16.4565 +trainer/V1 Predictions Max 1.03554 +trainer/V1 Predictions Min -87.6822 +trainer/VF Loss 0.0583623 +expl/num steps total 271000 +expl/num paths total 297 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0519028 +expl/Actions Std 0.815156 +expl/Actions Max 2.26341 +expl/Actions Min -2.26248 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 267148 +eval/num paths total 271 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0810542 +eval/Actions Std 0.694095 +eval/Actions Max 0.999111 +eval/Actions Min -0.999058 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.84985e-06 +time/evaluation sampling (s) 2.56359 +time/exploration sampling (s) 2.98789 +time/logging (s) 0.00719716 +time/saving (s) 0.0120206 +time/training (s) 13.0042 +time/epoch (s) 18.5749 +time/total (s) 5455.85 +Epoch -730 +------------------------------ ---------------- +2022-05-15 19:33:40.265220 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -729 finished +------------------------------ ---------------- +epoch -729 +replay_buffer/size 999047 +trainer/num train calls 272000 +trainer/QF1 Loss 1.53161 +trainer/QF2 Loss 1.60573 +trainer/Policy Loss 34.9142 +trainer/Q1 Predictions Mean -73.3059 +trainer/Q1 Predictions Std 18.2587 +trainer/Q1 Predictions Max -1.34955 +trainer/Q1 Predictions Min -86.1014 +trainer/Q2 Predictions Mean -73.2609 +trainer/Q2 Predictions Std 18.243 +trainer/Q2 Predictions Max -1.47959 +trainer/Q2 Predictions Min -86.1893 +trainer/Q Targets Mean -73.6441 +trainer/Q Targets Std 18.8692 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6812 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0139695 +trainer/policy/mean Std 0.700081 +trainer/policy/mean Max 0.999023 +trainer/policy/mean Min -0.999256 +trainer/policy/std Mean 0.431322 +trainer/policy/std Std 0.0206767 +trainer/policy/std Max 0.451577 +trainer/policy/std Min 0.39817 +trainer/Advantage Weights Mean 7.29235 +trainer/Advantage Weights Std 19.9169 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.95965e-12 +trainer/Advantage Score Mean -0.165079 +trainer/Advantage Score Std 0.521883 +trainer/Advantage Score Max 2.20604 +trainer/Advantage Score Min -2.54383 +trainer/V1 Predictions Mean -73.4771 +trainer/V1 Predictions Std 18.7527 +trainer/V1 Predictions Max -0.87244 +trainer/V1 Predictions Min -86.4901 +trainer/VF Loss 0.0633435 +expl/num steps total 272000 +expl/num paths total 298 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0488058 +expl/Actions Std 0.808525 +expl/Actions Max 2.18886 +expl/Actions Min -2.32925 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 268132 +eval/num paths total 272 +eval/path length Mean 984 +eval/path length Std 0 +eval/path length Max 984 +eval/path length Min 984 +eval/Rewards Mean 0.00101626 +eval/Rewards Std 0.0318626 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0285001 +eval/Actions Std 0.730469 +eval/Actions Max 0.999415 +eval/Actions Min -0.999686 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.16696e-06 +time/evaluation sampling (s) 2.54581 +time/exploration sampling (s) 2.90447 +time/logging (s) 0.00735213 +time/saving (s) 0.0122782 +time/training (s) 12.6733 +time/epoch (s) 18.1433 +time/total (s) 5474 +Epoch -729 +------------------------------ ---------------- +2022-05-15 19:33:57.226918 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -728 finished +------------------------------ ---------------- +epoch -728 +replay_buffer/size 999047 +trainer/num train calls 273000 +trainer/QF1 Loss 0.813541 +trainer/QF2 Loss 0.946995 +trainer/Policy Loss 44.0214 +trainer/Q1 Predictions Mean -75.3074 +trainer/Q1 Predictions Std 15.6149 +trainer/Q1 Predictions Max -0.4223 +trainer/Q1 Predictions Min -87.25 +trainer/Q2 Predictions Mean -75.2951 +trainer/Q2 Predictions Std 15.6101 +trainer/Q2 Predictions Max -0.395124 +trainer/Q2 Predictions Min -86.8022 +trainer/Q Targets Mean -75.7469 +trainer/Q Targets Std 15.4464 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5499 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0145851 +trainer/policy/mean Std 0.71452 +trainer/policy/mean Max 0.998598 +trainer/policy/mean Min -0.997518 +trainer/policy/std Mean 0.430058 +trainer/policy/std Std 0.0213541 +trainer/policy/std Max 0.45342 +trainer/policy/std Min 0.390925 +trainer/Advantage Weights Mean 7.36985 +trainer/Advantage Weights Std 22.7918 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.02609e-11 +trainer/Advantage Score Mean -0.141629 +trainer/Advantage Score Std 0.465689 +trainer/Advantage Score Max 1.94468 +trainer/Advantage Score Min -2.46223 +trainer/V1 Predictions Mean -75.5159 +trainer/V1 Predictions Std 15.5909 +trainer/V1 Predictions Max -0.674473 +trainer/V1 Predictions Min -87.4671 +trainer/VF Loss 0.0641209 +expl/num steps total 273000 +expl/num paths total 300 +expl/path length Mean 500 +expl/path length Std 309 +expl/path length Max 809 +expl/path length Min 191 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0646117 +expl/Actions Std 0.811833 +expl/Actions Max 2.44486 +expl/Actions Min -2.27272 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 269132 +eval/num paths total 273 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0300584 +eval/Actions Std 0.711685 +eval/Actions Max 0.99894 +eval/Actions Min -0.998991 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.11807e-06 +time/evaluation sampling (s) 2.58184 +time/exploration sampling (s) 2.92737 +time/logging (s) 0.00719758 +time/saving (s) 0.0103749 +time/training (s) 11.4265 +time/epoch (s) 16.9533 +time/total (s) 5490.96 +Epoch -728 +------------------------------ ---------------- +2022-05-15 19:34:15.070439 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -727 finished +------------------------------ ---------------- +epoch -727 +replay_buffer/size 999047 +trainer/num train calls 274000 +trainer/QF1 Loss 1.15983 +trainer/QF2 Loss 1.07923 +trainer/Policy Loss 45.0916 +trainer/Q1 Predictions Mean -74.0397 +trainer/Q1 Predictions Std 17.441 +trainer/Q1 Predictions Max -1.60571 +trainer/Q1 Predictions Min -86.3048 +trainer/Q2 Predictions Mean -74.078 +trainer/Q2 Predictions Std 17.3741 +trainer/Q2 Predictions Max -1.68651 +trainer/Q2 Predictions Min -86.361 +trainer/Q Targets Mean -74.8596 +trainer/Q Targets Std 17.3112 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4362 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00620361 +trainer/policy/mean Std 0.695496 +trainer/policy/mean Max 0.996299 +trainer/policy/mean Min -0.998314 +trainer/policy/std Mean 0.428182 +trainer/policy/std Std 0.0217735 +trainer/policy/std Max 0.453469 +trainer/policy/std Min 0.387866 +trainer/Advantage Weights Mean 10.3254 +trainer/Advantage Weights Std 25.3308 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.77355e-13 +trainer/Advantage Score Mean -0.12054 +trainer/Advantage Score Std 0.502447 +trainer/Advantage Score Max 2.16801 +trainer/Advantage Score Min -2.89135 +trainer/V1 Predictions Mean -74.5652 +trainer/V1 Predictions Std 17.4365 +trainer/V1 Predictions Max -1.93744 +trainer/V1 Predictions Min -87.1193 +trainer/VF Loss 0.0771648 +expl/num steps total 274000 +expl/num paths total 301 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0359885 +expl/Actions Std 0.832214 +expl/Actions Max 2.32327 +expl/Actions Min -2.34601 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 270132 +eval/num paths total 274 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.180194 +eval/Actions Std 0.757741 +eval/Actions Max 0.998479 +eval/Actions Min -0.999 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83588e-06 +time/evaluation sampling (s) 2.49647 +time/exploration sampling (s) 2.9739 +time/logging (s) 0.00660564 +time/saving (s) 0.00946801 +time/training (s) 12.3497 +time/epoch (s) 17.8362 +time/total (s) 5508.8 +Epoch -727 +------------------------------ ---------------- +2022-05-15 19:34:33.349756 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -726 finished +------------------------------ ---------------- +epoch -726 +replay_buffer/size 999047 +trainer/num train calls 275000 +trainer/QF1 Loss 0.883891 +trainer/QF2 Loss 0.885906 +trainer/Policy Loss 8.47197 +trainer/Q1 Predictions Mean -72.6556 +trainer/Q1 Predictions Std 19.0476 +trainer/Q1 Predictions Max -0.302365 +trainer/Q1 Predictions Min -86.4788 +trainer/Q2 Predictions Mean -72.6918 +trainer/Q2 Predictions Std 19.0721 +trainer/Q2 Predictions Max -0.258989 +trainer/Q2 Predictions Min -86.4468 +trainer/Q Targets Mean -72.7062 +trainer/Q Targets Std 19.5079 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6474 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00964794 +trainer/policy/mean Std 0.706564 +trainer/policy/mean Max 0.996165 +trainer/policy/mean Min -0.997993 +trainer/policy/std Mean 0.430036 +trainer/policy/std Std 0.0209634 +trainer/policy/std Max 0.451513 +trainer/policy/std Min 0.392384 +trainer/Advantage Weights Mean 2.11426 +trainer/Advantage Weights Std 12.5201 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19228e-24 +trainer/Advantage Score Mean -0.452178 +trainer/Advantage Score Std 0.657794 +trainer/Advantage Score Max 0.886198 +trainer/Advantage Score Min -5.50862 +trainer/V1 Predictions Mean -72.4616 +trainer/V1 Predictions Std 19.5681 +trainer/V1 Predictions Max -0.288146 +trainer/V1 Predictions Min -86.5145 +trainer/VF Loss 0.0715736 +expl/num steps total 275000 +expl/num paths total 302 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.200401 +expl/Actions Std 0.822048 +expl/Actions Max 2.54856 +expl/Actions Min -2.44796 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 271024 +eval/num paths total 275 +eval/path length Mean 892 +eval/path length Std 0 +eval/path length Max 892 +eval/path length Min 892 +eval/Rewards Mean 0.00112108 +eval/Rewards Std 0.0334637 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0301826 +eval/Actions Std 0.727615 +eval/Actions Max 0.998521 +eval/Actions Min -0.998829 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.62191e-06 +time/evaluation sampling (s) 2.45798 +time/exploration sampling (s) 2.99272 +time/logging (s) 0.00632137 +time/saving (s) 0.00982662 +time/training (s) 12.8057 +time/epoch (s) 18.2726 +time/total (s) 5527.07 +Epoch -726 +------------------------------ ---------------- +2022-05-15 19:34:50.847602 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -725 finished +------------------------------ ---------------- +epoch -725 +replay_buffer/size 999047 +trainer/num train calls 276000 +trainer/QF1 Loss 3.79392 +trainer/QF2 Loss 3.88615 +trainer/Policy Loss 96.1513 +trainer/Q1 Predictions Mean -72.5066 +trainer/Q1 Predictions Std 18.8102 +trainer/Q1 Predictions Max -0.377507 +trainer/Q1 Predictions Min -87.5784 +trainer/Q2 Predictions Mean -72.541 +trainer/Q2 Predictions Std 18.8205 +trainer/Q2 Predictions Max -0.363188 +trainer/Q2 Predictions Min -87.6883 +trainer/Q Targets Mean -73.5944 +trainer/Q Targets Std 18.3574 +trainer/Q Targets Max 0.850194 +trainer/Q Targets Min -86.8871 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0232131 +trainer/policy/mean Std 0.721623 +trainer/policy/mean Max 0.998947 +trainer/policy/mean Min -0.998593 +trainer/policy/std Mean 0.429648 +trainer/policy/std Std 0.0222742 +trainer/policy/std Max 0.453817 +trainer/policy/std Min 0.392102 +trainer/Advantage Weights Mean 23.5681 +trainer/Advantage Weights Std 37.7058 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.42401e-19 +trainer/Advantage Score Mean -0.0189375 +trainer/Advantage Score Std 0.652229 +trainer/Advantage Score Max 1.68265 +trainer/Advantage Score Min -4.28637 +trainer/V1 Predictions Mean -73.2696 +trainer/V1 Predictions Std 18.7337 +trainer/V1 Predictions Max 2.4009 +trainer/V1 Predictions Min -86.7571 +trainer/VF Loss 0.147547 +expl/num steps total 276000 +expl/num paths total 303 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0374174 +expl/Actions Std 0.791861 +expl/Actions Max 2.40111 +expl/Actions Min -2.30747 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 272024 +eval/num paths total 276 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0406401 +eval/Actions Std 0.687904 +eval/Actions Max 0.99967 +eval/Actions Min -0.999078 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.89083e-06 +time/evaluation sampling (s) 2.61 +time/exploration sampling (s) 2.93917 +time/logging (s) 0.00665273 +time/saving (s) 0.0097328 +time/training (s) 11.9261 +time/epoch (s) 17.4917 +time/total (s) 5544.57 +Epoch -725 +------------------------------ ---------------- +2022-05-15 19:35:09.538777 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -724 finished +------------------------------ ---------------- +epoch -724 +replay_buffer/size 999047 +trainer/num train calls 277000 +trainer/QF1 Loss 0.825564 +trainer/QF2 Loss 0.866064 +trainer/Policy Loss 12.8647 +trainer/Q1 Predictions Mean -72.2852 +trainer/Q1 Predictions Std 19.0856 +trainer/Q1 Predictions Max -3.59056 +trainer/Q1 Predictions Min -86.4145 +trainer/Q2 Predictions Mean -72.31 +trainer/Q2 Predictions Std 19.0902 +trainer/Q2 Predictions Max -4.50382 +trainer/Q2 Predictions Min -86.4397 +trainer/Q Targets Mean -72.3517 +trainer/Q Targets Std 19.2248 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7956 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00968753 +trainer/policy/mean Std 0.703629 +trainer/policy/mean Max 0.998779 +trainer/policy/mean Min -0.996787 +trainer/policy/std Mean 0.42989 +trainer/policy/std Std 0.0213859 +trainer/policy/std Max 0.453767 +trainer/policy/std Min 0.393117 +trainer/Advantage Weights Mean 2.91414 +trainer/Advantage Weights Std 12.9516 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.69343e-12 +trainer/Advantage Score Mean -0.347789 +trainer/Advantage Score Std 0.425366 +trainer/Advantage Score Max 1.49413 +trainer/Advantage Score Min -2.53596 +trainer/V1 Predictions Mean -72.09 +trainer/V1 Predictions Std 19.3233 +trainer/V1 Predictions Max -2.34938 +trainer/V1 Predictions Min -86.0881 +trainer/VF Loss 0.043342 +expl/num steps total 277000 +expl/num paths total 304 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0281811 +expl/Actions Std 0.821307 +expl/Actions Max 2.23846 +expl/Actions Min -2.39071 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 273024 +eval/num paths total 277 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0954221 +eval/Actions Std 0.741365 +eval/Actions Max 0.99658 +eval/Actions Min -0.998391 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.5279e-06 +time/evaluation sampling (s) 2.66679 +time/exploration sampling (s) 2.9183 +time/logging (s) 0.0111217 +time/saving (s) 0.0196713 +time/training (s) 13.0714 +time/epoch (s) 18.6873 +time/total (s) 5563.26 +Epoch -724 +------------------------------ ---------------- +2022-05-15 19:35:27.747677 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -723 finished +------------------------------ ---------------- +epoch -723 +replay_buffer/size 999047 +trainer/num train calls 278000 +trainer/QF1 Loss 0.818346 +trainer/QF2 Loss 0.758927 +trainer/Policy Loss 39.3607 +trainer/Q1 Predictions Mean -74.4203 +trainer/Q1 Predictions Std 16.984 +trainer/Q1 Predictions Max -1.40014 +trainer/Q1 Predictions Min -86.3527 +trainer/Q2 Predictions Mean -74.4668 +trainer/Q2 Predictions Std 17.0159 +trainer/Q2 Predictions Max -0.883631 +trainer/Q2 Predictions Min -86.2601 +trainer/Q Targets Mean -74.4967 +trainer/Q Targets Std 17.0458 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2525 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0290814 +trainer/policy/mean Std 0.715208 +trainer/policy/mean Max 0.999457 +trainer/policy/mean Min -0.998769 +trainer/policy/std Mean 0.429079 +trainer/policy/std Std 0.0203992 +trainer/policy/std Max 0.451219 +trainer/policy/std Min 0.395444 +trainer/Advantage Weights Mean 7.24157 +trainer/Advantage Weights Std 22.4583 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.2226e-14 +trainer/Advantage Score Mean -0.277183 +trainer/Advantage Score Std 0.594288 +trainer/Advantage Score Max 2.72198 +trainer/Advantage Score Min -3.20352 +trainer/V1 Predictions Mean -74.2369 +trainer/V1 Predictions Std 17.1842 +trainer/V1 Predictions Max -0.398556 +trainer/V1 Predictions Min -86.2183 +trainer/VF Loss 0.0974628 +expl/num steps total 278000 +expl/num paths total 305 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.124244 +expl/Actions Std 0.778482 +expl/Actions Max 2.30418 +expl/Actions Min -2.19453 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 274024 +eval/num paths total 278 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.151787 +eval/Actions Std 0.699054 +eval/Actions Max 0.999352 +eval/Actions Min -0.998948 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.41771e-06 +time/evaluation sampling (s) 2.48033 +time/exploration sampling (s) 2.98641 +time/logging (s) 0.0105339 +time/saving (s) 0.0146829 +time/training (s) 12.7081 +time/epoch (s) 18.2001 +time/total (s) 5581.47 +Epoch -723 +------------------------------ ---------------- +2022-05-15 19:35:45.399482 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -722 finished +------------------------------ ---------------- +epoch -722 +replay_buffer/size 999047 +trainer/num train calls 279000 +trainer/QF1 Loss 0.904704 +trainer/QF2 Loss 0.838975 +trainer/Policy Loss 24.1212 +trainer/Q1 Predictions Mean -73.5427 +trainer/Q1 Predictions Std 18.7548 +trainer/Q1 Predictions Max -0.563766 +trainer/Q1 Predictions Min -87.3633 +trainer/Q2 Predictions Mean -73.6454 +trainer/Q2 Predictions Std 18.7933 +trainer/Q2 Predictions Max -0.562429 +trainer/Q2 Predictions Min -87.4428 +trainer/Q Targets Mean -73.6783 +trainer/Q Targets Std 18.879 +trainer/Q Targets Max 0.159351 +trainer/Q Targets Min -87.4943 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 2.55234e-05 +trainer/policy/mean Std 0.72029 +trainer/policy/mean Max 0.998811 +trainer/policy/mean Min -0.997482 +trainer/policy/std Mean 0.429433 +trainer/policy/std Std 0.0218989 +trainer/policy/std Max 0.453114 +trainer/policy/std Min 0.393786 +trainer/Advantage Weights Mean 3.83439 +trainer/Advantage Weights Std 15.1479 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.82674e-13 +trainer/Advantage Score Mean -0.224623 +trainer/Advantage Score Std 0.467254 +trainer/Advantage Score Max 1.30404 +trainer/Advantage Score Min -2.88945 +trainer/V1 Predictions Mean -73.4599 +trainer/V1 Predictions Std 18.9643 +trainer/V1 Predictions Max 0.964209 +trainer/V1 Predictions Min -87.3559 +trainer/VF Loss 0.0471771 +expl/num steps total 279000 +expl/num paths total 307 +expl/path length Mean 500 +expl/path length Std 348 +expl/path length Max 848 +expl/path length Min 152 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0499342 +expl/Actions Std 0.811526 +expl/Actions Max 2.48162 +expl/Actions Min -2.50874 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 275024 +eval/num paths total 279 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0391568 +eval/Actions Std 0.712689 +eval/Actions Max 0.9993 +eval/Actions Min -0.999454 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.09292e-06 +time/evaluation sampling (s) 2.45481 +time/exploration sampling (s) 2.90197 +time/logging (s) 0.00699761 +time/saving (s) 0.0108116 +time/training (s) 12.2657 +time/epoch (s) 17.6403 +time/total (s) 5599.11 +Epoch -722 +------------------------------ ---------------- +2022-05-15 19:36:03.329476 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -721 finished +------------------------------ ---------------- +epoch -721 +replay_buffer/size 999047 +trainer/num train calls 280000 +trainer/QF1 Loss 0.838588 +trainer/QF2 Loss 0.754569 +trainer/Policy Loss 39.3946 +trainer/Q1 Predictions Mean -71.9102 +trainer/Q1 Predictions Std 19.9358 +trainer/Q1 Predictions Max -1.27113 +trainer/Q1 Predictions Min -87.1781 +trainer/Q2 Predictions Mean -71.9409 +trainer/Q2 Predictions Std 19.8898 +trainer/Q2 Predictions Max -1.65919 +trainer/Q2 Predictions Min -87.3116 +trainer/Q Targets Mean -72.0315 +trainer/Q Targets Std 20.1113 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1931 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0235369 +trainer/policy/mean Std 0.712187 +trainer/policy/mean Max 0.996688 +trainer/policy/mean Min -0.998701 +trainer/policy/std Mean 0.430165 +trainer/policy/std Std 0.0220374 +trainer/policy/std Max 0.454547 +trainer/policy/std Min 0.395011 +trainer/Advantage Weights Mean 7.92566 +trainer/Advantage Weights Std 24.296 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.37649e-13 +trainer/Advantage Score Mean -0.252426 +trainer/Advantage Score Std 0.535827 +trainer/Advantage Score Max 1.12677 +trainer/Advantage Score Min -2.96141 +trainer/V1 Predictions Mean -71.7699 +trainer/V1 Predictions Std 20.1638 +trainer/V1 Predictions Max -0.859279 +trainer/V1 Predictions Min -87.3213 +trainer/VF Loss 0.0697767 +expl/num steps total 280000 +expl/num paths total 308 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.015564 +expl/Actions Std 0.834962 +expl/Actions Max 2.61304 +expl/Actions Min -2.56716 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 276024 +eval/num paths total 280 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0195063 +eval/Actions Std 0.725866 +eval/Actions Max 0.999543 +eval/Actions Min -0.998721 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.81492e-06 +time/evaluation sampling (s) 2.49635 +time/exploration sampling (s) 2.93237 +time/logging (s) 0.00892354 +time/saving (s) 0.0143849 +time/training (s) 12.4727 +time/epoch (s) 17.9247 +time/total (s) 5617.04 +Epoch -721 +------------------------------ ---------------- +2022-05-15 19:36:21.426183 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -720 finished +------------------------------ ---------------- +epoch -720 +replay_buffer/size 999047 +trainer/num train calls 281000 +trainer/QF1 Loss 1.29596 +trainer/QF2 Loss 1.30249 +trainer/Policy Loss 21.7328 +trainer/Q1 Predictions Mean -74.4483 +trainer/Q1 Predictions Std 17.2023 +trainer/Q1 Predictions Max -1.49512 +trainer/Q1 Predictions Min -87.1239 +trainer/Q2 Predictions Mean -74.4669 +trainer/Q2 Predictions Std 17.1684 +trainer/Q2 Predictions Max -1.88401 +trainer/Q2 Predictions Min -86.923 +trainer/Q Targets Mean -74.435 +trainer/Q Targets Std 17.3087 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1032 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0107681 +trainer/policy/mean Std 0.695577 +trainer/policy/mean Max 0.997953 +trainer/policy/mean Min -0.995438 +trainer/policy/std Mean 0.429418 +trainer/policy/std Std 0.0222607 +trainer/policy/std Max 0.455325 +trainer/policy/std Min 0.392649 +trainer/Advantage Weights Mean 3.98794 +trainer/Advantage Weights Std 16.346 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.88818e-15 +trainer/Advantage Score Mean -0.376133 +trainer/Advantage Score Std 0.601144 +trainer/Advantage Score Max 0.913551 +trainer/Advantage Score Min -3.24734 +trainer/V1 Predictions Mean -74.1709 +trainer/V1 Predictions Std 17.3937 +trainer/V1 Predictions Max -1.02331 +trainer/V1 Predictions Min -86.9724 +trainer/VF Loss 0.0621331 +expl/num steps total 281000 +expl/num paths total 310 +expl/path length Mean 500 +expl/path length Std 371 +expl/path length Max 871 +expl/path length Min 129 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0281188 +expl/Actions Std 0.820986 +expl/Actions Max 2.21585 +expl/Actions Min -2.53623 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 277024 +eval/num paths total 281 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0625372 +eval/Actions Std 0.716524 +eval/Actions Max 0.998202 +eval/Actions Min -0.999472 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.09525e-06 +time/evaluation sampling (s) 2.59694 +time/exploration sampling (s) 2.94362 +time/logging (s) 0.00691726 +time/saving (s) 0.0109418 +time/training (s) 12.5267 +time/epoch (s) 18.0851 +time/total (s) 5635.13 +Epoch -720 +------------------------------ ---------------- +2022-05-15 19:36:39.988973 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -719 finished +------------------------------ ---------------- +epoch -719 +replay_buffer/size 999047 +trainer/num train calls 282000 +trainer/QF1 Loss 0.648364 +trainer/QF2 Loss 0.608205 +trainer/Policy Loss 32.0843 +trainer/Q1 Predictions Mean -73.6348 +trainer/Q1 Predictions Std 17.4739 +trainer/Q1 Predictions Max -0.609698 +trainer/Q1 Predictions Min -86.7942 +trainer/Q2 Predictions Mean -73.68 +trainer/Q2 Predictions Std 17.4396 +trainer/Q2 Predictions Max -0.278983 +trainer/Q2 Predictions Min -86.7574 +trainer/Q Targets Mean -73.8409 +trainer/Q Targets Std 17.4717 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0927 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.004055 +trainer/policy/mean Std 0.707816 +trainer/policy/mean Max 0.997159 +trainer/policy/mean Min -0.999287 +trainer/policy/std Mean 0.428993 +trainer/policy/std Std 0.0208621 +trainer/policy/std Max 0.453895 +trainer/policy/std Min 0.393573 +trainer/Advantage Weights Mean 6.68749 +trainer/Advantage Weights Std 21.1948 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.60354e-14 +trainer/Advantage Score Mean -0.235838 +trainer/Advantage Score Std 0.534713 +trainer/Advantage Score Max 1.29458 +trainer/Advantage Score Min -2.99741 +trainer/V1 Predictions Mean -73.599 +trainer/V1 Predictions Std 17.5281 +trainer/V1 Predictions Max -0.685464 +trainer/V1 Predictions Min -86.8371 +trainer/VF Loss 0.0649099 +expl/num steps total 282000 +expl/num paths total 311 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0262931 +expl/Actions Std 0.820712 +expl/Actions Max 2.14444 +expl/Actions Min -2.35437 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 278024 +eval/num paths total 282 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0889927 +eval/Actions Std 0.706639 +eval/Actions Max 0.999538 +eval/Actions Min -0.998929 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.90712e-06 +time/evaluation sampling (s) 2.55665 +time/exploration sampling (s) 2.92769 +time/logging (s) 0.00950493 +time/saving (s) 0.0128044 +time/training (s) 13.051 +time/epoch (s) 18.5577 +time/total (s) 5653.69 +Epoch -719 +------------------------------ ---------------- +2022-05-15 19:36:59.403806 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -718 finished +------------------------------ ---------------- +epoch -718 +replay_buffer/size 999047 +trainer/num train calls 283000 +trainer/QF1 Loss 0.727345 +trainer/QF2 Loss 0.743186 +trainer/Policy Loss 38.6005 +trainer/Q1 Predictions Mean -74.0071 +trainer/Q1 Predictions Std 17.1467 +trainer/Q1 Predictions Max -1.08334 +trainer/Q1 Predictions Min -87.3874 +trainer/Q2 Predictions Mean -73.969 +trainer/Q2 Predictions Std 17.1735 +trainer/Q2 Predictions Max -0.853272 +trainer/Q2 Predictions Min -88.884 +trainer/Q Targets Mean -74.1534 +trainer/Q Targets Std 17.3284 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9708 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0118168 +trainer/policy/mean Std 0.724946 +trainer/policy/mean Max 0.99831 +trainer/policy/mean Min -0.998629 +trainer/policy/std Mean 0.426316 +trainer/policy/std Std 0.0230738 +trainer/policy/std Max 0.45149 +trainer/policy/std Min 0.387513 +trainer/Advantage Weights Mean 6.22053 +trainer/Advantage Weights Std 18.5638 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.34039e-20 +trainer/Advantage Score Mean -0.214778 +trainer/Advantage Score Std 0.573467 +trainer/Advantage Score Max 0.817516 +trainer/Advantage Score Min -4.43764 +trainer/V1 Predictions Mean -73.9097 +trainer/V1 Predictions Std 17.4934 +trainer/V1 Predictions Max 0.410351 +trainer/V1 Predictions Min -87.472 +trainer/VF Loss 0.0564357 +expl/num steps total 283000 +expl/num paths total 312 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0256172 +expl/Actions Std 0.839494 +expl/Actions Max 2.53834 +expl/Actions Min -2.24541 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 279024 +eval/num paths total 283 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.282725 +eval/Actions Std 0.771284 +eval/Actions Max 0.998883 +eval/Actions Min -0.999371 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.78423e-06 +time/evaluation sampling (s) 2.60232 +time/exploration sampling (s) 3.05673 +time/logging (s) 0.00829697 +time/saving (s) 0.0117114 +time/training (s) 13.7275 +time/epoch (s) 19.4066 +time/total (s) 5673.1 +Epoch -718 +------------------------------ ---------------- +2022-05-15 19:37:16.713760 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -717 finished +------------------------------ ---------------- +epoch -717 +replay_buffer/size 999047 +trainer/num train calls 284000 +trainer/QF1 Loss 1.05424 +trainer/QF2 Loss 1.061 +trainer/Policy Loss 35.2496 +trainer/Q1 Predictions Mean -75.4236 +trainer/Q1 Predictions Std 15.1686 +trainer/Q1 Predictions Max -1.46093 +trainer/Q1 Predictions Min -86.3567 +trainer/Q2 Predictions Mean -75.4241 +trainer/Q2 Predictions Std 15.2349 +trainer/Q2 Predictions Max -0.765884 +trainer/Q2 Predictions Min -86.3415 +trainer/Q Targets Mean -75.7387 +trainer/Q Targets Std 15.5858 +trainer/Q Targets Max -1.44858 +trainer/Q Targets Min -86.9318 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00441083 +trainer/policy/mean Std 0.713687 +trainer/policy/mean Max 0.998725 +trainer/policy/mean Min -0.99953 +trainer/policy/std Mean 0.428324 +trainer/policy/std Std 0.0207256 +trainer/policy/std Max 0.451055 +trainer/policy/std Min 0.393362 +trainer/Advantage Weights Mean 8.49501 +trainer/Advantage Weights Std 23.808 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.58126e-17 +trainer/Advantage Score Mean -0.178963 +trainer/Advantage Score Std 0.53915 +trainer/Advantage Score Max 0.976027 +trainer/Advantage Score Min -3.78682 +trainer/V1 Predictions Mean -75.5503 +trainer/V1 Predictions Std 15.4796 +trainer/V1 Predictions Max 0.099241 +trainer/V1 Predictions Min -86.8243 +trainer/VF Loss 0.0586177 +expl/num steps total 284000 +expl/num paths total 313 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.035758 +expl/Actions Std 0.81759 +expl/Actions Max 2.26253 +expl/Actions Min -2.33478 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 279835 +eval/num paths total 284 +eval/path length Mean 811 +eval/path length Std 0 +eval/path length Max 811 +eval/path length Min 811 +eval/Rewards Mean 0.00123305 +eval/Rewards Std 0.0350931 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0136872 +eval/Actions Std 0.728141 +eval/Actions Max 0.999849 +eval/Actions Min -0.999415 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.56579e-06 +time/evaluation sampling (s) 2.46601 +time/exploration sampling (s) 2.84312 +time/logging (s) 0.00629534 +time/saving (s) 0.00976641 +time/training (s) 11.9757 +time/epoch (s) 17.3009 +time/total (s) 5690.41 +Epoch -717 +------------------------------ ---------------- +2022-05-15 19:37:34.936642 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -716 finished +------------------------------ ---------------- +epoch -716 +replay_buffer/size 999047 +trainer/num train calls 285000 +trainer/QF1 Loss 0.800767 +trainer/QF2 Loss 0.675164 +trainer/Policy Loss 19.9631 +trainer/Q1 Predictions Mean -73.678 +trainer/Q1 Predictions Std 18.1161 +trainer/Q1 Predictions Max -0.470155 +trainer/Q1 Predictions Min -86.3925 +trainer/Q2 Predictions Mean -73.4305 +trainer/Q2 Predictions Std 18.1638 +trainer/Q2 Predictions Max 0.350582 +trainer/Q2 Predictions Min -86.3335 +trainer/Q Targets Mean -73.436 +trainer/Q Targets Std 17.9811 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0586 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0170433 +trainer/policy/mean Std 0.716284 +trainer/policy/mean Max 0.998647 +trainer/policy/mean Min -0.999176 +trainer/policy/std Mean 0.428373 +trainer/policy/std Std 0.0220246 +trainer/policy/std Max 0.452625 +trainer/policy/std Min 0.390798 +trainer/Advantage Weights Mean 3.58503 +trainer/Advantage Weights Std 13.7817 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.73002e-14 +trainer/Advantage Score Mean -0.323129 +trainer/Advantage Score Std 0.543316 +trainer/Advantage Score Max 2.29608 +trainer/Advantage Score Min -3.16881 +trainer/V1 Predictions Mean -73.1659 +trainer/V1 Predictions Std 18.0475 +trainer/V1 Predictions Max -0.214036 +trainer/V1 Predictions Min -85.8877 +trainer/VF Loss 0.0680576 +expl/num steps total 285000 +expl/num paths total 314 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0130401 +expl/Actions Std 0.852146 +expl/Actions Max 2.25087 +expl/Actions Min -2.4444 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 280559 +eval/num paths total 285 +eval/path length Mean 724 +eval/path length Std 0 +eval/path length Max 724 +eval/path length Min 724 +eval/Rewards Mean 0.00138122 +eval/Rewards Std 0.037139 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0140357 +eval/Actions Std 0.715181 +eval/Actions Max 0.999037 +eval/Actions Min -0.999458 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.93786e-06 +time/evaluation sampling (s) 2.45673 +time/exploration sampling (s) 2.82818 +time/logging (s) 0.00776875 +time/saving (s) 0.0129501 +time/training (s) 12.9121 +time/epoch (s) 18.2177 +time/total (s) 5708.63 +Epoch -716 +------------------------------ ---------------- +2022-05-15 19:37:52.943137 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -715 finished +------------------------------ ---------------- +epoch -715 +replay_buffer/size 999047 +trainer/num train calls 286000 +trainer/QF1 Loss 1.19539 +trainer/QF2 Loss 1.24184 +trainer/Policy Loss 12.0087 +trainer/Q1 Predictions Mean -74.1731 +trainer/Q1 Predictions Std 18.5234 +trainer/Q1 Predictions Max -0.344891 +trainer/Q1 Predictions Min -87.3772 +trainer/Q2 Predictions Mean -74.1792 +trainer/Q2 Predictions Std 18.5184 +trainer/Q2 Predictions Max -0.174245 +trainer/Q2 Predictions Min -87.1238 +trainer/Q Targets Mean -73.8102 +trainer/Q Targets Std 18.1508 +trainer/Q Targets Max 0.0114188 +trainer/Q Targets Min -86.5643 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0357165 +trainer/policy/mean Std 0.714761 +trainer/policy/mean Max 0.998728 +trainer/policy/mean Min -0.999182 +trainer/policy/std Mean 0.427899 +trainer/policy/std Std 0.0214523 +trainer/policy/std Max 0.448704 +trainer/policy/std Min 0.392923 +trainer/Advantage Weights Mean 2.18807 +trainer/Advantage Weights Std 13.2923 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.8012e-13 +trainer/Advantage Score Mean -0.580473 +trainer/Advantage Score Std 0.472499 +trainer/Advantage Score Max 0.840069 +trainer/Advantage Score Min -2.85983 +trainer/V1 Predictions Mean -73.5386 +trainer/V1 Predictions Std 18.4966 +trainer/V1 Predictions Max 1.34737 +trainer/V1 Predictions Min -86.3487 +trainer/VF Loss 0.0649731 +expl/num steps total 286000 +expl/num paths total 315 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0226806 +expl/Actions Std 0.815337 +expl/Actions Max 2.35451 +expl/Actions Min -2.27307 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 281559 +eval/num paths total 286 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0290766 +eval/Actions Std 0.722028 +eval/Actions Max 0.998992 +eval/Actions Min -0.999023 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.86801e-06 +time/evaluation sampling (s) 2.63714 +time/exploration sampling (s) 2.73052 +time/logging (s) 0.00664729 +time/saving (s) 0.00937916 +time/training (s) 12.6135 +time/epoch (s) 17.9972 +time/total (s) 5726.63 +Epoch -715 +------------------------------ ---------------- +2022-05-15 19:38:10.949453 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -714 finished +------------------------------ ---------------- +epoch -714 +replay_buffer/size 999047 +trainer/num train calls 287000 +trainer/QF1 Loss 0.805788 +trainer/QF2 Loss 0.819233 +trainer/Policy Loss 61.7286 +trainer/Q1 Predictions Mean -73.9112 +trainer/Q1 Predictions Std 17.5049 +trainer/Q1 Predictions Max -0.433401 +trainer/Q1 Predictions Min -86.4272 +trainer/Q2 Predictions Mean -73.8913 +trainer/Q2 Predictions Std 17.5582 +trainer/Q2 Predictions Max 0.0467711 +trainer/Q2 Predictions Min -86.6521 +trainer/Q Targets Mean -74.2194 +trainer/Q Targets Std 17.5109 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1064 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00563861 +trainer/policy/mean Std 0.70642 +trainer/policy/mean Max 0.997815 +trainer/policy/mean Min -0.998494 +trainer/policy/std Mean 0.426713 +trainer/policy/std Std 0.0221113 +trainer/policy/std Max 0.451535 +trainer/policy/std Min 0.388888 +trainer/Advantage Weights Mean 12.4251 +trainer/Advantage Weights Std 28.8236 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.62313e-17 +trainer/Advantage Score Mean -0.213871 +trainer/Advantage Score Std 0.565678 +trainer/Advantage Score Max 1.61804 +trainer/Advantage Score Min -3.86596 +trainer/V1 Predictions Mean -73.995 +trainer/V1 Predictions Std 17.629 +trainer/V1 Predictions Max -0.241441 +trainer/V1 Predictions Min -87.2197 +trainer/VF Loss 0.0883798 +expl/num steps total 287000 +expl/num paths total 316 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0103164 +expl/Actions Std 0.835213 +expl/Actions Max 2.45824 +expl/Actions Min -2.42576 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 282559 +eval/num paths total 287 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0250909 +eval/Actions Std 0.749881 +eval/Actions Max 0.997492 +eval/Actions Min -0.99742 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.05194e-06 +time/evaluation sampling (s) 2.5825 +time/exploration sampling (s) 2.75569 +time/logging (s) 0.00743089 +time/saving (s) 0.012112 +time/training (s) 12.643 +time/epoch (s) 18.0008 +time/total (s) 5744.64 +Epoch -714 +------------------------------ ---------------- +2022-05-15 19:38:28.924840 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -713 finished +------------------------------ ---------------- +epoch -713 +replay_buffer/size 999047 +trainer/num train calls 288000 +trainer/QF1 Loss 0.897408 +trainer/QF2 Loss 1.14911 +trainer/Policy Loss 18.1795 +trainer/Q1 Predictions Mean -74.1062 +trainer/Q1 Predictions Std 17.2463 +trainer/Q1 Predictions Max -0.836177 +trainer/Q1 Predictions Min -86.8816 +trainer/Q2 Predictions Mean -74.0015 +trainer/Q2 Predictions Std 17.2776 +trainer/Q2 Predictions Max -0.35843 +trainer/Q2 Predictions Min -86.5437 +trainer/Q Targets Mean -74.2415 +trainer/Q Targets Std 17.3901 +trainer/Q Targets Max 0.0201914 +trainer/Q Targets Min -87.4273 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0158915 +trainer/policy/mean Std 0.704501 +trainer/policy/mean Max 0.996543 +trainer/policy/mean Min -0.994661 +trainer/policy/std Mean 0.429277 +trainer/policy/std Std 0.0226071 +trainer/policy/std Max 0.454838 +trainer/policy/std Min 0.38943 +trainer/Advantage Weights Mean 2.89436 +trainer/Advantage Weights Std 12.447 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.81547e-20 +trainer/Advantage Score Mean -0.374883 +trainer/Advantage Score Std 0.64448 +trainer/Advantage Score Max 0.738937 +trainer/Advantage Score Min -4.47126 +trainer/V1 Predictions Mean -73.9124 +trainer/V1 Predictions Std 17.6246 +trainer/V1 Predictions Max 1.47166 +trainer/V1 Predictions Min -87.2866 +trainer/VF Loss 0.0636225 +expl/num steps total 288000 +expl/num paths total 317 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0853818 +expl/Actions Std 0.834726 +expl/Actions Max 2.51838 +expl/Actions Min -2.36567 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 283559 +eval/num paths total 288 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0311593 +eval/Actions Std 0.713818 +eval/Actions Max 0.999794 +eval/Actions Min -0.998364 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94065e-06 +time/evaluation sampling (s) 2.44708 +time/exploration sampling (s) 2.81644 +time/logging (s) 0.00712599 +time/saving (s) 0.0101522 +time/training (s) 12.685 +time/epoch (s) 17.9658 +time/total (s) 5762.61 +Epoch -713 +------------------------------ ---------------- +2022-05-15 19:38:46.946276 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -712 finished +------------------------------ ---------------- +epoch -712 +replay_buffer/size 999047 +trainer/num train calls 289000 +trainer/QF1 Loss 0.696196 +trainer/QF2 Loss 0.785853 +trainer/Policy Loss 28.747 +trainer/Q1 Predictions Mean -72.2237 +trainer/Q1 Predictions Std 20.6163 +trainer/Q1 Predictions Max -0.467978 +trainer/Q1 Predictions Min -87.8364 +trainer/Q2 Predictions Mean -72.1712 +trainer/Q2 Predictions Std 20.6085 +trainer/Q2 Predictions Max -0.140031 +trainer/Q2 Predictions Min -87.9321 +trainer/Q Targets Mean -72.3115 +trainer/Q Targets Std 20.5923 +trainer/Q Targets Max 0.245642 +trainer/Q Targets Min -86.8888 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0119758 +trainer/policy/mean Std 0.717611 +trainer/policy/mean Max 0.99771 +trainer/policy/mean Min -0.997706 +trainer/policy/std Mean 0.430218 +trainer/policy/std Std 0.0221953 +trainer/policy/std Max 0.454736 +trainer/policy/std Min 0.391513 +trainer/Advantage Weights Mean 4.91689 +trainer/Advantage Weights Std 17.3626 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.22597e-20 +trainer/Advantage Score Mean -0.348976 +trainer/Advantage Score Std 0.730752 +trainer/Advantage Score Max 0.88691 +trainer/Advantage Score Min -4.5848 +trainer/V1 Predictions Mean -71.9831 +trainer/V1 Predictions Std 20.8657 +trainer/V1 Predictions Max 1.5506 +trainer/V1 Predictions Min -86.8556 +trainer/VF Loss 0.0822581 +expl/num steps total 289000 +expl/num paths total 318 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0641614 +expl/Actions Std 0.787049 +expl/Actions Max 2.53137 +expl/Actions Min -2.44329 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 284559 +eval/num paths total 289 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0041632 +eval/Actions Std 0.732613 +eval/Actions Max 0.997984 +eval/Actions Min -0.99961 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.32808e-06 +time/evaluation sampling (s) 2.49201 +time/exploration sampling (s) 2.79493 +time/logging (s) 0.00750234 +time/saving (s) 0.0121905 +time/training (s) 12.7085 +time/epoch (s) 18.0152 +time/total (s) 5780.63 +Epoch -712 +------------------------------ ---------------- +2022-05-15 19:39:03.733702 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -711 finished +------------------------------ ---------------- +epoch -711 +replay_buffer/size 999047 +trainer/num train calls 290000 +trainer/QF1 Loss 0.442747 +trainer/QF2 Loss 0.537719 +trainer/Policy Loss 9.93072 +trainer/Q1 Predictions Mean -74.2414 +trainer/Q1 Predictions Std 17.0294 +trainer/Q1 Predictions Max -1.53066 +trainer/Q1 Predictions Min -86.5013 +trainer/Q2 Predictions Mean -74.1683 +trainer/Q2 Predictions Std 17.0878 +trainer/Q2 Predictions Max -1.58075 +trainer/Q2 Predictions Min -86.4659 +trainer/Q Targets Mean -74.3965 +trainer/Q Targets Std 17.0468 +trainer/Q Targets Max -1.32127 +trainer/Q Targets Min -86.4709 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0131837 +trainer/policy/mean Std 0.704969 +trainer/policy/mean Max 0.995906 +trainer/policy/mean Min -0.995842 +trainer/policy/std Mean 0.429881 +trainer/policy/std Std 0.0212945 +trainer/policy/std Max 0.454707 +trainer/policy/std Min 0.394427 +trainer/Advantage Weights Mean 3.33403 +trainer/Advantage Weights Std 14.9114 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.51365e-11 +trainer/Advantage Score Mean -0.319864 +trainer/Advantage Score Std 0.44933 +trainer/Advantage Score Max 0.780369 +trainer/Advantage Score Min -2.34545 +trainer/V1 Predictions Mean -74.1312 +trainer/V1 Predictions Std 17.1887 +trainer/V1 Predictions Max -0.38158 +trainer/V1 Predictions Min -86.3344 +trainer/VF Loss 0.0407969 +expl/num steps total 290000 +expl/num paths total 319 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0392713 +expl/Actions Std 0.771142 +expl/Actions Max 2.41085 +expl/Actions Min -2.08284 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 285559 +eval/num paths total 290 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0397759 +eval/Actions Std 0.663038 +eval/Actions Max 0.997527 +eval/Actions Min -0.998085 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.16324e-06 +time/evaluation sampling (s) 2.49768 +time/exploration sampling (s) 2.6721 +time/logging (s) 0.00659822 +time/saving (s) 0.00946082 +time/training (s) 11.5925 +time/epoch (s) 16.7783 +time/total (s) 5797.41 +Epoch -711 +------------------------------ ---------------- +2022-05-15 19:39:21.463507 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -710 finished +------------------------------ ---------------- +epoch -710 +replay_buffer/size 999047 +trainer/num train calls 291000 +trainer/QF1 Loss 0.517117 +trainer/QF2 Loss 0.472539 +trainer/Policy Loss 8.58203 +trainer/Q1 Predictions Mean -74.1665 +trainer/Q1 Predictions Std 16.4883 +trainer/Q1 Predictions Max -0.573565 +trainer/Q1 Predictions Min -86.5774 +trainer/Q2 Predictions Mean -74.1319 +trainer/Q2 Predictions Std 16.5356 +trainer/Q2 Predictions Max -0.348759 +trainer/Q2 Predictions Min -86.4291 +trainer/Q Targets Mean -74.08 +trainer/Q Targets Std 16.6217 +trainer/Q Targets Max -0.193814 +trainer/Q Targets Min -86.5272 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0144076 +trainer/policy/mean Std 0.716375 +trainer/policy/mean Max 0.997999 +trainer/policy/mean Min -0.998067 +trainer/policy/std Mean 0.42832 +trainer/policy/std Std 0.0198299 +trainer/policy/std Max 0.450776 +trainer/policy/std Min 0.393705 +trainer/Advantage Weights Mean 1.79122 +trainer/Advantage Weights Std 11.0448 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.26744e-15 +trainer/Advantage Score Mean -0.413074 +trainer/Advantage Score Std 0.514402 +trainer/Advantage Score Max 1.04111 +trainer/Advantage Score Min -3.43018 +trainer/V1 Predictions Mean -73.8176 +trainer/V1 Predictions Std 16.6727 +trainer/V1 Predictions Max 0.0630814 +trainer/V1 Predictions Min -86.4098 +trainer/VF Loss 0.0521858 +expl/num steps total 291000 +expl/num paths total 320 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.02386 +expl/Actions Std 0.841219 +expl/Actions Max 2.34508 +expl/Actions Min -2.2339 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 286253 +eval/num paths total 291 +eval/path length Mean 694 +eval/path length Std 0 +eval/path length Max 694 +eval/path length Min 694 +eval/Rewards Mean 0.00144092 +eval/Rewards Std 0.0379321 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0335882 +eval/Actions Std 0.720104 +eval/Actions Max 0.999757 +eval/Actions Min -0.999574 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.69385e-06 +time/evaluation sampling (s) 2.43388 +time/exploration sampling (s) 2.76353 +time/logging (s) 0.00668458 +time/saving (s) 0.0120627 +time/training (s) 12.5071 +time/epoch (s) 17.7233 +time/total (s) 5815.14 +Epoch -710 +------------------------------ ---------------- +2022-05-15 19:39:39.348540 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -709 finished +------------------------------ ---------------- +epoch -709 +replay_buffer/size 999047 +trainer/num train calls 292000 +trainer/QF1 Loss 0.6707 +trainer/QF2 Loss 0.780602 +trainer/Policy Loss 4.47581 +trainer/Q1 Predictions Mean -73.353 +trainer/Q1 Predictions Std 18.8225 +trainer/Q1 Predictions Max -0.279023 +trainer/Q1 Predictions Min -87.127 +trainer/Q2 Predictions Mean -73.3414 +trainer/Q2 Predictions Std 18.7936 +trainer/Q2 Predictions Max -0.351606 +trainer/Q2 Predictions Min -87.239 +trainer/Q Targets Mean -72.9824 +trainer/Q Targets Std 18.7709 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9605 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0216437 +trainer/policy/mean Std 0.703753 +trainer/policy/mean Max 0.997919 +trainer/policy/mean Min -0.99708 +trainer/policy/std Mean 0.428653 +trainer/policy/std Std 0.0208927 +trainer/policy/std Max 0.450079 +trainer/policy/std Min 0.391149 +trainer/Advantage Weights Mean 1.26311 +trainer/Advantage Weights Std 9.08027 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.93113e-20 +trainer/Advantage Score Mean -0.511084 +trainer/Advantage Score Std 0.58993 +trainer/Advantage Score Max 1.41892 +trainer/Advantage Score Min -4.3756 +trainer/V1 Predictions Mean -72.6976 +trainer/V1 Predictions Std 18.7864 +trainer/V1 Predictions Max 0.710445 +trainer/V1 Predictions Min -86.8294 +trainer/VF Loss 0.0692987 +expl/num steps total 292000 +expl/num paths total 322 +expl/path length Mean 500 +expl/path length Std 345 +expl/path length Max 845 +expl/path length Min 155 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.00782585 +expl/Actions Std 0.816017 +expl/Actions Max 2.71503 +expl/Actions Min -2.22899 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 287253 +eval/num paths total 292 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00641952 +eval/Actions Std 0.728349 +eval/Actions Max 0.998971 +eval/Actions Min -0.999542 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.36394e-06 +time/evaluation sampling (s) 2.45241 +time/exploration sampling (s) 2.7495 +time/logging (s) 0.00712812 +time/saving (s) 0.0101714 +time/training (s) 12.658 +time/epoch (s) 17.8772 +time/total (s) 5833.02 +Epoch -709 +------------------------------ ---------------- +2022-05-15 19:39:56.545817 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -708 finished +------------------------------ ---------------- +epoch -708 +replay_buffer/size 999047 +trainer/num train calls 293000 +trainer/QF1 Loss 0.64281 +trainer/QF2 Loss 0.626343 +trainer/Policy Loss 13.7286 +trainer/Q1 Predictions Mean -72.9623 +trainer/Q1 Predictions Std 18.7039 +trainer/Q1 Predictions Max -0.286115 +trainer/Q1 Predictions Min -87.055 +trainer/Q2 Predictions Mean -73.0238 +trainer/Q2 Predictions Std 18.6435 +trainer/Q2 Predictions Max -0.262618 +trainer/Q2 Predictions Min -87.13 +trainer/Q Targets Mean -72.9481 +trainer/Q Targets Std 18.9822 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9659 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0322414 +trainer/policy/mean Std 0.718024 +trainer/policy/mean Max 0.998036 +trainer/policy/mean Min -0.998703 +trainer/policy/std Mean 0.430598 +trainer/policy/std Std 0.0207938 +trainer/policy/std Max 0.451941 +trainer/policy/std Min 0.394974 +trainer/Advantage Weights Mean 2.91366 +trainer/Advantage Weights Std 12.2292 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.57223e-18 +trainer/Advantage Score Mean -0.316984 +trainer/Advantage Score Std 0.568822 +trainer/Advantage Score Max 0.652878 +trainer/Advantage Score Min -3.97287 +trainer/V1 Predictions Mean -72.6512 +trainer/V1 Predictions Std 19.1203 +trainer/V1 Predictions Max 2.11572 +trainer/V1 Predictions Min -86.8334 +trainer/VF Loss 0.0509456 +expl/num steps total 293000 +expl/num paths total 324 +expl/path length Mean 500 +expl/path length Std 23 +expl/path length Max 523 +expl/path length Min 477 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0299688 +expl/Actions Std 0.826198 +expl/Actions Max 2.42692 +expl/Actions Min -2.5491 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 288253 +eval/num paths total 293 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0099629 +eval/Actions Std 0.727156 +eval/Actions Max 0.999437 +eval/Actions Min -0.999539 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.75392e-06 +time/evaluation sampling (s) 2.57679 +time/exploration sampling (s) 2.76774 +time/logging (s) 0.00816164 +time/saving (s) 0.0116839 +time/training (s) 11.8271 +time/epoch (s) 17.1915 +time/total (s) 5850.21 +Epoch -708 +------------------------------ ---------------- +2022-05-15 19:40:13.975432 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -707 finished +------------------------------ ---------------- +epoch -707 +replay_buffer/size 999047 +trainer/num train calls 294000 +trainer/QF1 Loss 0.675839 +trainer/QF2 Loss 0.662207 +trainer/Policy Loss 9.36055 +trainer/Q1 Predictions Mean -73.3443 +trainer/Q1 Predictions Std 18.987 +trainer/Q1 Predictions Max -1.15946 +trainer/Q1 Predictions Min -87.8098 +trainer/Q2 Predictions Mean -73.3073 +trainer/Q2 Predictions Std 18.9955 +trainer/Q2 Predictions Max -1.12574 +trainer/Q2 Predictions Min -87.7839 +trainer/Q Targets Mean -72.8207 +trainer/Q Targets Std 19.1677 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9408 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0105343 +trainer/policy/mean Std 0.706612 +trainer/policy/mean Max 0.998108 +trainer/policy/mean Min -0.996405 +trainer/policy/std Mean 0.428899 +trainer/policy/std Std 0.0213615 +trainer/policy/std Max 0.451307 +trainer/policy/std Min 0.391921 +trainer/Advantage Weights Mean 2.28451 +trainer/Advantage Weights Std 12.6573 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.81674e-21 +trainer/Advantage Score Mean -0.414145 +trainer/Advantage Score Std 0.586568 +trainer/Advantage Score Max 1.0557 +trainer/Advantage Score Min -4.77572 +trainer/V1 Predictions Mean -72.5923 +trainer/V1 Predictions Std 19.3152 +trainer/V1 Predictions Max 0.642061 +trainer/V1 Predictions Min -86.8944 +trainer/VF Loss 0.0620856 +expl/num steps total 294000 +expl/num paths total 325 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0194952 +expl/Actions Std 0.86789 +expl/Actions Max 2.18736 +expl/Actions Min -2.32847 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 289253 +eval/num paths total 294 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.13255 +eval/Actions Std 0.770204 +eval/Actions Max 0.999509 +eval/Actions Min -0.999515 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.59839e-06 +time/evaluation sampling (s) 2.47753 +time/exploration sampling (s) 2.80988 +time/logging (s) 0.0066682 +time/saving (s) 0.00955871 +time/training (s) 12.1157 +time/epoch (s) 17.4194 +time/total (s) 5867.64 +Epoch -707 +------------------------------ ---------------- +2022-05-15 19:40:31.368269 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -706 finished +------------------------------ ---------------- +epoch -706 +replay_buffer/size 999047 +trainer/num train calls 295000 +trainer/QF1 Loss 0.852304 +trainer/QF2 Loss 0.831191 +trainer/Policy Loss 6.603 +trainer/Q1 Predictions Mean -74.4727 +trainer/Q1 Predictions Std 17.8253 +trainer/Q1 Predictions Max -1.52594 +trainer/Q1 Predictions Min -87.1844 +trainer/Q2 Predictions Mean -74.3995 +trainer/Q2 Predictions Std 17.7894 +trainer/Q2 Predictions Max -1.3658 +trainer/Q2 Predictions Min -87.1501 +trainer/Q Targets Mean -74.0582 +trainer/Q Targets Std 17.9217 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8393 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00299483 +trainer/policy/mean Std 0.735451 +trainer/policy/mean Max 0.996566 +trainer/policy/mean Min -0.99901 +trainer/policy/std Mean 0.428067 +trainer/policy/std Std 0.0216504 +trainer/policy/std Max 0.451339 +trainer/policy/std Min 0.391299 +trainer/Advantage Weights Mean 1.26468 +trainer/Advantage Weights Std 9.08434 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.12673e-10 +trainer/Advantage Score Mean -0.479041 +trainer/Advantage Score Std 0.43706 +trainer/Advantage Score Max 0.71148 +trainer/Advantage Score Min -2.29065 +trainer/V1 Predictions Mean -73.8811 +trainer/V1 Predictions Std 17.8103 +trainer/V1 Predictions Max -1.32351 +trainer/V1 Predictions Min -86.5179 +trainer/VF Loss 0.0457372 +expl/num steps total 295000 +expl/num paths total 327 +expl/path length Mean 500 +expl/path length Std 265 +expl/path length Max 765 +expl/path length Min 235 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0400961 +expl/Actions Std 0.825592 +expl/Actions Max 2.29531 +expl/Actions Min -2.30058 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 290253 +eval/num paths total 295 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0219106 +eval/Actions Std 0.708813 +eval/Actions Max 0.999287 +eval/Actions Min -0.99852 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.90805e-06 +time/evaluation sampling (s) 2.39676 +time/exploration sampling (s) 2.85713 +time/logging (s) 0.00742442 +time/saving (s) 0.0120205 +time/training (s) 12.1141 +time/epoch (s) 17.3875 +time/total (s) 5885.03 +Epoch -706 +------------------------------ ---------------- +2022-05-15 19:40:48.873817 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -705 finished +------------------------------ ---------------- +epoch -705 +replay_buffer/size 999047 +trainer/num train calls 296000 +trainer/QF1 Loss 0.471426 +trainer/QF2 Loss 0.545532 +trainer/Policy Loss 14.6629 +trainer/Q1 Predictions Mean -73.778 +trainer/Q1 Predictions Std 16.8921 +trainer/Q1 Predictions Max -0.282259 +trainer/Q1 Predictions Min -88.0185 +trainer/Q2 Predictions Mean -73.8491 +trainer/Q2 Predictions Std 16.9075 +trainer/Q2 Predictions Max -0.415591 +trainer/Q2 Predictions Min -87.2471 +trainer/Q Targets Mean -73.7218 +trainer/Q Targets Std 16.6933 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8695 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00921082 +trainer/policy/mean Std 0.719486 +trainer/policy/mean Max 0.999033 +trainer/policy/mean Min -0.997599 +trainer/policy/std Mean 0.428214 +trainer/policy/std Std 0.0204529 +trainer/policy/std Max 0.451778 +trainer/policy/std Min 0.392505 +trainer/Advantage Weights Mean 3.09918 +trainer/Advantage Weights Std 14.9826 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3776e-13 +trainer/Advantage Score Mean -0.383408 +trainer/Advantage Score Std 0.473357 +trainer/Advantage Score Max 1.1496 +trainer/Advantage Score Min -2.96133 +trainer/V1 Predictions Mean -73.4669 +trainer/V1 Predictions Std 16.8089 +trainer/V1 Predictions Max 0.725356 +trainer/V1 Predictions Min -86.8249 +trainer/VF Loss 0.0517644 +expl/num steps total 296000 +expl/num paths total 328 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0263683 +expl/Actions Std 0.837047 +expl/Actions Max 2.53744 +expl/Actions Min -2.37623 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 290928 +eval/num paths total 296 +eval/path length Mean 675 +eval/path length Std 0 +eval/path length Max 675 +eval/path length Min 675 +eval/Rewards Mean 0.00148148 +eval/Rewards Std 0.0384615 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0162766 +eval/Actions Std 0.739546 +eval/Actions Max 0.999263 +eval/Actions Min -0.999298 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.54577e-06 +time/evaluation sampling (s) 2.2822 +time/exploration sampling (s) 2.88102 +time/logging (s) 0.00702993 +time/saving (s) 0.0150245 +time/training (s) 12.3113 +time/epoch (s) 17.4966 +time/total (s) 5902.53 +Epoch -705 +------------------------------ ---------------- +2022-05-15 19:41:06.932236 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -704 finished +------------------------------ ---------------- +epoch -704 +replay_buffer/size 999047 +trainer/num train calls 297000 +trainer/QF1 Loss 0.923905 +trainer/QF2 Loss 0.913131 +trainer/Policy Loss 25.7373 +trainer/Q1 Predictions Mean -73.4326 +trainer/Q1 Predictions Std 17.4122 +trainer/Q1 Predictions Max -0.878524 +trainer/Q1 Predictions Min -86.5769 +trainer/Q2 Predictions Mean -73.4059 +trainer/Q2 Predictions Std 17.4575 +trainer/Q2 Predictions Max -1.01432 +trainer/Q2 Predictions Min -86.4865 +trainer/Q Targets Mean -73.52 +trainer/Q Targets Std 17.5821 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7871 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0303848 +trainer/policy/mean Std 0.716087 +trainer/policy/mean Max 0.997898 +trainer/policy/mean Min -0.995812 +trainer/policy/std Mean 0.426732 +trainer/policy/std Std 0.0211907 +trainer/policy/std Max 0.449009 +trainer/policy/std Min 0.390405 +trainer/Advantage Weights Mean 4.80531 +trainer/Advantage Weights Std 18.4976 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.26235e-13 +trainer/Advantage Score Mean -0.334865 +trainer/Advantage Score Std 0.504135 +trainer/Advantage Score Max 1.25751 +trainer/Advantage Score Min -2.91172 +trainer/V1 Predictions Mean -73.321 +trainer/V1 Predictions Std 17.4989 +trainer/V1 Predictions Max 0.316613 +trainer/V1 Predictions Min -86.7849 +trainer/VF Loss 0.0604071 +expl/num steps total 297000 +expl/num paths total 329 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0435817 +expl/Actions Std 0.844312 +expl/Actions Max 2.68737 +expl/Actions Min -2.77248 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 291928 +eval/num paths total 297 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.181198 +eval/Actions Std 0.756715 +eval/Actions Max 0.999085 +eval/Actions Min -0.999069 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.49082e-06 +time/evaluation sampling (s) 2.38164 +time/exploration sampling (s) 2.98172 +time/logging (s) 0.00734238 +time/saving (s) 0.011938 +time/training (s) 12.6679 +time/epoch (s) 18.0505 +time/total (s) 5920.59 +Epoch -704 +------------------------------ ---------------- +2022-05-15 19:41:24.444277 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -703 finished +------------------------------ ---------------- +epoch -703 +replay_buffer/size 999047 +trainer/num train calls 298000 +trainer/QF1 Loss 0.716177 +trainer/QF2 Loss 0.748228 +trainer/Policy Loss 17.2299 +trainer/Q1 Predictions Mean -72.7782 +trainer/Q1 Predictions Std 20.5321 +trainer/Q1 Predictions Max -0.878542 +trainer/Q1 Predictions Min -87.2272 +trainer/Q2 Predictions Mean -72.7401 +trainer/Q2 Predictions Std 20.5919 +trainer/Q2 Predictions Max -0.182258 +trainer/Q2 Predictions Min -87.1749 +trainer/Q Targets Mean -72.5478 +trainer/Q Targets Std 20.5748 +trainer/Q Targets Max 1.45606 +trainer/Q Targets Min -86.8564 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0420905 +trainer/policy/mean Std 0.708363 +trainer/policy/mean Max 0.999568 +trainer/policy/mean Min -0.9986 +trainer/policy/std Mean 0.428213 +trainer/policy/std Std 0.0221406 +trainer/policy/std Max 0.449299 +trainer/policy/std Min 0.388943 +trainer/Advantage Weights Mean 2.58586 +trainer/Advantage Weights Std 11.2302 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.29043e-17 +trainer/Advantage Score Mean -0.360448 +trainer/Advantage Score Std 0.555039 +trainer/Advantage Score Max 0.524651 +trainer/Advantage Score Min -3.8889 +trainer/V1 Predictions Mean -72.2966 +trainer/V1 Predictions Std 20.777 +trainer/V1 Predictions Max 0.959088 +trainer/V1 Predictions Min -86.7114 +trainer/VF Loss 0.050244 +expl/num steps total 298000 +expl/num paths total 330 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0484167 +expl/Actions Std 0.853677 +expl/Actions Max 2.4285 +expl/Actions Min -2.3162 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 292928 +eval/num paths total 298 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.196242 +eval/Actions Std 0.569123 +eval/Actions Max 0.998101 +eval/Actions Min -0.999922 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.50991e-06 +time/evaluation sampling (s) 2.28162 +time/exploration sampling (s) 2.79306 +time/logging (s) 0.00660094 +time/saving (s) 0.00941486 +time/training (s) 12.4123 +time/epoch (s) 17.503 +time/total (s) 5938.09 +Epoch -703 +------------------------------ ---------------- +2022-05-15 19:41:42.423922 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -702 finished +------------------------------ ---------------- +epoch -702 +replay_buffer/size 999047 +trainer/num train calls 299000 +trainer/QF1 Loss 0.853957 +trainer/QF2 Loss 0.714065 +trainer/Policy Loss 31.1443 +trainer/Q1 Predictions Mean -73.5189 +trainer/Q1 Predictions Std 18.5432 +trainer/Q1 Predictions Max -0.510737 +trainer/Q1 Predictions Min -86.7351 +trainer/Q2 Predictions Mean -73.5637 +trainer/Q2 Predictions Std 18.5501 +trainer/Q2 Predictions Max -0.376358 +trainer/Q2 Predictions Min -86.8365 +trainer/Q Targets Mean -73.8687 +trainer/Q Targets Std 18.7213 +trainer/Q Targets Max 2.23352 +trainer/Q Targets Min -87.5033 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0140244 +trainer/policy/mean Std 0.716406 +trainer/policy/mean Max 0.999455 +trainer/policy/mean Min -0.999576 +trainer/policy/std Mean 0.427589 +trainer/policy/std Std 0.0206988 +trainer/policy/std Max 0.449721 +trainer/policy/std Min 0.39182 +trainer/Advantage Weights Mean 8.47732 +trainer/Advantage Weights Std 22.3559 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.4225e-29 +trainer/Advantage Score Mean -0.153051 +trainer/Advantage Score Std 0.624731 +trainer/Advantage Score Max 1.12173 +trainer/Advantage Score Min -6.58902 +trainer/V1 Predictions Mean -73.6492 +trainer/V1 Predictions Std 18.757 +trainer/V1 Predictions Max 5.40507 +trainer/V1 Predictions Min -87.385 +trainer/VF Loss 0.0706128 +expl/num steps total 299000 +expl/num paths total 331 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.15054 +expl/Actions Std 0.888276 +expl/Actions Max 2.29478 +expl/Actions Min -2.51395 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 293928 +eval/num paths total 299 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0472473 +eval/Actions Std 0.700471 +eval/Actions Max 0.999489 +eval/Actions Min -0.998804 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.15439e-06 +time/evaluation sampling (s) 2.31372 +time/exploration sampling (s) 2.98778 +time/logging (s) 0.00735339 +time/saving (s) 0.0120901 +time/training (s) 12.6528 +time/epoch (s) 17.9737 +time/total (s) 5956.07 +Epoch -702 +------------------------------ ---------------- +2022-05-15 19:42:00.451827 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -701 finished +------------------------------ ---------------- +epoch -701 +replay_buffer/size 999047 +trainer/num train calls 300000 +trainer/QF1 Loss 0.853869 +trainer/QF2 Loss 0.767478 +trainer/Policy Loss 36.1478 +trainer/Q1 Predictions Mean -74.3124 +trainer/Q1 Predictions Std 17.4826 +trainer/Q1 Predictions Max -1.82021 +trainer/Q1 Predictions Min -86.7625 +trainer/Q2 Predictions Mean -74.3192 +trainer/Q2 Predictions Std 17.3978 +trainer/Q2 Predictions Max -1.80577 +trainer/Q2 Predictions Min -86.8398 +trainer/Q Targets Mean -74.4075 +trainer/Q Targets Std 17.3718 +trainer/Q Targets Max -3.37501 +trainer/Q Targets Min -86.9889 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00844463 +trainer/policy/mean Std 0.711283 +trainer/policy/mean Max 0.996906 +trainer/policy/mean Min -0.997708 +trainer/policy/std Mean 0.426314 +trainer/policy/std Std 0.0198448 +trainer/policy/std Max 0.446258 +trainer/policy/std Min 0.391646 +trainer/Advantage Weights Mean 5.55114 +trainer/Advantage Weights Std 20.0166 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.36078e-12 +trainer/Advantage Score Mean -0.296668 +trainer/Advantage Score Std 0.435088 +trainer/Advantage Score Max 0.967274 +trainer/Advantage Score Min -2.64188 +trainer/V1 Predictions Mean -74.2306 +trainer/V1 Predictions Std 17.4153 +trainer/V1 Predictions Max -2.90323 +trainer/V1 Predictions Min -86.6337 +trainer/VF Loss 0.0442687 +expl/num steps total 300000 +expl/num paths total 332 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0435579 +expl/Actions Std 0.813939 +expl/Actions Max 2.30174 +expl/Actions Min -2.63915 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 294928 +eval/num paths total 300 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0264691 +eval/Actions Std 0.733728 +eval/Actions Max 0.999726 +eval/Actions Min -0.999608 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.86568e-06 +time/evaluation sampling (s) 2.35315 +time/exploration sampling (s) 2.77338 +time/logging (s) 0.00795093 +time/saving (s) 0.013003 +time/training (s) 12.8726 +time/epoch (s) 18.0201 +time/total (s) 5974.1 +Epoch -701 +------------------------------ ---------------- +2022-05-15 19:42:18.502317 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -700 finished +------------------------------ ---------------- +epoch -700 +replay_buffer/size 999047 +trainer/num train calls 301000 +trainer/QF1 Loss 0.607576 +trainer/QF2 Loss 0.625443 +trainer/Policy Loss 2.18736 +trainer/Q1 Predictions Mean -74.8342 +trainer/Q1 Predictions Std 15.7469 +trainer/Q1 Predictions Max -2.92472 +trainer/Q1 Predictions Min -86.1936 +trainer/Q2 Predictions Mean -74.8248 +trainer/Q2 Predictions Std 15.7049 +trainer/Q2 Predictions Max -3.02441 +trainer/Q2 Predictions Min -86.622 +trainer/Q Targets Mean -74.7943 +trainer/Q Targets Std 15.5966 +trainer/Q Targets Max -3.63088 +trainer/Q Targets Min -86.2242 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0053828 +trainer/policy/mean Std 0.717159 +trainer/policy/mean Max 0.999339 +trainer/policy/mean Min -0.996811 +trainer/policy/std Mean 0.426687 +trainer/policy/std Std 0.0205487 +trainer/policy/std Max 0.447174 +trainer/policy/std Min 0.390438 +trainer/Advantage Weights Mean 1.37788 +trainer/Advantage Weights Std 10.8369 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.68798e-18 +trainer/Advantage Score Mean -0.638749 +trainer/Advantage Score Std 0.537134 +trainer/Advantage Score Max 1.16617 +trainer/Advantage Score Min -3.92846 +trainer/V1 Predictions Mean -74.4422 +trainer/V1 Predictions Std 15.929 +trainer/V1 Predictions Max -2.74298 +trainer/V1 Predictions Min -86.1637 +trainer/VF Loss 0.0764349 +expl/num steps total 301000 +expl/num paths total 333 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0385589 +expl/Actions Std 0.84197 +expl/Actions Max 2.39888 +expl/Actions Min -2.40336 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 295928 +eval/num paths total 301 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0668873 +eval/Actions Std 0.736397 +eval/Actions Max 0.999149 +eval/Actions Min -0.999032 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.11201e-06 +time/evaluation sampling (s) 2.36093 +time/exploration sampling (s) 2.71762 +time/logging (s) 0.00752012 +time/saving (s) 0.023945 +time/training (s) 12.9315 +time/epoch (s) 18.0415 +time/total (s) 5992.14 +Epoch -700 +------------------------------ ---------------- +2022-05-15 19:42:36.192770 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -699 finished +------------------------------ ---------------- +epoch -699 +replay_buffer/size 999047 +trainer/num train calls 302000 +trainer/QF1 Loss 0.967323 +trainer/QF2 Loss 0.883193 +trainer/Policy Loss 20.3829 +trainer/Q1 Predictions Mean -73.5084 +trainer/Q1 Predictions Std 18.5614 +trainer/Q1 Predictions Max -2.73007 +trainer/Q1 Predictions Min -87.489 +trainer/Q2 Predictions Mean -73.518 +trainer/Q2 Predictions Std 18.5342 +trainer/Q2 Predictions Max -2.63327 +trainer/Q2 Predictions Min -87.4422 +trainer/Q Targets Mean -73.159 +trainer/Q Targets Std 18.6455 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6471 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0139991 +trainer/policy/mean Std 0.712701 +trainer/policy/mean Max 0.999434 +trainer/policy/mean Min -0.999265 +trainer/policy/std Mean 0.426487 +trainer/policy/std Std 0.0218721 +trainer/policy/std Max 0.451016 +trainer/policy/std Min 0.385597 +trainer/Advantage Weights Mean 3.2088 +trainer/Advantage Weights Std 15.7823 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.55989e-10 +trainer/Advantage Score Mean -0.383197 +trainer/Advantage Score Std 0.406803 +trainer/Advantage Score Max 1.24242 +trainer/Advantage Score Min -2.13103 +trainer/V1 Predictions Mean -73.0177 +trainer/V1 Predictions Std 18.3805 +trainer/V1 Predictions Max -2.36723 +trainer/V1 Predictions Min -86.4672 +trainer/VF Loss 0.0446628 +expl/num steps total 302000 +expl/num paths total 334 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0163965 +expl/Actions Std 0.781718 +expl/Actions Max 2.57408 +expl/Actions Min -2.16114 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 296928 +eval/num paths total 302 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0849309 +eval/Actions Std 0.714038 +eval/Actions Max 0.999422 +eval/Actions Min -0.999343 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.0743e-06 +time/evaluation sampling (s) 2.52181 +time/exploration sampling (s) 2.76243 +time/logging (s) 0.00829512 +time/saving (s) 0.0127712 +time/training (s) 12.3775 +time/epoch (s) 17.6828 +time/total (s) 6009.83 +Epoch -699 +------------------------------ ---------------- +2022-05-15 19:42:53.834934 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -698 finished +------------------------------ ---------------- +epoch -698 +replay_buffer/size 999047 +trainer/num train calls 303000 +trainer/QF1 Loss 0.746912 +trainer/QF2 Loss 0.838442 +trainer/Policy Loss 50.9962 +trainer/Q1 Predictions Mean -72.937 +trainer/Q1 Predictions Std 20.2959 +trainer/Q1 Predictions Max -0.279956 +trainer/Q1 Predictions Min -87.0898 +trainer/Q2 Predictions Mean -72.8918 +trainer/Q2 Predictions Std 20.3145 +trainer/Q2 Predictions Max -0.323438 +trainer/Q2 Predictions Min -87.1542 +trainer/Q Targets Mean -73.1864 +trainer/Q Targets Std 20.052 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8781 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00356442 +trainer/policy/mean Std 0.705039 +trainer/policy/mean Max 0.999347 +trainer/policy/mean Min -0.99859 +trainer/policy/std Mean 0.426282 +trainer/policy/std Std 0.0215177 +trainer/policy/std Max 0.452983 +trainer/policy/std Min 0.388102 +trainer/Advantage Weights Mean 11.8118 +trainer/Advantage Weights Std 27.7007 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.56929e-11 +trainer/Advantage Score Mean -0.153434 +trainer/Advantage Score Std 0.544717 +trainer/Advantage Score Max 1.52444 +trainer/Advantage Score Min -2.48778 +trainer/V1 Predictions Mean -72.8904 +trainer/V1 Predictions Std 20.2305 +trainer/V1 Predictions Max -0.561743 +trainer/V1 Predictions Min -86.8618 +trainer/VF Loss 0.0840089 +expl/num steps total 303000 +expl/num paths total 335 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00889522 +expl/Actions Std 0.822624 +expl/Actions Max 2.24615 +expl/Actions Min -2.14409 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 297928 +eval/num paths total 303 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00944895 +eval/Actions Std 0.801543 +eval/Actions Max 0.999101 +eval/Actions Min -0.997649 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.10596e-06 +time/evaluation sampling (s) 2.34986 +time/exploration sampling (s) 2.60327 +time/logging (s) 0.00888676 +time/saving (s) 0.0140664 +time/training (s) 12.6584 +time/epoch (s) 17.6344 +time/total (s) 6027.47 +Epoch -698 +------------------------------ ---------------- +2022-05-15 19:43:11.378597 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -697 finished +------------------------------ ---------------- +epoch -697 +replay_buffer/size 999047 +trainer/num train calls 304000 +trainer/QF1 Loss 0.848952 +trainer/QF2 Loss 0.96059 +trainer/Policy Loss 7.34477 +trainer/Q1 Predictions Mean -73.3457 +trainer/Q1 Predictions Std 18.1917 +trainer/Q1 Predictions Max -0.770212 +trainer/Q1 Predictions Min -87.6452 +trainer/Q2 Predictions Mean -73.2864 +trainer/Q2 Predictions Std 18.1352 +trainer/Q2 Predictions Max -0.424788 +trainer/Q2 Predictions Min -87.3551 +trainer/Q Targets Mean -72.9976 +trainer/Q Targets Std 18.3993 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3771 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0210261 +trainer/policy/mean Std 0.719515 +trainer/policy/mean Max 0.99977 +trainer/policy/mean Min -0.999658 +trainer/policy/std Mean 0.426968 +trainer/policy/std Std 0.0208789 +trainer/policy/std Max 0.453787 +trainer/policy/std Min 0.389334 +trainer/Advantage Weights Mean 0.881114 +trainer/Advantage Weights Std 6.53562 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.46965e-18 +trainer/Advantage Score Mean -0.505986 +trainer/Advantage Score Std 0.556196 +trainer/Advantage Score Max 0.561295 +trainer/Advantage Score Min -4.02025 +trainer/V1 Predictions Mean -72.7709 +trainer/V1 Predictions Std 18.5061 +trainer/V1 Predictions Max 1.21753 +trainer/V1 Predictions Min -87.3004 +trainer/VF Loss 0.0589502 +expl/num steps total 304000 +expl/num paths total 336 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -9.77135e-06 +expl/Actions Std 0.791654 +expl/Actions Max 2.41996 +expl/Actions Min -2.38451 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 298928 +eval/num paths total 304 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.203261 +eval/Actions Std 0.626294 +eval/Actions Max 0.999408 +eval/Actions Min -0.999975 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.64915e-06 +time/evaluation sampling (s) 2.35073 +time/exploration sampling (s) 2.60695 +time/logging (s) 0.00787149 +time/saving (s) 0.0141852 +time/training (s) 12.5561 +time/epoch (s) 17.5358 +time/total (s) 6045.01 +Epoch -697 +------------------------------ ---------------- +2022-05-15 19:43:28.839493 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -696 finished +------------------------------ ---------------- +epoch -696 +replay_buffer/size 999047 +trainer/num train calls 305000 +trainer/QF1 Loss 0.572117 +trainer/QF2 Loss 0.725518 +trainer/Policy Loss 9.41719 +trainer/Q1 Predictions Mean -74.7863 +trainer/Q1 Predictions Std 17.3458 +trainer/Q1 Predictions Max -0.339126 +trainer/Q1 Predictions Min -86.8872 +trainer/Q2 Predictions Mean -74.8282 +trainer/Q2 Predictions Std 17.4698 +trainer/Q2 Predictions Max -0.409446 +trainer/Q2 Predictions Min -86.8231 +trainer/Q Targets Mean -74.7728 +trainer/Q Targets Std 17.2465 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4395 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00679066 +trainer/policy/mean Std 0.706563 +trainer/policy/mean Max 0.998831 +trainer/policy/mean Min -0.99914 +trainer/policy/std Mean 0.428237 +trainer/policy/std Std 0.0209419 +trainer/policy/std Max 0.45179 +trainer/policy/std Min 0.389138 +trainer/Advantage Weights Mean 2.47466 +trainer/Advantage Weights Std 12.9299 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.81282e-11 +trainer/Advantage Score Mean -0.387188 +trainer/Advantage Score Std 0.433066 +trainer/Advantage Score Max 0.947103 +trainer/Advantage Score Min -2.37572 +trainer/V1 Predictions Mean -74.4858 +trainer/V1 Predictions Std 17.3915 +trainer/V1 Predictions Max -0.460118 +trainer/V1 Predictions Min -86.2812 +trainer/VF Loss 0.0436396 +expl/num steps total 305000 +expl/num paths total 337 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0338043 +expl/Actions Std 0.80258 +expl/Actions Max 2.39611 +expl/Actions Min -2.29813 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 299928 +eval/num paths total 305 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0396379 +eval/Actions Std 0.704773 +eval/Actions Max 0.99973 +eval/Actions Min -0.999204 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.65986e-06 +time/evaluation sampling (s) 2.33854 +time/exploration sampling (s) 2.60967 +time/logging (s) 0.00750266 +time/saving (s) 0.0122966 +time/training (s) 12.4855 +time/epoch (s) 17.4535 +time/total (s) 6062.47 +Epoch -696 +------------------------------ ---------------- +2022-05-15 19:43:46.179688 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -695 finished +------------------------------ ---------------- +epoch -695 +replay_buffer/size 999047 +trainer/num train calls 306000 +trainer/QF1 Loss 1.42879 +trainer/QF2 Loss 1.34613 +trainer/Policy Loss 3.46443 +trainer/Q1 Predictions Mean -73.1917 +trainer/Q1 Predictions Std 19.8717 +trainer/Q1 Predictions Max -0.819075 +trainer/Q1 Predictions Min -87.153 +trainer/Q2 Predictions Mean -73.1149 +trainer/Q2 Predictions Std 19.8983 +trainer/Q2 Predictions Max -0.208989 +trainer/Q2 Predictions Min -87.5096 +trainer/Q Targets Mean -72.3852 +trainer/Q Targets Std 19.8314 +trainer/Q Targets Max 0.407763 +trainer/Q Targets Min -86.4726 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0259733 +trainer/policy/mean Std 0.706379 +trainer/policy/mean Max 0.9982 +trainer/policy/mean Min -0.997746 +trainer/policy/std Mean 0.429381 +trainer/policy/std Std 0.0206897 +trainer/policy/std Max 0.454154 +trainer/policy/std Min 0.393614 +trainer/Advantage Weights Mean 1.22912 +trainer/Advantage Weights Std 10.7604 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.45468e-11 +trainer/Advantage Score Mean -0.675047 +trainer/Advantage Score Std 0.422357 +trainer/Advantage Score Max 1.53051 +trainer/Advantage Score Min -2.34636 +trainer/V1 Predictions Mean -72.12 +trainer/V1 Predictions Std 19.8734 +trainer/V1 Predictions Max 1.41296 +trainer/V1 Predictions Min -86.3254 +trainer/VF Loss 0.0780609 +expl/num steps total 306000 +expl/num paths total 339 +expl/path length Mean 500 +expl/path length Std 166 +expl/path length Max 666 +expl/path length Min 334 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0348164 +expl/Actions Std 0.825976 +expl/Actions Max 2.32207 +expl/Actions Min -2.36266 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 300417 +eval/num paths total 306 +eval/path length Mean 489 +eval/path length Std 0 +eval/path length Max 489 +eval/path length Min 489 +eval/Rewards Mean 0.00204499 +eval/Rewards Std 0.0451753 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0278346 +eval/Actions Std 0.730534 +eval/Actions Max 0.998978 +eval/Actions Min -0.998066 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.66964e-06 +time/evaluation sampling (s) 2.43759 +time/exploration sampling (s) 2.58334 +time/logging (s) 0.00594839 +time/saving (s) 0.012151 +time/training (s) 12.2921 +time/epoch (s) 17.3311 +time/total (s) 6079.8 +Epoch -695 +------------------------------ ---------------- +2022-05-15 19:44:03.799089 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -694 finished +------------------------------ ---------------- +epoch -694 +replay_buffer/size 999047 +trainer/num train calls 307000 +trainer/QF1 Loss 1.43062 +trainer/QF2 Loss 1.48047 +trainer/Policy Loss 33.5205 +trainer/Q1 Predictions Mean -72.8825 +trainer/Q1 Predictions Std 18.4098 +trainer/Q1 Predictions Max -1.68675 +trainer/Q1 Predictions Min -85.611 +trainer/Q2 Predictions Mean -72.9574 +trainer/Q2 Predictions Std 18.3278 +trainer/Q2 Predictions Max -2.14457 +trainer/Q2 Predictions Min -85.8765 +trainer/Q Targets Mean -73.549 +trainer/Q Targets Std 18.5801 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1572 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0108216 +trainer/policy/mean Std 0.706404 +trainer/policy/mean Max 0.998779 +trainer/policy/mean Min -0.998209 +trainer/policy/std Mean 0.430376 +trainer/policy/std Std 0.0204878 +trainer/policy/std Max 0.455296 +trainer/policy/std Min 0.394702 +trainer/Advantage Weights Mean 9.6771 +trainer/Advantage Weights Std 24.1216 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.80073e-17 +trainer/Advantage Score Mean -0.0884128 +trainer/Advantage Score Std 0.517617 +trainer/Advantage Score Max 1.38255 +trainer/Advantage Score Min -3.85558 +trainer/V1 Predictions Mean -73.3183 +trainer/V1 Predictions Std 18.5912 +trainer/V1 Predictions Max -1.27645 +trainer/V1 Predictions Min -86.0059 +trainer/VF Loss 0.0698863 +expl/num steps total 307000 +expl/num paths total 341 +expl/path length Mean 500 +expl/path length Std 424 +expl/path length Max 924 +expl/path length Min 76 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0469656 +expl/Actions Std 0.808974 +expl/Actions Max 2.51325 +expl/Actions Min -2.44891 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 301417 +eval/num paths total 307 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0422212 +eval/Actions Std 0.72874 +eval/Actions Max 0.999314 +eval/Actions Min -0.99885 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.09385e-06 +time/evaluation sampling (s) 2.35488 +time/exploration sampling (s) 2.56934 +time/logging (s) 0.00769722 +time/saving (s) 0.0133652 +time/training (s) 12.6671 +time/epoch (s) 17.6124 +time/total (s) 6097.42 +Epoch -694 +------------------------------ ---------------- +2022-05-15 19:44:21.403285 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -693 finished +------------------------------ ---------------- +epoch -693 +replay_buffer/size 999047 +trainer/num train calls 308000 +trainer/QF1 Loss 0.717257 +trainer/QF2 Loss 0.604767 +trainer/Policy Loss 15.6674 +trainer/Q1 Predictions Mean -72.2658 +trainer/Q1 Predictions Std 18.7448 +trainer/Q1 Predictions Max -0.62185 +trainer/Q1 Predictions Min -86.8089 +trainer/Q2 Predictions Mean -72.4068 +trainer/Q2 Predictions Std 18.8812 +trainer/Q2 Predictions Max -0.392241 +trainer/Q2 Predictions Min -86.8268 +trainer/Q Targets Mean -72.6777 +trainer/Q Targets Std 18.8916 +trainer/Q Targets Max 0.446631 +trainer/Q Targets Min -87.426 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0123947 +trainer/policy/mean Std 0.721497 +trainer/policy/mean Max 0.998475 +trainer/policy/mean Min -0.998848 +trainer/policy/std Mean 0.427868 +trainer/policy/std Std 0.0218754 +trainer/policy/std Max 0.453992 +trainer/policy/std Min 0.389186 +trainer/Advantage Weights Mean 2.69789 +trainer/Advantage Weights Std 12.0148 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.73388e-12 +trainer/Advantage Score Mean -0.372475 +trainer/Advantage Score Std 0.512093 +trainer/Advantage Score Max 0.509083 +trainer/Advantage Score Min -2.63136 +trainer/V1 Predictions Mean -72.4314 +trainer/V1 Predictions Std 18.9554 +trainer/V1 Predictions Max 0.890314 +trainer/V1 Predictions Min -87.2248 +trainer/VF Loss 0.0468009 +expl/num steps total 308000 +expl/num paths total 342 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.028532 +expl/Actions Std 0.801682 +expl/Actions Max 2.33778 +expl/Actions Min -2.22096 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 301997 +eval/num paths total 308 +eval/path length Mean 580 +eval/path length Std 0 +eval/path length Max 580 +eval/path length Min 580 +eval/Rewards Mean 0.00172414 +eval/Rewards Std 0.0414869 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0324014 +eval/Actions Std 0.719741 +eval/Actions Max 0.999494 +eval/Actions Min -0.998943 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.66917e-06 +time/evaluation sampling (s) 2.32775 +time/exploration sampling (s) 2.53145 +time/logging (s) 0.00695514 +time/saving (s) 0.0132908 +time/training (s) 12.7137 +time/epoch (s) 17.5932 +time/total (s) 6115.02 +Epoch -693 +------------------------------ ---------------- +2022-05-15 19:44:39.210191 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -692 finished +------------------------------ ---------------- +epoch -692 +replay_buffer/size 999047 +trainer/num train calls 309000 +trainer/QF1 Loss 1.04909 +trainer/QF2 Loss 0.959357 +trainer/Policy Loss 31.835 +trainer/Q1 Predictions Mean -74.274 +trainer/Q1 Predictions Std 16.4332 +trainer/Q1 Predictions Max -2.75669 +trainer/Q1 Predictions Min -87.2035 +trainer/Q2 Predictions Mean -74.2272 +trainer/Q2 Predictions Std 16.4425 +trainer/Q2 Predictions Max -3.42908 +trainer/Q2 Predictions Min -87.295 +trainer/Q Targets Mean -74.4425 +trainer/Q Targets Std 16.2604 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6434 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0241738 +trainer/policy/mean Std 0.724372 +trainer/policy/mean Max 0.99685 +trainer/policy/mean Min -0.999687 +trainer/policy/std Mean 0.427749 +trainer/policy/std Std 0.0207105 +trainer/policy/std Max 0.449256 +trainer/policy/std Min 0.39088 +trainer/Advantage Weights Mean 7.35118 +trainer/Advantage Weights Std 21.3704 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.35515e-10 +trainer/Advantage Score Mean -0.187262 +trainer/Advantage Score Std 0.41982 +trainer/Advantage Score Max 1.35887 +trainer/Advantage Score Min -2.11766 +trainer/V1 Predictions Mean -74.1684 +trainer/V1 Predictions Std 16.3049 +trainer/V1 Predictions Max -3.68606 +trainer/V1 Predictions Min -87.5154 +trainer/VF Loss 0.0486818 +expl/num steps total 309000 +expl/num paths total 344 +expl/path length Mean 500 +expl/path length Std 485 +expl/path length Max 985 +expl/path length Min 15 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0243356 +expl/Actions Std 0.831637 +expl/Actions Max 2.35522 +expl/Actions Min -2.14706 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 302997 +eval/num paths total 309 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.243537 +eval/Actions Std 0.703386 +eval/Actions Max 0.999816 +eval/Actions Min -0.998872 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.79024e-06 +time/evaluation sampling (s) 2.36326 +time/exploration sampling (s) 2.59194 +time/logging (s) 0.00743986 +time/saving (s) 0.0113877 +time/training (s) 12.8238 +time/epoch (s) 17.7978 +time/total (s) 6132.82 +Epoch -692 +------------------------------ ---------------- +2022-05-15 19:44:56.602972 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -691 finished +------------------------------ ---------------- +epoch -691 +replay_buffer/size 999047 +trainer/num train calls 310000 +trainer/QF1 Loss 0.460454 +trainer/QF2 Loss 0.455838 +trainer/Policy Loss 15.6895 +trainer/Q1 Predictions Mean -73.2874 +trainer/Q1 Predictions Std 18.1094 +trainer/Q1 Predictions Max -0.273741 +trainer/Q1 Predictions Min -87.1139 +trainer/Q2 Predictions Mean -73.3742 +trainer/Q2 Predictions Std 18.0538 +trainer/Q2 Predictions Max -0.266689 +trainer/Q2 Predictions Min -86.9292 +trainer/Q Targets Mean -73.4442 +trainer/Q Targets Std 17.8689 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2042 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0418616 +trainer/policy/mean Std 0.717478 +trainer/policy/mean Max 0.999055 +trainer/policy/mean Min -0.999873 +trainer/policy/std Mean 0.428947 +trainer/policy/std Std 0.0211101 +trainer/policy/std Max 0.449516 +trainer/policy/std Min 0.39257 +trainer/Advantage Weights Mean 3.94429 +trainer/Advantage Weights Std 14.7585 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.57539e-19 +trainer/Advantage Score Mean -0.365023 +trainer/Advantage Score Std 0.627061 +trainer/Advantage Score Max 1.33206 +trainer/Advantage Score Min -4.28031 +trainer/V1 Predictions Mean -73.1366 +trainer/V1 Predictions Std 18.1696 +trainer/V1 Predictions Max -0.191839 +trainer/V1 Predictions Min -87.0816 +trainer/VF Loss 0.0681182 +expl/num steps total 310000 +expl/num paths total 346 +expl/path length Mean 500 +expl/path length Std 103 +expl/path length Max 603 +expl/path length Min 397 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0169547 +expl/Actions Std 0.819678 +expl/Actions Max 2.40507 +expl/Actions Min -2.2742 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 303997 +eval/num paths total 310 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.187482 +eval/Actions Std 0.829925 +eval/Actions Max 0.999329 +eval/Actions Min -0.999217 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.97278e-06 +time/evaluation sampling (s) 2.29973 +time/exploration sampling (s) 2.57423 +time/logging (s) 0.0067312 +time/saving (s) 0.00933917 +time/training (s) 12.4945 +time/epoch (s) 17.3846 +time/total (s) 6150.21 +Epoch -691 +------------------------------ ---------------- +2022-05-15 19:45:14.044687 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -690 finished +------------------------------ ---------------- +epoch -690 +replay_buffer/size 999047 +trainer/num train calls 311000 +trainer/QF1 Loss 0.786144 +trainer/QF2 Loss 0.839546 +trainer/Policy Loss 7.24335 +trainer/Q1 Predictions Mean -70.9157 +trainer/Q1 Predictions Std 20.4478 +trainer/Q1 Predictions Max -0.616834 +trainer/Q1 Predictions Min -86.8787 +trainer/Q2 Predictions Mean -71.0489 +trainer/Q2 Predictions Std 20.3663 +trainer/Q2 Predictions Max -0.549853 +trainer/Q2 Predictions Min -86.8158 +trainer/Q Targets Mean -70.8147 +trainer/Q Targets Std 20.2944 +trainer/Q Targets Max -0.663066 +trainer/Q Targets Min -87.1096 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0372851 +trainer/policy/mean Std 0.711345 +trainer/policy/mean Max 0.996534 +trainer/policy/mean Min -0.999545 +trainer/policy/std Mean 0.42983 +trainer/policy/std Std 0.020809 +trainer/policy/std Max 0.454202 +trainer/policy/std Min 0.395226 +trainer/Advantage Weights Mean 2.18106 +trainer/Advantage Weights Std 11.9238 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.77528e-18 +trainer/Advantage Score Mean -0.470714 +trainer/Advantage Score Std 0.669125 +trainer/Advantage Score Max 1.92737 +trainer/Advantage Score Min -3.98831 +trainer/V1 Predictions Mean -70.4287 +trainer/V1 Predictions Std 20.5905 +trainer/V1 Predictions Max 1.57244 +trainer/V1 Predictions Min -87.0738 +trainer/VF Loss 0.0846456 +expl/num steps total 311000 +expl/num paths total 348 +expl/path length Mean 500 +expl/path length Std 463 +expl/path length Max 963 +expl/path length Min 37 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0342673 +expl/Actions Std 0.828277 +expl/Actions Max 2.26141 +expl/Actions Min -2.48127 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 304997 +eval/num paths total 311 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0224404 +eval/Actions Std 0.722113 +eval/Actions Max 0.999593 +eval/Actions Min -0.999509 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.75206e-06 +time/evaluation sampling (s) 2.29405 +time/exploration sampling (s) 2.61381 +time/logging (s) 0.00916257 +time/saving (s) 0.0132287 +time/training (s) 12.5071 +time/epoch (s) 17.4374 +time/total (s) 6167.65 +Epoch -690 +------------------------------ ---------------- +2022-05-15 19:45:31.229043 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -689 finished +------------------------------ ---------------- +epoch -689 +replay_buffer/size 999047 +trainer/num train calls 312000 +trainer/QF1 Loss 0.755954 +trainer/QF2 Loss 0.713498 +trainer/Policy Loss 11.7443 +trainer/Q1 Predictions Mean -75.9918 +trainer/Q1 Predictions Std 15.0911 +trainer/Q1 Predictions Max -0.630498 +trainer/Q1 Predictions Min -86.7047 +trainer/Q2 Predictions Mean -75.8803 +trainer/Q2 Predictions Std 15.0998 +trainer/Q2 Predictions Max 0.0119382 +trainer/Q2 Predictions Min -86.5523 +trainer/Q Targets Mean -75.5967 +trainer/Q Targets Std 14.9888 +trainer/Q Targets Max -0.226846 +trainer/Q Targets Min -86.9057 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00239712 +trainer/policy/mean Std 0.712231 +trainer/policy/mean Max 0.997877 +trainer/policy/mean Min -0.998332 +trainer/policy/std Mean 0.427731 +trainer/policy/std Std 0.0212309 +trainer/policy/std Max 0.449967 +trainer/policy/std Min 0.392855 +trainer/Advantage Weights Mean 4.26599 +trainer/Advantage Weights Std 19.3527 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.15127e-19 +trainer/Advantage Score Mean -0.390185 +trainer/Advantage Score Std 0.476307 +trainer/Advantage Score Max 1.30607 +trainer/Advantage Score Min -4.26013 +trainer/V1 Predictions Mean -75.3537 +trainer/V1 Predictions Std 15.1965 +trainer/V1 Predictions Max 0.606048 +trainer/V1 Predictions Min -86.7981 +trainer/VF Loss 0.0575797 +expl/num steps total 312000 +expl/num paths total 350 +expl/path length Mean 500 +expl/path length Std 72 +expl/path length Max 572 +expl/path length Min 428 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0429897 +expl/Actions Std 0.820657 +expl/Actions Max 2.46283 +expl/Actions Min -2.27004 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 305997 +eval/num paths total 312 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0497602 +eval/Actions Std 0.661093 +eval/Actions Max 0.999011 +eval/Actions Min -0.998421 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83308e-06 +time/evaluation sampling (s) 2.33295 +time/exploration sampling (s) 2.55437 +time/logging (s) 0.00736295 +time/saving (s) 0.0103237 +time/training (s) 12.2706 +time/epoch (s) 17.1757 +time/total (s) 6184.83 +Epoch -689 +------------------------------ ---------------- +2022-05-15 19:45:48.935464 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -688 finished +------------------------------ ---------------- +epoch -688 +replay_buffer/size 999047 +trainer/num train calls 313000 +trainer/QF1 Loss 0.588897 +trainer/QF2 Loss 0.643458 +trainer/Policy Loss 20.069 +trainer/Q1 Predictions Mean -73.5344 +trainer/Q1 Predictions Std 17.2165 +trainer/Q1 Predictions Max -0.2929 +trainer/Q1 Predictions Min -86.6163 +trainer/Q2 Predictions Mean -73.5647 +trainer/Q2 Predictions Std 17.1808 +trainer/Q2 Predictions Max -0.276792 +trainer/Q2 Predictions Min -86.8001 +trainer/Q Targets Mean -73.7662 +trainer/Q Targets Std 17.1394 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2875 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0134409 +trainer/policy/mean Std 0.713386 +trainer/policy/mean Max 0.998782 +trainer/policy/mean Min -0.998443 +trainer/policy/std Mean 0.426436 +trainer/policy/std Std 0.0212518 +trainer/policy/std Max 0.44928 +trainer/policy/std Min 0.389516 +trainer/Advantage Weights Mean 4.2946 +trainer/Advantage Weights Std 17.6095 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.15044e-08 +trainer/Advantage Score Mean -0.325409 +trainer/Advantage Score Std 0.397001 +trainer/Advantage Score Max 1.25321 +trainer/Advantage Score Min -1.66042 +trainer/V1 Predictions Mean -73.6052 +trainer/V1 Predictions Std 17.0466 +trainer/V1 Predictions Max 0.528177 +trainer/V1 Predictions Min -86.4147 +trainer/VF Loss 0.045464 +expl/num steps total 313000 +expl/num paths total 352 +expl/path length Mean 500 +expl/path length Std 368 +expl/path length Max 868 +expl/path length Min 132 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0585514 +expl/Actions Std 0.830651 +expl/Actions Max 2.49606 +expl/Actions Min -2.39886 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 306702 +eval/num paths total 313 +eval/path length Mean 705 +eval/path length Std 0 +eval/path length Max 705 +eval/path length Min 705 +eval/Rewards Mean 0.00141844 +eval/Rewards Std 0.0376355 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0127786 +eval/Actions Std 0.74469 +eval/Actions Max 0.999263 +eval/Actions Min -0.999267 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.99979e-06 +time/evaluation sampling (s) 2.31267 +time/exploration sampling (s) 2.63147 +time/logging (s) 0.00658874 +time/saving (s) 0.0117442 +time/training (s) 12.7362 +time/epoch (s) 17.6987 +time/total (s) 6202.53 +Epoch -688 +------------------------------ ---------------- +2022-05-15 19:46:06.618778 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -687 finished +------------------------------ ---------------- +epoch -687 +replay_buffer/size 999047 +trainer/num train calls 314000 +trainer/QF1 Loss 0.923464 +trainer/QF2 Loss 0.924323 +trainer/Policy Loss 11.2127 +trainer/Q1 Predictions Mean -75.2099 +trainer/Q1 Predictions Std 15.5157 +trainer/Q1 Predictions Max -0.621637 +trainer/Q1 Predictions Min -88.1702 +trainer/Q2 Predictions Mean -75.2022 +trainer/Q2 Predictions Std 15.5394 +trainer/Q2 Predictions Max -0.349377 +trainer/Q2 Predictions Min -88.1718 +trainer/Q Targets Mean -75.0828 +trainer/Q Targets Std 15.1958 +trainer/Q Targets Max -2.37753 +trainer/Q Targets Min -87.6417 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0146832 +trainer/policy/mean Std 0.708407 +trainer/policy/mean Max 0.999101 +trainer/policy/mean Min -0.999482 +trainer/policy/std Mean 0.427711 +trainer/policy/std Std 0.0216249 +trainer/policy/std Max 0.452176 +trainer/policy/std Min 0.388991 +trainer/Advantage Weights Mean 2.26887 +trainer/Advantage Weights Std 12.6668 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.40004e-12 +trainer/Advantage Score Mean -0.366655 +trainer/Advantage Score Std 0.418351 +trainer/Advantage Score Max 0.679764 +trainer/Advantage Score Min -2.61494 +trainer/V1 Predictions Mean -74.8522 +trainer/V1 Predictions Std 15.3226 +trainer/V1 Predictions Max -0.707521 +trainer/V1 Predictions Min -87.5483 +trainer/VF Loss 0.0367815 +expl/num steps total 314000 +expl/num paths total 353 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.121932 +expl/Actions Std 0.818066 +expl/Actions Max 2.51838 +expl/Actions Min -2.33761 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 307702 +eval/num paths total 314 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0357778 +eval/Actions Std 0.723307 +eval/Actions Max 0.999036 +eval/Actions Min -0.999762 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.55601e-06 +time/evaluation sampling (s) 2.30962 +time/exploration sampling (s) 2.67286 +time/logging (s) 0.00664725 +time/saving (s) 0.00956167 +time/training (s) 12.6764 +time/epoch (s) 17.6751 +time/total (s) 6220.21 +Epoch -687 +------------------------------ ---------------- +2022-05-15 19:46:24.569153 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -686 finished +------------------------------ ---------------- +epoch -686 +replay_buffer/size 999047 +trainer/num train calls 315000 +trainer/QF1 Loss 1.16833 +trainer/QF2 Loss 1.32752 +trainer/Policy Loss 33.0797 +trainer/Q1 Predictions Mean -72.6112 +trainer/Q1 Predictions Std 18.7557 +trainer/Q1 Predictions Max -0.87822 +trainer/Q1 Predictions Min -86.8954 +trainer/Q2 Predictions Mean -72.5892 +trainer/Q2 Predictions Std 18.6712 +trainer/Q2 Predictions Max -0.332427 +trainer/Q2 Predictions Min -86.4413 +trainer/Q Targets Mean -73.2488 +trainer/Q Targets Std 19.0004 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2174 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00320912 +trainer/policy/mean Std 0.713334 +trainer/policy/mean Max 0.998316 +trainer/policy/mean Min -0.997782 +trainer/policy/std Mean 0.428355 +trainer/policy/std Std 0.0210816 +trainer/policy/std Max 0.452812 +trainer/policy/std Min 0.392498 +trainer/Advantage Weights Mean 8.14227 +trainer/Advantage Weights Std 21.9131 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19813e-18 +trainer/Advantage Score Mean -0.179968 +trainer/Advantage Score Std 0.604648 +trainer/Advantage Score Max 1.76967 +trainer/Advantage Score Min -4.12658 +trainer/V1 Predictions Mean -73.0135 +trainer/V1 Predictions Std 19.1626 +trainer/V1 Predictions Max -0.141188 +trainer/V1 Predictions Min -87.1701 +trainer/VF Loss 0.0777048 +expl/num steps total 315000 +expl/num paths total 355 +expl/path length Mean 500 +expl/path length Std 172 +expl/path length Max 672 +expl/path length Min 328 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0301192 +expl/Actions Std 0.816602 +expl/Actions Max 2.42149 +expl/Actions Min -2.43202 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 308702 +eval/num paths total 315 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.228369 +eval/Actions Std 0.701432 +eval/Actions Max 0.99822 +eval/Actions Min -0.998133 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.09106e-06 +time/evaluation sampling (s) 2.52949 +time/exploration sampling (s) 2.62607 +time/logging (s) 0.00673869 +time/saving (s) 0.00961462 +time/training (s) 12.7719 +time/epoch (s) 17.9438 +time/total (s) 6238.16 +Epoch -686 +------------------------------ ---------------- +2022-05-15 19:46:41.688940 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -685 finished +------------------------------ ---------------- +epoch -685 +replay_buffer/size 999047 +trainer/num train calls 316000 +trainer/QF1 Loss 0.829873 +trainer/QF2 Loss 0.886685 +trainer/Policy Loss 20.7368 +trainer/Q1 Predictions Mean -73.5061 +trainer/Q1 Predictions Std 18.177 +trainer/Q1 Predictions Max -0.826858 +trainer/Q1 Predictions Min -86.7009 +trainer/Q2 Predictions Mean -73.4891 +trainer/Q2 Predictions Std 18.1285 +trainer/Q2 Predictions Max -0.786305 +trainer/Q2 Predictions Min -86.5899 +trainer/Q Targets Mean -73.7423 +trainer/Q Targets Std 17.8873 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.845 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.020985 +trainer/policy/mean Std 0.70597 +trainer/policy/mean Max 0.999606 +trainer/policy/mean Min -0.996478 +trainer/policy/std Mean 0.428824 +trainer/policy/std Std 0.0200553 +trainer/policy/std Max 0.449959 +trainer/policy/std Min 0.3927 +trainer/Advantage Weights Mean 2.64702 +trainer/Advantage Weights Std 13.0736 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.32153e-16 +trainer/Advantage Score Mean -0.329933 +trainer/Advantage Score Std 0.515706 +trainer/Advantage Score Max 1.11466 +trainer/Advantage Score Min -3.51696 +trainer/V1 Predictions Mean -73.464 +trainer/V1 Predictions Std 18.1317 +trainer/V1 Predictions Max -0.745443 +trainer/V1 Predictions Min -86.6875 +trainer/VF Loss 0.0471215 +expl/num steps total 316000 +expl/num paths total 357 +expl/path length Mean 500 +expl/path length Std 305 +expl/path length Max 805 +expl/path length Min 195 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0394065 +expl/Actions Std 0.807634 +expl/Actions Max 2.39227 +expl/Actions Min -2.54602 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 309483 +eval/num paths total 316 +eval/path length Mean 781 +eval/path length Std 0 +eval/path length Max 781 +eval/path length Min 781 +eval/Rewards Mean 0.00128041 +eval/Rewards Std 0.0357599 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0552534 +eval/Actions Std 0.717446 +eval/Actions Max 0.998528 +eval/Actions Min -0.999029 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.19122e-06 +time/evaluation sampling (s) 2.34341 +time/exploration sampling (s) 2.60191 +time/logging (s) 0.00738081 +time/saving (s) 0.0127243 +time/training (s) 12.1483 +time/epoch (s) 17.1137 +time/total (s) 6255.28 +Epoch -685 +------------------------------ ---------------- +2022-05-15 19:46:59.113521 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -684 finished +------------------------------ ---------------- +epoch -684 +replay_buffer/size 999047 +trainer/num train calls 317000 +trainer/QF1 Loss 0.433855 +trainer/QF2 Loss 0.429105 +trainer/Policy Loss 41.6401 +trainer/Q1 Predictions Mean -73.4258 +trainer/Q1 Predictions Std 18.3334 +trainer/Q1 Predictions Max -2.71167 +trainer/Q1 Predictions Min -86.9666 +trainer/Q2 Predictions Mean -73.4864 +trainer/Q2 Predictions Std 18.4033 +trainer/Q2 Predictions Max -2.31833 +trainer/Q2 Predictions Min -87.0116 +trainer/Q Targets Mean -73.543 +trainer/Q Targets Std 18.5111 +trainer/Q Targets Max -3.35907 +trainer/Q Targets Min -86.8954 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0141403 +trainer/policy/mean Std 0.710087 +trainer/policy/mean Max 0.998753 +trainer/policy/mean Min -0.997789 +trainer/policy/std Mean 0.42948 +trainer/policy/std Std 0.0208235 +trainer/policy/std Max 0.451425 +trainer/policy/std Min 0.392212 +trainer/Advantage Weights Mean 5.2364 +trainer/Advantage Weights Std 19.2276 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.64355e-12 +trainer/Advantage Score Mean -0.234023 +trainer/Advantage Score Std 0.494194 +trainer/Advantage Score Max 2.58981 +trainer/Advantage Score Min -2.60955 +trainer/V1 Predictions Mean -73.3585 +trainer/V1 Predictions Std 18.5003 +trainer/V1 Predictions Max -3.44782 +trainer/V1 Predictions Min -86.726 +trainer/VF Loss 0.0709844 +expl/num steps total 317000 +expl/num paths total 358 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0158936 +expl/Actions Std 0.839866 +expl/Actions Max 2.54215 +expl/Actions Min -2.31223 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 310483 +eval/num paths total 317 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.35394 +eval/Actions Std 0.685609 +eval/Actions Max 0.996704 +eval/Actions Min -0.999503 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.77208e-06 +time/evaluation sampling (s) 2.34872 +time/exploration sampling (s) 2.63172 +time/logging (s) 0.00719741 +time/saving (s) 0.0116643 +time/training (s) 12.4165 +time/epoch (s) 17.4158 +time/total (s) 6272.7 +Epoch -684 +------------------------------ ---------------- +2022-05-15 19:47:16.600100 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -683 finished +------------------------------ ---------------- +epoch -683 +replay_buffer/size 999047 +trainer/num train calls 318000 +trainer/QF1 Loss 0.623877 +trainer/QF2 Loss 0.616629 +trainer/Policy Loss 11.9865 +trainer/Q1 Predictions Mean -72.0836 +trainer/Q1 Predictions Std 19.7422 +trainer/Q1 Predictions Max -0.326942 +trainer/Q1 Predictions Min -86.2787 +trainer/Q2 Predictions Mean -72.1559 +trainer/Q2 Predictions Std 19.8123 +trainer/Q2 Predictions Max -0.268979 +trainer/Q2 Predictions Min -86.4346 +trainer/Q Targets Mean -72.2444 +trainer/Q Targets Std 19.631 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6282 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00606915 +trainer/policy/mean Std 0.713174 +trainer/policy/mean Max 0.997823 +trainer/policy/mean Min -0.998349 +trainer/policy/std Mean 0.429737 +trainer/policy/std Std 0.021851 +trainer/policy/std Max 0.455416 +trainer/policy/std Min 0.389238 +trainer/Advantage Weights Mean 2.59538 +trainer/Advantage Weights Std 13.7521 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.02463e-19 +trainer/Advantage Score Mean -0.607246 +trainer/Advantage Score Std 0.668422 +trainer/Advantage Score Max 1.52864 +trainer/Advantage Score Min -4.30437 +trainer/V1 Predictions Mean -71.8892 +trainer/V1 Predictions Std 19.9698 +trainer/V1 Predictions Max -1.01177 +trainer/V1 Predictions Min -86.5571 +trainer/VF Loss 0.0957737 +expl/num steps total 318000 +expl/num paths total 359 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0300221 +expl/Actions Std 0.815165 +expl/Actions Max 2.91474 +expl/Actions Min -2.27405 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 311483 +eval/num paths total 318 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0262616 +eval/Actions Std 0.709549 +eval/Actions Max 0.999279 +eval/Actions Min -0.999738 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.74181e-06 +time/evaluation sampling (s) 2.4405 +time/exploration sampling (s) 2.6854 +time/logging (s) 0.00695565 +time/saving (s) 0.00945617 +time/training (s) 12.3351 +time/epoch (s) 17.4774 +time/total (s) 6290.18 +Epoch -683 +------------------------------ ---------------- +2022-05-15 19:47:34.346680 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -682 finished +------------------------------ ---------------- +epoch -682 +replay_buffer/size 999047 +trainer/num train calls 319000 +trainer/QF1 Loss 0.525091 +trainer/QF2 Loss 0.616145 +trainer/Policy Loss 18.5487 +trainer/Q1 Predictions Mean -72.3266 +trainer/Q1 Predictions Std 21.1457 +trainer/Q1 Predictions Max -0.358184 +trainer/Q1 Predictions Min -88.601 +trainer/Q2 Predictions Mean -72.3578 +trainer/Q2 Predictions Std 21.1224 +trainer/Q2 Predictions Max -0.11043 +trainer/Q2 Predictions Min -88.6725 +trainer/Q Targets Mean -72.1624 +trainer/Q Targets Std 21.1534 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6187 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0144203 +trainer/policy/mean Std 0.720521 +trainer/policy/mean Max 0.999372 +trainer/policy/mean Min -0.999022 +trainer/policy/std Mean 0.42875 +trainer/policy/std Std 0.0216958 +trainer/policy/std Max 0.456417 +trainer/policy/std Min 0.389889 +trainer/Advantage Weights Mean 3.22233 +trainer/Advantage Weights Std 15.5157 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.54526e-10 +trainer/Advantage Score Mean -0.402705 +trainer/Advantage Score Std 0.477832 +trainer/Advantage Score Max 1.13453 +trainer/Advantage Score Min -2.25907 +trainer/V1 Predictions Mean -71.8755 +trainer/V1 Predictions Std 21.2758 +trainer/V1 Predictions Max -0.0398835 +trainer/V1 Predictions Min -87.6515 +trainer/VF Loss 0.0516252 +expl/num steps total 319000 +expl/num paths total 360 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0418508 +expl/Actions Std 0.818471 +expl/Actions Max 2.61402 +expl/Actions Min -2.16999 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 312246 +eval/num paths total 319 +eval/path length Mean 763 +eval/path length Std 0 +eval/path length Max 763 +eval/path length Min 763 +eval/Rewards Mean 0.00131062 +eval/Rewards Std 0.0361787 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.075631 +eval/Actions Std 0.707189 +eval/Actions Max 0.998273 +eval/Actions Min -0.998506 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.47126e-06 +time/evaluation sampling (s) 2.32441 +time/exploration sampling (s) 2.65251 +time/logging (s) 0.00606845 +time/saving (s) 0.00971456 +time/training (s) 12.7458 +time/epoch (s) 17.7385 +time/total (s) 6307.92 +Epoch -682 +------------------------------ ---------------- +2022-05-15 19:47:51.957245 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -681 finished +------------------------------ ---------------- +epoch -681 +replay_buffer/size 999047 +trainer/num train calls 320000 +trainer/QF1 Loss 0.48354 +trainer/QF2 Loss 0.543589 +trainer/Policy Loss 14.8821 +trainer/Q1 Predictions Mean -73.5307 +trainer/Q1 Predictions Std 17.7025 +trainer/Q1 Predictions Max -0.959762 +trainer/Q1 Predictions Min -86.1812 +trainer/Q2 Predictions Mean -73.4445 +trainer/Q2 Predictions Std 17.7024 +trainer/Q2 Predictions Max -0.791974 +trainer/Q2 Predictions Min -85.9437 +trainer/Q Targets Mean -73.7087 +trainer/Q Targets Std 17.8704 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.775 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0119995 +trainer/policy/mean Std 0.719194 +trainer/policy/mean Max 0.99923 +trainer/policy/mean Min -0.994565 +trainer/policy/std Mean 0.426472 +trainer/policy/std Std 0.0210493 +trainer/policy/std Max 0.449799 +trainer/policy/std Min 0.388863 +trainer/Advantage Weights Mean 3.22665 +trainer/Advantage Weights Std 15.3283 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.85346e-16 +trainer/Advantage Score Mean -0.353679 +trainer/Advantage Score Std 0.496672 +trainer/Advantage Score Max 0.83252 +trainer/Advantage Score Min -3.57928 +trainer/V1 Predictions Mean -73.476 +trainer/V1 Predictions Std 17.9197 +trainer/V1 Predictions Max -0.548791 +trainer/V1 Predictions Min -86.9265 +trainer/VF Loss 0.0473035 +expl/num steps total 320000 +expl/num paths total 362 +expl/path length Mean 500 +expl/path length Std 94 +expl/path length Max 594 +expl/path length Min 406 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0503872 +expl/Actions Std 0.826326 +expl/Actions Max 2.57356 +expl/Actions Min -2.35773 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 313246 +eval/num paths total 320 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0331625 +eval/Actions Std 0.730904 +eval/Actions Max 0.999652 +eval/Actions Min -0.999456 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.60426e-06 +time/evaluation sampling (s) 2.27887 +time/exploration sampling (s) 2.57697 +time/logging (s) 0.00697443 +time/saving (s) 0.0104023 +time/training (s) 12.7314 +time/epoch (s) 17.6046 +time/total (s) 6325.53 +Epoch -681 +------------------------------ ---------------- +2022-05-15 19:48:08.827666 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -680 finished +------------------------------ ---------------- +epoch -680 +replay_buffer/size 999047 +trainer/num train calls 321000 +trainer/QF1 Loss 1.07654 +trainer/QF2 Loss 0.905976 +trainer/Policy Loss 16.1392 +trainer/Q1 Predictions Mean -74.7396 +trainer/Q1 Predictions Std 17.9903 +trainer/Q1 Predictions Max -0.853938 +trainer/Q1 Predictions Min -87.4986 +trainer/Q2 Predictions Mean -74.7401 +trainer/Q2 Predictions Std 18.0491 +trainer/Q2 Predictions Max -0.130623 +trainer/Q2 Predictions Min -87.4792 +trainer/Q Targets Mean -74.3306 +trainer/Q Targets Std 18.2797 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0206 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0231817 +trainer/policy/mean Std 0.709451 +trainer/policy/mean Max 0.999094 +trainer/policy/mean Min -0.997286 +trainer/policy/std Mean 0.426715 +trainer/policy/std Std 0.0225526 +trainer/policy/std Max 0.451349 +trainer/policy/std Min 0.384901 +trainer/Advantage Weights Mean 2.30694 +trainer/Advantage Weights Std 12.7908 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.73626e-15 +trainer/Advantage Score Mean -0.392091 +trainer/Advantage Score Std 0.462058 +trainer/Advantage Score Max 1.13443 +trainer/Advantage Score Min -3.2792 +trainer/V1 Predictions Mean -74.1826 +trainer/V1 Predictions Std 18.2016 +trainer/V1 Predictions Max -0.0648489 +trainer/V1 Predictions Min -86.9074 +trainer/VF Loss 0.0464529 +expl/num steps total 321000 +expl/num paths total 363 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0277698 +expl/Actions Std 0.783089 +expl/Actions Max 2.65886 +expl/Actions Min -2.61767 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 314095 +eval/num paths total 321 +eval/path length Mean 849 +eval/path length Std 0 +eval/path length Max 849 +eval/path length Min 849 +eval/Rewards Mean 0.00117786 +eval/Rewards Std 0.0342997 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0254998 +eval/Actions Std 0.70616 +eval/Actions Max 0.999507 +eval/Actions Min -0.999803 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.66591e-06 +time/evaluation sampling (s) 2.23441 +time/exploration sampling (s) 2.62075 +time/logging (s) 0.00795983 +time/saving (s) 0.0114562 +time/training (s) 11.99 +time/epoch (s) 16.8646 +time/total (s) 6342.4 +Epoch -680 +------------------------------ ---------------- +2022-05-15 19:48:26.204903 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -679 finished +------------------------------ ---------------- +epoch -679 +replay_buffer/size 999047 +trainer/num train calls 322000 +trainer/QF1 Loss 1.01301 +trainer/QF2 Loss 1.01925 +trainer/Policy Loss 29.4669 +trainer/Q1 Predictions Mean -73.7183 +trainer/Q1 Predictions Std 18.1992 +trainer/Q1 Predictions Max -0.567229 +trainer/Q1 Predictions Min -88.8593 +trainer/Q2 Predictions Mean -73.7336 +trainer/Q2 Predictions Std 18.2312 +trainer/Q2 Predictions Max -0.87888 +trainer/Q2 Predictions Min -88.7609 +trainer/Q Targets Mean -73.7533 +trainer/Q Targets Std 18.0753 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.668 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0383487 +trainer/policy/mean Std 0.719696 +trainer/policy/mean Max 0.99971 +trainer/policy/mean Min -0.998933 +trainer/policy/std Mean 0.426815 +trainer/policy/std Std 0.0214095 +trainer/policy/std Max 0.450263 +trainer/policy/std Min 0.386516 +trainer/Advantage Weights Mean 4.3623 +trainer/Advantage Weights Std 17.3354 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.87543e-26 +trainer/Advantage Score Mean -0.331702 +trainer/Advantage Score Std 0.582337 +trainer/Advantage Score Max 1.81065 +trainer/Advantage Score Min -5.8283 +trainer/V1 Predictions Mean -73.4578 +trainer/V1 Predictions Std 18.1007 +trainer/V1 Predictions Max -0.193556 +trainer/V1 Predictions Min -87.8066 +trainer/VF Loss 0.0748081 +expl/num steps total 322000 +expl/num paths total 364 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.09621 +expl/Actions Std 0.812987 +expl/Actions Max 2.39072 +expl/Actions Min -2.40172 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 315095 +eval/num paths total 322 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0381894 +eval/Actions Std 0.732121 +eval/Actions Max 0.999658 +eval/Actions Min -0.999742 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.6417e-06 +time/evaluation sampling (s) 2.31291 +time/exploration sampling (s) 2.56347 +time/logging (s) 0.00680466 +time/saving (s) 0.0117657 +time/training (s) 12.4732 +time/epoch (s) 17.3681 +time/total (s) 6359.77 +Epoch -679 +------------------------------ ---------------- +2022-05-15 19:48:43.866262 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -678 finished +------------------------------ ---------------- +epoch -678 +replay_buffer/size 999047 +trainer/num train calls 323000 +trainer/QF1 Loss 1.09295 +trainer/QF2 Loss 1.00569 +trainer/Policy Loss 20.2472 +trainer/Q1 Predictions Mean -73.1273 +trainer/Q1 Predictions Std 18.6236 +trainer/Q1 Predictions Max -0.604261 +trainer/Q1 Predictions Min -87.4778 +trainer/Q2 Predictions Mean -73.2163 +trainer/Q2 Predictions Std 18.6476 +trainer/Q2 Predictions Max -1.16617 +trainer/Q2 Predictions Min -87.3795 +trainer/Q Targets Mean -73.7177 +trainer/Q Targets Std 18.6153 +trainer/Q Targets Max -0.700896 +trainer/Q Targets Min -87.4221 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0514998 +trainer/policy/mean Std 0.711413 +trainer/policy/mean Max 0.999047 +trainer/policy/mean Min -0.999791 +trainer/policy/std Mean 0.425698 +trainer/policy/std Std 0.0213589 +trainer/policy/std Max 0.448571 +trainer/policy/std Min 0.386371 +trainer/Advantage Weights Mean 6.39071 +trainer/Advantage Weights Std 19.7968 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.34515e-10 +trainer/Advantage Score Mean -0.14494 +trainer/Advantage Score Std 0.497938 +trainer/Advantage Score Max 4.49064 +trainer/Advantage Score Min -2.21735 +trainer/V1 Predictions Mean -73.3861 +trainer/V1 Predictions Std 18.7634 +trainer/V1 Predictions Max -0.208588 +trainer/V1 Predictions Min -87.1916 +trainer/VF Loss 0.109509 +expl/num steps total 323000 +expl/num paths total 365 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0252345 +expl/Actions Std 0.822323 +expl/Actions Max 2.38576 +expl/Actions Min -2.46017 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 316095 +eval/num paths total 323 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0163337 +eval/Actions Std 0.730565 +eval/Actions Max 0.999724 +eval/Actions Min -0.999527 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.45195e-06 +time/evaluation sampling (s) 2.35253 +time/exploration sampling (s) 2.56523 +time/logging (s) 0.00744156 +time/saving (s) 0.0120553 +time/training (s) 12.7161 +time/epoch (s) 17.6533 +time/total (s) 6377.43 +Epoch -678 +------------------------------ ---------------- +2022-05-15 19:49:00.985628 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -677 finished +------------------------------ ---------------- +epoch -677 +replay_buffer/size 999047 +trainer/num train calls 324000 +trainer/QF1 Loss 0.620687 +trainer/QF2 Loss 0.576331 +trainer/Policy Loss 13.7466 +trainer/Q1 Predictions Mean -73.4716 +trainer/Q1 Predictions Std 18.6005 +trainer/Q1 Predictions Max -1.30978 +trainer/Q1 Predictions Min -87.3025 +trainer/Q2 Predictions Mean -73.4703 +trainer/Q2 Predictions Std 18.5743 +trainer/Q2 Predictions Max -1.27333 +trainer/Q2 Predictions Min -87.2207 +trainer/Q Targets Mean -73.5156 +trainer/Q Targets Std 18.6774 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4793 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00423094 +trainer/policy/mean Std 0.71309 +trainer/policy/mean Max 0.998874 +trainer/policy/mean Min -0.997228 +trainer/policy/std Mean 0.426852 +trainer/policy/std Std 0.020791 +trainer/policy/std Max 0.447644 +trainer/policy/std Min 0.388828 +trainer/Advantage Weights Mean 3.03348 +trainer/Advantage Weights Std 12.7511 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.00147e-15 +trainer/Advantage Score Mean -0.351063 +trainer/Advantage Score Std 0.551107 +trainer/Advantage Score Max 0.650574 +trainer/Advantage Score Min -3.27468 +trainer/V1 Predictions Mean -73.1718 +trainer/V1 Predictions Std 18.9759 +trainer/V1 Predictions Max 0.331366 +trainer/V1 Predictions Min -87.2209 +trainer/VF Loss 0.0505543 +expl/num steps total 324000 +expl/num paths total 366 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0396251 +expl/Actions Std 0.838124 +expl/Actions Max 2.52045 +expl/Actions Min -2.13583 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 317095 +eval/num paths total 324 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.314572 +eval/Actions Std 0.65902 +eval/Actions Max 0.998728 +eval/Actions Min -0.999698 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.82614e-06 +time/evaluation sampling (s) 2.34164 +time/exploration sampling (s) 2.5782 +time/logging (s) 0.0102722 +time/saving (s) 0.0143661 +time/training (s) 12.1692 +time/epoch (s) 17.1137 +time/total (s) 6394.55 +Epoch -677 +------------------------------ ---------------- +2022-05-15 19:49:18.212416 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -676 finished +------------------------------ ---------------- +epoch -676 +replay_buffer/size 999047 +trainer/num train calls 325000 +trainer/QF1 Loss 1.30155 +trainer/QF2 Loss 1.12668 +trainer/Policy Loss 3.29368 +trainer/Q1 Predictions Mean -74.3625 +trainer/Q1 Predictions Std 17.9929 +trainer/Q1 Predictions Max -1.39278 +trainer/Q1 Predictions Min -89.3978 +trainer/Q2 Predictions Mean -74.2834 +trainer/Q2 Predictions Std 18.0627 +trainer/Q2 Predictions Max -1.22162 +trainer/Q2 Predictions Min -89.8072 +trainer/Q Targets Mean -74.1066 +trainer/Q Targets Std 18.5214 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.2065 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00833187 +trainer/policy/mean Std 0.709041 +trainer/policy/mean Max 0.99939 +trainer/policy/mean Min -0.999241 +trainer/policy/std Mean 0.427253 +trainer/policy/std Std 0.0205834 +trainer/policy/std Max 0.450473 +trainer/policy/std Min 0.391747 +trainer/Advantage Weights Mean 0.869657 +trainer/Advantage Weights Std 6.39991 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.51636e-12 +trainer/Advantage Score Mean -0.402173 +trainer/Advantage Score Std 0.426486 +trainer/Advantage Score Max 0.544788 +trainer/Advantage Score Min -2.61233 +trainer/V1 Predictions Mean -74.0035 +trainer/V1 Predictions Std 18.1775 +trainer/V1 Predictions Max -0.354896 +trainer/V1 Predictions Min -89.037 +trainer/VF Loss 0.0365448 +expl/num steps total 325000 +expl/num paths total 367 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0258362 +expl/Actions Std 0.833304 +expl/Actions Max 2.382 +expl/Actions Min -2.26734 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 317790 +eval/num paths total 325 +eval/path length Mean 695 +eval/path length Std 0 +eval/path length Max 695 +eval/path length Min 695 +eval/Rewards Mean 0.00143885 +eval/Rewards Std 0.0379049 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0182209 +eval/Actions Std 0.718411 +eval/Actions Max 0.999254 +eval/Actions Min -0.999078 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.00584e-06 +time/evaluation sampling (s) 2.34952 +time/exploration sampling (s) 2.64695 +time/logging (s) 0.00579831 +time/saving (s) 0.00983439 +time/training (s) 12.199 +time/epoch (s) 17.2111 +time/total (s) 6411.76 +Epoch -676 +------------------------------ ---------------- +2022-05-15 19:49:35.727915 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -675 finished +------------------------------ ---------------- +epoch -675 +replay_buffer/size 999047 +trainer/num train calls 326000 +trainer/QF1 Loss 0.743288 +trainer/QF2 Loss 0.598217 +trainer/Policy Loss 5.77011 +trainer/Q1 Predictions Mean -73.9984 +trainer/Q1 Predictions Std 18.385 +trainer/Q1 Predictions Max -2.70447 +trainer/Q1 Predictions Min -86.7958 +trainer/Q2 Predictions Mean -74.0705 +trainer/Q2 Predictions Std 18.3165 +trainer/Q2 Predictions Max -1.76172 +trainer/Q2 Predictions Min -86.8113 +trainer/Q Targets Mean -74.1073 +trainer/Q Targets Std 18.1771 +trainer/Q Targets Max -1.96781 +trainer/Q Targets Min -86.8903 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00235331 +trainer/policy/mean Std 0.710997 +trainer/policy/mean Max 0.997327 +trainer/policy/mean Min -0.999158 +trainer/policy/std Mean 0.425064 +trainer/policy/std Std 0.0213929 +trainer/policy/std Max 0.448731 +trainer/policy/std Min 0.38708 +trainer/Advantage Weights Mean 1.37101 +trainer/Advantage Weights Std 7.44408 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.77714e-14 +trainer/Advantage Score Mean -0.441241 +trainer/Advantage Score Std 0.563083 +trainer/Advantage Score Max 0.463558 +trainer/Advantage Score Min -3.12148 +trainer/V1 Predictions Mean -73.7769 +trainer/V1 Predictions Std 18.6065 +trainer/V1 Predictions Max -1.83163 +trainer/V1 Predictions Min -86.5087 +trainer/VF Loss 0.0547437 +expl/num steps total 326000 +expl/num paths total 368 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0318548 +expl/Actions Std 0.820536 +expl/Actions Max 2.43339 +expl/Actions Min -2.51785 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 318790 +eval/num paths total 326 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0439756 +eval/Actions Std 0.730496 +eval/Actions Max 0.999009 +eval/Actions Min -0.999613 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.9048e-06 +time/evaluation sampling (s) 2.49421 +time/exploration sampling (s) 2.55599 +time/logging (s) 0.00748315 +time/saving (s) 0.0139901 +time/training (s) 12.4391 +time/epoch (s) 17.5107 +time/total (s) 6429.28 +Epoch -675 +------------------------------ ---------------- +2022-05-15 19:49:53.284423 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -674 finished +------------------------------ ---------------- +epoch -674 +replay_buffer/size 999047 +trainer/num train calls 327000 +trainer/QF1 Loss 1.48795 +trainer/QF2 Loss 1.38979 +trainer/Policy Loss 26.5403 +trainer/Q1 Predictions Mean -73.0499 +trainer/Q1 Predictions Std 18.5644 +trainer/Q1 Predictions Max -1.35351 +trainer/Q1 Predictions Min -86.2805 +trainer/Q2 Predictions Mean -73.0348 +trainer/Q2 Predictions Std 18.5901 +trainer/Q2 Predictions Max -0.873873 +trainer/Q2 Predictions Min -86.2169 +trainer/Q Targets Mean -73.1229 +trainer/Q Targets Std 18.3352 +trainer/Q Targets Max -1.03648 +trainer/Q Targets Min -87.7763 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0139663 +trainer/policy/mean Std 0.710906 +trainer/policy/mean Max 0.997607 +trainer/policy/mean Min -0.99788 +trainer/policy/std Mean 0.425818 +trainer/policy/std Std 0.0207518 +trainer/policy/std Max 0.448594 +trainer/policy/std Min 0.390263 +trainer/Advantage Weights Mean 5.9829 +trainer/Advantage Weights Std 19.4345 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.5728e-10 +trainer/Advantage Score Mean -0.223839 +trainer/Advantage Score Std 0.480974 +trainer/Advantage Score Max 1.37214 +trainer/Advantage Score Min -2.2573 +trainer/V1 Predictions Mean -72.9089 +trainer/V1 Predictions Std 18.3411 +trainer/V1 Predictions Max -0.0796342 +trainer/V1 Predictions Min -86.1437 +trainer/VF Loss 0.052569 +expl/num steps total 327000 +expl/num paths total 369 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0401964 +expl/Actions Std 0.786941 +expl/Actions Max 2.31168 +expl/Actions Min -2.2782 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 319363 +eval/num paths total 327 +eval/path length Mean 573 +eval/path length Std 0 +eval/path length Max 573 +eval/path length Min 573 +eval/Rewards Mean 0.0017452 +eval/Rewards Std 0.0417391 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0459937 +eval/Actions Std 0.73157 +eval/Actions Max 0.999078 +eval/Actions Min -0.999373 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.59234e-06 +time/evaluation sampling (s) 2.45141 +time/exploration sampling (s) 2.53062 +time/logging (s) 0.00608881 +time/saving (s) 0.0118895 +time/training (s) 12.546 +time/epoch (s) 17.546 +time/total (s) 6446.83 +Epoch -674 +------------------------------ ---------------- +2022-05-15 19:50:10.700792 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -673 finished +------------------------------ ---------------- +epoch -673 +replay_buffer/size 999047 +trainer/num train calls 328000 +trainer/QF1 Loss 0.975245 +trainer/QF2 Loss 1.0104 +trainer/Policy Loss 4.93358 +trainer/Q1 Predictions Mean -75.5398 +trainer/Q1 Predictions Std 14.5251 +trainer/Q1 Predictions Max -6.93799 +trainer/Q1 Predictions Min -86.3516 +trainer/Q2 Predictions Mean -75.5045 +trainer/Q2 Predictions Std 14.5839 +trainer/Q2 Predictions Max -6.57334 +trainer/Q2 Predictions Min -86.1551 +trainer/Q Targets Mean -75.5028 +trainer/Q Targets Std 14.4457 +trainer/Q Targets Max -6.47416 +trainer/Q Targets Min -85.9435 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00695067 +trainer/policy/mean Std 0.727087 +trainer/policy/mean Max 0.99988 +trainer/policy/mean Min -0.998704 +trainer/policy/std Mean 0.426773 +trainer/policy/std Std 0.0217288 +trainer/policy/std Max 0.452107 +trainer/policy/std Min 0.389921 +trainer/Advantage Weights Mean 1.01494 +trainer/Advantage Weights Std 7.45998 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.28789e-14 +trainer/Advantage Score Mean -0.526848 +trainer/Advantage Score Std 0.51539 +trainer/Advantage Score Max 1.37028 +trainer/Advantage Score Min -3.19832 +trainer/V1 Predictions Mean -75.255 +trainer/V1 Predictions Std 14.746 +trainer/V1 Predictions Max -7.01619 +trainer/V1 Predictions Min -85.7888 +trainer/VF Loss 0.0618442 +expl/num steps total 328000 +expl/num paths total 370 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0655563 +expl/Actions Std 0.843988 +expl/Actions Max 2.45351 +expl/Actions Min -2.26163 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 320363 +eval/num paths total 328 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.158025 +eval/Actions Std 0.680756 +eval/Actions Max 0.999607 +eval/Actions Min -0.99879 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.86712e-06 +time/evaluation sampling (s) 2.54833 +time/exploration sampling (s) 2.56302 +time/logging (s) 0.00924923 +time/saving (s) 0.0102464 +time/training (s) 12.2802 +time/epoch (s) 17.411 +time/total (s) 6464.24 +Epoch -673 +------------------------------ ---------------- +2022-05-15 19:50:27.780765 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -672 finished +------------------------------ ---------------- +epoch -672 +replay_buffer/size 999047 +trainer/num train calls 329000 +trainer/QF1 Loss 0.859515 +trainer/QF2 Loss 0.873958 +trainer/Policy Loss 23.7263 +trainer/Q1 Predictions Mean -75.0686 +trainer/Q1 Predictions Std 14.7298 +trainer/Q1 Predictions Max -0.505216 +trainer/Q1 Predictions Min -87.2228 +trainer/Q2 Predictions Mean -75.0306 +trainer/Q2 Predictions Std 14.7969 +trainer/Q2 Predictions Max -0.679709 +trainer/Q2 Predictions Min -87.253 +trainer/Q Targets Mean -75.126 +trainer/Q Targets Std 15.0478 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1659 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00275226 +trainer/policy/mean Std 0.7145 +trainer/policy/mean Max 0.998452 +trainer/policy/mean Min -0.998968 +trainer/policy/std Mean 0.427793 +trainer/policy/std Std 0.0215466 +trainer/policy/std Max 0.45416 +trainer/policy/std Min 0.392662 +trainer/Advantage Weights Mean 6.83052 +trainer/Advantage Weights Std 18.9884 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.83808e-27 +trainer/Advantage Score Mean -0.259357 +trainer/Advantage Score Std 0.776945 +trainer/Advantage Score Max 1.12132 +trainer/Advantage Score Min -5.98835 +trainer/V1 Predictions Mean -74.7957 +trainer/V1 Predictions Std 15.2417 +trainer/V1 Predictions Max 1.72193 +trainer/V1 Predictions Min -87.0294 +trainer/VF Loss 0.0912715 +expl/num steps total 329000 +expl/num paths total 372 +expl/path length Mean 500 +expl/path length Std 359 +expl/path length Max 859 +expl/path length Min 141 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0441006 +expl/Actions Std 0.84138 +expl/Actions Max 2.18311 +expl/Actions Min -2.37342 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 321363 +eval/num paths total 329 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0495697 +eval/Actions Std 0.732888 +eval/Actions Max 0.99957 +eval/Actions Min -0.999714 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.84892e-06 +time/evaluation sampling (s) 2.4645 +time/exploration sampling (s) 2.5569 +time/logging (s) 0.00670773 +time/saving (s) 0.0118048 +time/training (s) 12.0306 +time/epoch (s) 17.0705 +time/total (s) 6481.32 +Epoch -672 +------------------------------ ---------------- +2022-05-15 19:50:45.075904 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -671 finished +------------------------------ ---------------- +epoch -671 +replay_buffer/size 999047 +trainer/num train calls 330000 +trainer/QF1 Loss 0.786741 +trainer/QF2 Loss 0.758349 +trainer/Policy Loss 11.2615 +trainer/Q1 Predictions Mean -73.7585 +trainer/Q1 Predictions Std 17.758 +trainer/Q1 Predictions Max -1.59919 +trainer/Q1 Predictions Min -86.4826 +trainer/Q2 Predictions Mean -73.7815 +trainer/Q2 Predictions Std 17.6499 +trainer/Q2 Predictions Max -2.15551 +trainer/Q2 Predictions Min -86.2523 +trainer/Q Targets Mean -73.8786 +trainer/Q Targets Std 17.6392 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4229 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00627021 +trainer/policy/mean Std 0.722007 +trainer/policy/mean Max 0.999625 +trainer/policy/mean Min -0.998954 +trainer/policy/std Mean 0.426124 +trainer/policy/std Std 0.020699 +trainer/policy/std Max 0.451347 +trainer/policy/std Min 0.391702 +trainer/Advantage Weights Mean 3.30681 +trainer/Advantage Weights Std 13.415 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.80862e-14 +trainer/Advantage Score Mean -0.300918 +trainer/Advantage Score Std 0.469824 +trainer/Advantage Score Max 1.19595 +trainer/Advantage Score Min -3.12035 +trainer/V1 Predictions Mean -73.5103 +trainer/V1 Predictions Std 17.8472 +trainer/V1 Predictions Max 0.478121 +trainer/V1 Predictions Min -86.0849 +trainer/VF Loss 0.0450958 +expl/num steps total 330000 +expl/num paths total 373 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0858822 +expl/Actions Std 0.828191 +expl/Actions Max 2.30146 +expl/Actions Min -2.49947 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 322273 +eval/num paths total 330 +eval/path length Mean 910 +eval/path length Std 0 +eval/path length Max 910 +eval/path length Min 910 +eval/Rewards Mean 0.0010989 +eval/Rewards Std 0.0331315 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0128873 +eval/Actions Std 0.742301 +eval/Actions Max 0.999241 +eval/Actions Min -0.999372 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.58069e-06 +time/evaluation sampling (s) 2.43814 +time/exploration sampling (s) 2.68598 +time/logging (s) 0.00651677 +time/saving (s) 0.0158259 +time/training (s) 12.1417 +time/epoch (s) 17.2881 +time/total (s) 6498.61 +Epoch -671 +------------------------------ ---------------- +2022-05-15 19:51:02.010483 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -670 finished +------------------------------ ---------------- +epoch -670 +replay_buffer/size 999047 +trainer/num train calls 331000 +trainer/QF1 Loss 0.626128 +trainer/QF2 Loss 0.670064 +trainer/Policy Loss 14.603 +trainer/Q1 Predictions Mean -74.5833 +trainer/Q1 Predictions Std 17.1274 +trainer/Q1 Predictions Max -0.920762 +trainer/Q1 Predictions Min -87.4444 +trainer/Q2 Predictions Mean -74.5245 +trainer/Q2 Predictions Std 17.151 +trainer/Q2 Predictions Max -0.549771 +trainer/Q2 Predictions Min -87.1019 +trainer/Q Targets Mean -74.364 +trainer/Q Targets Std 17.1592 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9369 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0117241 +trainer/policy/mean Std 0.728108 +trainer/policy/mean Max 0.998763 +trainer/policy/mean Min -0.997629 +trainer/policy/std Mean 0.423756 +trainer/policy/std Std 0.0214785 +trainer/policy/std Max 0.45119 +trainer/policy/std Min 0.387743 +trainer/Advantage Weights Mean 4.55153 +trainer/Advantage Weights Std 17.983 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.96088e-12 +trainer/Advantage Score Mean -0.33348 +trainer/Advantage Score Std 0.488679 +trainer/Advantage Score Max 1.03331 +trainer/Advantage Score Min -2.69576 +trainer/V1 Predictions Mean -74.1622 +trainer/V1 Predictions Std 17.1377 +trainer/V1 Predictions Max -0.474474 +trainer/V1 Predictions Min -86.7309 +trainer/VF Loss 0.0508745 +expl/num steps total 331000 +expl/num paths total 374 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0644249 +expl/Actions Std 0.76082 +expl/Actions Max 2.4843 +expl/Actions Min -2.17453 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 323273 +eval/num paths total 331 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.084803 +eval/Actions Std 0.756395 +eval/Actions Max 0.999145 +eval/Actions Min -0.999724 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.79909e-06 +time/evaluation sampling (s) 2.56648 +time/exploration sampling (s) 2.68782 +time/logging (s) 0.00726133 +time/saving (s) 0.0118955 +time/training (s) 11.6548 +time/epoch (s) 16.9283 +time/total (s) 6515.54 +Epoch -670 +------------------------------ ---------------- +2022-05-15 19:51:19.390578 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -669 finished +------------------------------ ---------------- +epoch -669 +replay_buffer/size 999047 +trainer/num train calls 332000 +trainer/QF1 Loss 1.42801 +trainer/QF2 Loss 1.34722 +trainer/Policy Loss 11.9762 +trainer/Q1 Predictions Mean -72.111 +trainer/Q1 Predictions Std 19.5665 +trainer/Q1 Predictions Max -0.293316 +trainer/Q1 Predictions Min -86.7368 +trainer/Q2 Predictions Mean -72.1051 +trainer/Q2 Predictions Std 19.4933 +trainer/Q2 Predictions Max -0.348289 +trainer/Q2 Predictions Min -86.6313 +trainer/Q Targets Mean -72.2302 +trainer/Q Targets Std 19.793 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0792 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00147492 +trainer/policy/mean Std 0.705314 +trainer/policy/mean Max 0.999114 +trainer/policy/mean Min -0.998478 +trainer/policy/std Mean 0.424402 +trainer/policy/std Std 0.0203743 +trainer/policy/std Max 0.450725 +trainer/policy/std Min 0.39009 +trainer/Advantage Weights Mean 3.24281 +trainer/Advantage Weights Std 9.8697 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.1637e-20 +trainer/Advantage Score Mean -0.35408 +trainer/Advantage Score Std 0.666568 +trainer/Advantage Score Max 0.518864 +trainer/Advantage Score Min -4.52799 +trainer/V1 Predictions Mean -71.9973 +trainer/V1 Predictions Std 19.8192 +trainer/V1 Predictions Max 0.327261 +trainer/V1 Predictions Min -86.6437 +trainer/VF Loss 0.0669899 +expl/num steps total 332000 +expl/num paths total 375 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0378062 +expl/Actions Std 0.847941 +expl/Actions Max 2.40987 +expl/Actions Min -2.29465 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 324273 +eval/num paths total 332 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.123603 +eval/Actions Std 0.807019 +eval/Actions Max 0.998203 +eval/Actions Min -0.999252 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.96817e-06 +time/evaluation sampling (s) 2.58705 +time/exploration sampling (s) 2.83696 +time/logging (s) 0.00699239 +time/saving (s) 0.00985671 +time/training (s) 11.9318 +time/epoch (s) 17.3726 +time/total (s) 6532.92 +Epoch -669 +------------------------------ ---------------- +2022-05-15 19:51:36.903604 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -668 finished +------------------------------ ---------------- +epoch -668 +replay_buffer/size 999047 +trainer/num train calls 333000 +trainer/QF1 Loss 0.948045 +trainer/QF2 Loss 0.953086 +trainer/Policy Loss 35.0903 +trainer/Q1 Predictions Mean -74.3463 +trainer/Q1 Predictions Std 17.3282 +trainer/Q1 Predictions Max -2.47139 +trainer/Q1 Predictions Min -87.0995 +trainer/Q2 Predictions Mean -74.396 +trainer/Q2 Predictions Std 17.2446 +trainer/Q2 Predictions Max -3.07204 +trainer/Q2 Predictions Min -87.2062 +trainer/Q Targets Mean -74.689 +trainer/Q Targets Std 17.2062 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1324 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0171594 +trainer/policy/mean Std 0.71521 +trainer/policy/mean Max 0.994752 +trainer/policy/mean Min -0.997097 +trainer/policy/std Mean 0.425131 +trainer/policy/std Std 0.0211944 +trainer/policy/std Max 0.449644 +trainer/policy/std Min 0.389429 +trainer/Advantage Weights Mean 8.36068 +trainer/Advantage Weights Std 22.3103 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.08216e-11 +trainer/Advantage Score Mean -0.135909 +trainer/Advantage Score Std 0.465174 +trainer/Advantage Score Max 0.829872 +trainer/Advantage Score Min -2.42028 +trainer/V1 Predictions Mean -74.4585 +trainer/V1 Predictions Std 17.3526 +trainer/V1 Predictions Max -2.38887 +trainer/V1 Predictions Min -86.9217 +trainer/VF Loss 0.0511923 +expl/num steps total 333000 +expl/num paths total 376 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.368861 +expl/Actions Std 0.883686 +expl/Actions Max 2.45594 +expl/Actions Min -2.72596 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 325273 +eval/num paths total 333 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.108166 +eval/Actions Std 0.737917 +eval/Actions Max 0.999567 +eval/Actions Min -0.997654 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.80794e-06 +time/evaluation sampling (s) 2.54544 +time/exploration sampling (s) 2.8886 +time/logging (s) 0.00665278 +time/saving (s) 0.00967104 +time/training (s) 12.0558 +time/epoch (s) 17.5061 +time/total (s) 6550.43 +Epoch -668 +------------------------------ ---------------- +2022-05-15 19:51:54.908093 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -667 finished +------------------------------ ---------------- +epoch -667 +replay_buffer/size 999047 +trainer/num train calls 334000 +trainer/QF1 Loss 1.13647 +trainer/QF2 Loss 1.08826 +trainer/Policy Loss 3.44978 +trainer/Q1 Predictions Mean -73.5633 +trainer/Q1 Predictions Std 16.9576 +trainer/Q1 Predictions Max -0.956191 +trainer/Q1 Predictions Min -86.3135 +trainer/Q2 Predictions Mean -73.6375 +trainer/Q2 Predictions Std 16.9243 +trainer/Q2 Predictions Max -0.868519 +trainer/Q2 Predictions Min -86.4627 +trainer/Q Targets Mean -73.5402 +trainer/Q Targets Std 17.3099 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3833 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0147552 +trainer/policy/mean Std 0.722532 +trainer/policy/mean Max 0.996409 +trainer/policy/mean Min -0.998461 +trainer/policy/std Mean 0.426728 +trainer/policy/std Std 0.0214923 +trainer/policy/std Max 0.449593 +trainer/policy/std Min 0.391012 +trainer/Advantage Weights Mean 0.614639 +trainer/Advantage Weights Std 6.72999 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.5215e-13 +trainer/Advantage Score Mean -0.781117 +trainer/Advantage Score Std 0.539353 +trainer/Advantage Score Max 1.04347 +trainer/Advantage Score Min -2.90088 +trainer/V1 Predictions Mean -73.3746 +trainer/V1 Predictions Std 17.0669 +trainer/V1 Predictions Max 0.0446864 +trainer/V1 Predictions Min -86.1096 +trainer/VF Loss 0.0940346 +expl/num steps total 334000 +expl/num paths total 377 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0380496 +expl/Actions Std 0.83126 +expl/Actions Max 2.32372 +expl/Actions Min -2.44772 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 326273 +eval/num paths total 334 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0306298 +eval/Actions Std 0.719507 +eval/Actions Max 0.998329 +eval/Actions Min -0.999482 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.50805e-06 +time/evaluation sampling (s) 2.50032 +time/exploration sampling (s) 2.83323 +time/logging (s) 0.00688227 +time/saving (s) 0.0120746 +time/training (s) 12.6443 +time/epoch (s) 17.9968 +time/total (s) 6568.43 +Epoch -667 +------------------------------ ---------------- +2022-05-15 19:52:12.000523 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -666 finished +------------------------------ ---------------- +epoch -666 +replay_buffer/size 999047 +trainer/num train calls 335000 +trainer/QF1 Loss 0.812937 +trainer/QF2 Loss 0.782666 +trainer/Policy Loss 9.98067 +trainer/Q1 Predictions Mean -72.5063 +trainer/Q1 Predictions Std 18.9876 +trainer/Q1 Predictions Max -0.849749 +trainer/Q1 Predictions Min -86.2215 +trainer/Q2 Predictions Mean -72.5645 +trainer/Q2 Predictions Std 18.9986 +trainer/Q2 Predictions Max -1.32612 +trainer/Q2 Predictions Min -86.1746 +trainer/Q Targets Mean -72.3186 +trainer/Q Targets Std 19.3445 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3536 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.017516 +trainer/policy/mean Std 0.72438 +trainer/policy/mean Max 0.999468 +trainer/policy/mean Min -0.999166 +trainer/policy/std Mean 0.425954 +trainer/policy/std Std 0.0192788 +trainer/policy/std Max 0.445499 +trainer/policy/std Min 0.391816 +trainer/Advantage Weights Mean 3.1057 +trainer/Advantage Weights Std 15.6047 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.32334e-11 +trainer/Advantage Score Mean -0.420805 +trainer/Advantage Score Std 0.499702 +trainer/Advantage Score Max 0.881889 +trainer/Advantage Score Min -2.41275 +trainer/V1 Predictions Mean -72.0623 +trainer/V1 Predictions Std 19.4093 +trainer/V1 Predictions Max 0.245624 +trainer/V1 Predictions Min -86.1647 +trainer/VF Loss 0.0519443 +expl/num steps total 335000 +expl/num paths total 379 +expl/path length Mean 500 +expl/path length Std 431 +expl/path length Max 931 +expl/path length Min 69 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0469453 +expl/Actions Std 0.822666 +expl/Actions Max 2.44962 +expl/Actions Min -2.42727 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 326991 +eval/num paths total 335 +eval/path length Mean 718 +eval/path length Std 0 +eval/path length Max 718 +eval/path length Min 718 +eval/Rewards Mean 0.00139276 +eval/Rewards Std 0.0372937 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0650666 +eval/Actions Std 0.724439 +eval/Actions Max 0.999496 +eval/Actions Min -0.997544 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.09572e-06 +time/evaluation sampling (s) 2.43313 +time/exploration sampling (s) 2.75017 +time/logging (s) 0.00732543 +time/saving (s) 0.0132876 +time/training (s) 11.8806 +time/epoch (s) 17.0845 +time/total (s) 6585.52 +Epoch -666 +------------------------------ ---------------- +2022-05-15 19:52:29.544440 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -665 finished +------------------------------ ---------------- +epoch -665 +replay_buffer/size 999047 +trainer/num train calls 336000 +trainer/QF1 Loss 0.788259 +trainer/QF2 Loss 0.851916 +trainer/Policy Loss 13.253 +trainer/Q1 Predictions Mean -74.4568 +trainer/Q1 Predictions Std 16.2487 +trainer/Q1 Predictions Max -0.986316 +trainer/Q1 Predictions Min -86.7536 +trainer/Q2 Predictions Mean -74.4287 +trainer/Q2 Predictions Std 16.2642 +trainer/Q2 Predictions Max -1.08453 +trainer/Q2 Predictions Min -86.6529 +trainer/Q Targets Mean -74.0611 +trainer/Q Targets Std 16.4869 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1243 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0140594 +trainer/policy/mean Std 0.716889 +trainer/policy/mean Max 0.999109 +trainer/policy/mean Min -0.998923 +trainer/policy/std Mean 0.426804 +trainer/policy/std Std 0.0206123 +trainer/policy/std Max 0.450323 +trainer/policy/std Min 0.390702 +trainer/Advantage Weights Mean 4.0276 +trainer/Advantage Weights Std 17.499 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.84074e-26 +trainer/Advantage Score Mean -0.475799 +trainer/Advantage Score Std 0.73928 +trainer/Advantage Score Max 1.31135 +trainer/Advantage Score Min -5.79443 +trainer/V1 Predictions Mean -73.8523 +trainer/V1 Predictions Std 16.5494 +trainer/V1 Predictions Max -0.2224 +trainer/V1 Predictions Min -86.1502 +trainer/VF Loss 0.0982884 +expl/num steps total 336000 +expl/num paths total 380 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0179602 +expl/Actions Std 0.820941 +expl/Actions Max 2.32025 +expl/Actions Min -2.38182 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 327855 +eval/num paths total 336 +eval/path length Mean 864 +eval/path length Std 0 +eval/path length Max 864 +eval/path length Min 864 +eval/Rewards Mean 0.00115741 +eval/Rewards Std 0.034001 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0181439 +eval/Actions Std 0.737958 +eval/Actions Max 0.999431 +eval/Actions Min -0.999267 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.76603e-06 +time/evaluation sampling (s) 2.45685 +time/exploration sampling (s) 2.8381 +time/logging (s) 0.00912404 +time/saving (s) 0.014177 +time/training (s) 12.2177 +time/epoch (s) 17.536 +time/total (s) 6603.06 +Epoch -665 +------------------------------ ---------------- +2022-05-15 19:52:47.155720 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -664 finished +------------------------------ ---------------- +epoch -664 +replay_buffer/size 999047 +trainer/num train calls 337000 +trainer/QF1 Loss 0.957097 +trainer/QF2 Loss 0.902974 +trainer/Policy Loss 13.1163 +trainer/Q1 Predictions Mean -73.9216 +trainer/Q1 Predictions Std 18.9721 +trainer/Q1 Predictions Max 0.3016 +trainer/Q1 Predictions Min -86.9032 +trainer/Q2 Predictions Mean -73.9456 +trainer/Q2 Predictions Std 19.001 +trainer/Q2 Predictions Max -0.27804 +trainer/Q2 Predictions Min -86.6959 +trainer/Q Targets Mean -73.5529 +trainer/Q Targets Std 19.0636 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8271 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00372158 +trainer/policy/mean Std 0.717295 +trainer/policy/mean Max 0.998229 +trainer/policy/mean Min -0.997463 +trainer/policy/std Mean 0.425632 +trainer/policy/std Std 0.0210942 +trainer/policy/std Max 0.449345 +trainer/policy/std Min 0.391066 +trainer/Advantage Weights Mean 2.14589 +trainer/Advantage Weights Std 10.5777 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.60317e-23 +trainer/Advantage Score Mean -0.51808 +trainer/Advantage Score Std 0.612319 +trainer/Advantage Score Max 0.530293 +trainer/Advantage Score Min -5.24875 +trainer/V1 Predictions Mean -73.3997 +trainer/V1 Predictions Std 19.1282 +trainer/V1 Predictions Max 2.37542 +trainer/V1 Predictions Min -86.7234 +trainer/VF Loss 0.0697179 +expl/num steps total 337000 +expl/num paths total 381 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0129158 +expl/Actions Std 0.811127 +expl/Actions Max 2.37492 +expl/Actions Min -2.24064 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 328855 +eval/num paths total 337 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.253184 +eval/Actions Std 0.49755 +eval/Actions Max 0.999214 +eval/Actions Min -0.995555 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.98396e-06 +time/evaluation sampling (s) 2.575 +time/exploration sampling (s) 2.80313 +time/logging (s) 0.00673149 +time/saving (s) 0.0151535 +time/training (s) 12.2014 +time/epoch (s) 17.6014 +time/total (s) 6620.67 +Epoch -664 +------------------------------ ---------------- +2022-05-15 19:53:04.945319 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -663 finished +------------------------------ ---------------- +epoch -663 +replay_buffer/size 999047 +trainer/num train calls 338000 +trainer/QF1 Loss 1.39495 +trainer/QF2 Loss 1.44491 +trainer/Policy Loss 17.0271 +trainer/Q1 Predictions Mean -74.1149 +trainer/Q1 Predictions Std 17.9142 +trainer/Q1 Predictions Max -0.8719 +trainer/Q1 Predictions Min -86.4292 +trainer/Q2 Predictions Mean -74.1005 +trainer/Q2 Predictions Std 17.9627 +trainer/Q2 Predictions Max -0.756037 +trainer/Q2 Predictions Min -86.4924 +trainer/Q Targets Mean -73.8348 +trainer/Q Targets Std 17.7682 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3931 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0102314 +trainer/policy/mean Std 0.724038 +trainer/policy/mean Max 0.997917 +trainer/policy/mean Min -0.998684 +trainer/policy/std Mean 0.426965 +trainer/policy/std Std 0.0209717 +trainer/policy/std Max 0.44999 +trainer/policy/std Min 0.392668 +trainer/Advantage Weights Mean 2.46326 +trainer/Advantage Weights Std 13.7047 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.37464e-13 +trainer/Advantage Score Mean -0.451753 +trainer/Advantage Score Std 0.479059 +trainer/Advantage Score Max 2.33918 +trainer/Advantage Score Min -2.82519 +trainer/V1 Predictions Mean -73.7047 +trainer/V1 Predictions Std 17.7496 +trainer/V1 Predictions Max -0.28361 +trainer/V1 Predictions Min -86.2421 +trainer/VF Loss 0.0678053 +expl/num steps total 338000 +expl/num paths total 382 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.196833 +expl/Actions Std 0.793034 +expl/Actions Max 2.71798 +expl/Actions Min -2.34622 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 329466 +eval/num paths total 338 +eval/path length Mean 611 +eval/path length Std 0 +eval/path length Max 611 +eval/path length Min 611 +eval/Rewards Mean 0.00163666 +eval/Rewards Std 0.0404226 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0181676 +eval/Actions Std 0.733037 +eval/Actions Max 0.999594 +eval/Actions Min -0.999012 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.24892e-06 +time/evaluation sampling (s) 2.58862 +time/exploration sampling (s) 2.90078 +time/logging (s) 0.00696776 +time/saving (s) 0.0132741 +time/training (s) 12.271 +time/epoch (s) 17.7807 +time/total (s) 6638.45 +Epoch -663 +------------------------------ ---------------- +2022-05-15 19:53:22.407059 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -662 finished +------------------------------ ---------------- +epoch -662 +replay_buffer/size 999047 +trainer/num train calls 339000 +trainer/QF1 Loss 0.907542 +trainer/QF2 Loss 1.05081 +trainer/Policy Loss 16.2508 +trainer/Q1 Predictions Mean -72.2914 +trainer/Q1 Predictions Std 18.6807 +trainer/Q1 Predictions Max -1.06407 +trainer/Q1 Predictions Min -87.6202 +trainer/Q2 Predictions Mean -72.291 +trainer/Q2 Predictions Std 18.716 +trainer/Q2 Predictions Max -0.55224 +trainer/Q2 Predictions Min -87.2931 +trainer/Q Targets Mean -72.3161 +trainer/Q Targets Std 18.4923 +trainer/Q Targets Max -1.64794 +trainer/Q Targets Min -86.9564 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00219848 +trainer/policy/mean Std 0.714637 +trainer/policy/mean Max 0.999415 +trainer/policy/mean Min -0.99895 +trainer/policy/std Mean 0.424765 +trainer/policy/std Std 0.0202321 +trainer/policy/std Max 0.446004 +trainer/policy/std Min 0.391142 +trainer/Advantage Weights Mean 3.63927 +trainer/Advantage Weights Std 15.9833 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.12471e-25 +trainer/Advantage Score Mean -0.436319 +trainer/Advantage Score Std 0.638028 +trainer/Advantage Score Max 0.896648 +trainer/Advantage Score Min -5.61476 +trainer/V1 Predictions Mean -71.9508 +trainer/V1 Predictions Std 18.77 +trainer/V1 Predictions Max 1.3717 +trainer/V1 Predictions Min -86.8815 +trainer/VF Loss 0.0736981 +expl/num steps total 339000 +expl/num paths total 383 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0303006 +expl/Actions Std 0.810388 +expl/Actions Max 2.6067 +expl/Actions Min -2.28416 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 330006 +eval/num paths total 339 +eval/path length Mean 540 +eval/path length Std 0 +eval/path length Max 540 +eval/path length Min 540 +eval/Rewards Mean 0.00185185 +eval/Rewards Std 0.0429933 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0553153 +eval/Actions Std 0.703377 +eval/Actions Max 0.999353 +eval/Actions Min -0.999418 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.87406e-06 +time/evaluation sampling (s) 2.51085 +time/exploration sampling (s) 2.742 +time/logging (s) 0.00580006 +time/saving (s) 0.00954388 +time/training (s) 12.1828 +time/epoch (s) 17.451 +time/total (s) 6655.91 +Epoch -662 +------------------------------ ---------------- +2022-05-15 19:53:39.402461 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -661 finished +------------------------------ ---------------- +epoch -661 +replay_buffer/size 999047 +trainer/num train calls 340000 +trainer/QF1 Loss 1.20984 +trainer/QF2 Loss 1.14484 +trainer/Policy Loss 17.2606 +trainer/Q1 Predictions Mean -73.0341 +trainer/Q1 Predictions Std 19.5227 +trainer/Q1 Predictions Max -1.10402 +trainer/Q1 Predictions Min -86.4935 +trainer/Q2 Predictions Mean -72.9475 +trainer/Q2 Predictions Std 19.5217 +trainer/Q2 Predictions Max -0.895267 +trainer/Q2 Predictions Min -86.2754 +trainer/Q Targets Mean -73.2194 +trainer/Q Targets Std 19.3103 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7057 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0137022 +trainer/policy/mean Std 0.725094 +trainer/policy/mean Max 0.998473 +trainer/policy/mean Min -0.998943 +trainer/policy/std Mean 0.426651 +trainer/policy/std Std 0.0187805 +trainer/policy/std Max 0.448997 +trainer/policy/std Min 0.396206 +trainer/Advantage Weights Mean 4.52968 +trainer/Advantage Weights Std 18.4747 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.53753e-21 +trainer/Advantage Score Mean -0.309543 +trainer/Advantage Score Std 0.506964 +trainer/Advantage Score Max 1.24516 +trainer/Advantage Score Min -4.74231 +trainer/V1 Predictions Mean -73.0068 +trainer/V1 Predictions Std 19.3282 +trainer/V1 Predictions Max -1.94852 +trainer/V1 Predictions Min -86.5795 +trainer/VF Loss 0.0563604 +expl/num steps total 340000 +expl/num paths total 384 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0027579 +expl/Actions Std 0.82935 +expl/Actions Max 2.24762 +expl/Actions Min -2.40912 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 331006 +eval/num paths total 340 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.200233 +eval/Actions Std 0.756189 +eval/Actions Max 0.999175 +eval/Actions Min -0.999684 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.91085e-06 +time/evaluation sampling (s) 2.42552 +time/exploration sampling (s) 2.78321 +time/logging (s) 0.0067843 +time/saving (s) 0.00989891 +time/training (s) 11.7643 +time/epoch (s) 16.9897 +time/total (s) 6672.9 +Epoch -661 +------------------------------ ---------------- +2022-05-15 19:53:56.959642 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -660 finished +------------------------------ ---------------- +epoch -660 +replay_buffer/size 999047 +trainer/num train calls 341000 +trainer/QF1 Loss 0.668382 +trainer/QF2 Loss 0.652084 +trainer/Policy Loss 19.4993 +trainer/Q1 Predictions Mean -74.1611 +trainer/Q1 Predictions Std 15.6568 +trainer/Q1 Predictions Max -3.68976 +trainer/Q1 Predictions Min -86.3519 +trainer/Q2 Predictions Mean -74.244 +trainer/Q2 Predictions Std 15.6342 +trainer/Q2 Predictions Max -3.85058 +trainer/Q2 Predictions Min -86.184 +trainer/Q Targets Mean -74.1293 +trainer/Q Targets Std 15.6989 +trainer/Q Targets Max -4.38371 +trainer/Q Targets Min -86.2286 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0142104 +trainer/policy/mean Std 0.699227 +trainer/policy/mean Max 0.997647 +trainer/policy/mean Min -0.997286 +trainer/policy/std Mean 0.424752 +trainer/policy/std Std 0.0210287 +trainer/policy/std Max 0.448848 +trainer/policy/std Min 0.389172 +trainer/Advantage Weights Mean 3.86889 +trainer/Advantage Weights Std 16.0692 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.71628e-13 +trainer/Advantage Score Mean -0.333795 +trainer/Advantage Score Std 0.540815 +trainer/Advantage Score Max 2.47429 +trainer/Advantage Score Min -2.81903 +trainer/V1 Predictions Mean -73.8676 +trainer/V1 Predictions Std 15.7964 +trainer/V1 Predictions Max -3.12739 +trainer/V1 Predictions Min -86.0056 +trainer/VF Loss 0.0750226 +expl/num steps total 341000 +expl/num paths total 385 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00117651 +expl/Actions Std 0.818469 +expl/Actions Max 2.18702 +expl/Actions Min -2.39962 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 332006 +eval/num paths total 341 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.321294 +eval/Actions Std 0.662449 +eval/Actions Max 0.999255 +eval/Actions Min -0.998675 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.11434e-06 +time/evaluation sampling (s) 2.46807 +time/exploration sampling (s) 2.82025 +time/logging (s) 0.00692993 +time/saving (s) 0.0100704 +time/training (s) 12.2442 +time/epoch (s) 17.5496 +time/total (s) 6690.45 +Epoch -660 +------------------------------ ---------------- +2022-05-15 19:54:14.156972 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -659 finished +------------------------------ ---------------- +epoch -659 +replay_buffer/size 999047 +trainer/num train calls 342000 +trainer/QF1 Loss 0.862285 +trainer/QF2 Loss 0.867142 +trainer/Policy Loss 35.8952 +trainer/Q1 Predictions Mean -71.6461 +trainer/Q1 Predictions Std 19.7248 +trainer/Q1 Predictions Max -0.932131 +trainer/Q1 Predictions Min -86.462 +trainer/Q2 Predictions Mean -71.6246 +trainer/Q2 Predictions Std 19.7082 +trainer/Q2 Predictions Max -0.680049 +trainer/Q2 Predictions Min -86.2 +trainer/Q Targets Mean -72.0699 +trainer/Q Targets Std 19.8191 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3181 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0400558 +trainer/policy/mean Std 0.707793 +trainer/policy/mean Max 0.997668 +trainer/policy/mean Min -0.998772 +trainer/policy/std Mean 0.423462 +trainer/policy/std Std 0.0203879 +trainer/policy/std Max 0.447415 +trainer/policy/std Min 0.390296 +trainer/Advantage Weights Mean 7.84533 +trainer/Advantage Weights Std 23.3907 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.92504e-13 +trainer/Advantage Score Mean -0.153735 +trainer/Advantage Score Std 0.497022 +trainer/Advantage Score Max 1.956 +trainer/Advantage Score Min -2.78636 +trainer/V1 Predictions Mean -71.8434 +trainer/V1 Predictions Std 19.7405 +trainer/V1 Predictions Max 0.201276 +trainer/V1 Predictions Min -86.1572 +trainer/VF Loss 0.0859673 +expl/num steps total 342000 +expl/num paths total 387 +expl/path length Mean 500 +expl/path length Std 113 +expl/path length Max 613 +expl/path length Min 387 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0320785 +expl/Actions Std 0.818506 +expl/Actions Max 2.31705 +expl/Actions Min -2.32001 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 333006 +eval/num paths total 342 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0203829 +eval/Actions Std 0.724505 +eval/Actions Max 0.999052 +eval/Actions Min -0.999165 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.80607e-06 +time/evaluation sampling (s) 2.48676 +time/exploration sampling (s) 2.65919 +time/logging (s) 0.010835 +time/saving (s) 0.0159795 +time/training (s) 12.0214 +time/epoch (s) 17.1942 +time/total (s) 6707.65 +Epoch -659 +------------------------------ ---------------- +2022-05-15 19:54:31.856617 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -658 finished +------------------------------ ---------------- +epoch -658 +replay_buffer/size 999047 +trainer/num train calls 343000 +trainer/QF1 Loss 0.931182 +trainer/QF2 Loss 0.863021 +trainer/Policy Loss 22.046 +trainer/Q1 Predictions Mean -72.425 +trainer/Q1 Predictions Std 19.6949 +trainer/Q1 Predictions Max -1.232 +trainer/Q1 Predictions Min -85.8547 +trainer/Q2 Predictions Mean -72.4377 +trainer/Q2 Predictions Std 19.6949 +trainer/Q2 Predictions Max -0.992728 +trainer/Q2 Predictions Min -85.8101 +trainer/Q Targets Mean -72.2187 +trainer/Q Targets Std 20.1553 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0661 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0134406 +trainer/policy/mean Std 0.706613 +trainer/policy/mean Max 0.998118 +trainer/policy/mean Min -0.998218 +trainer/policy/std Mean 0.424062 +trainer/policy/std Std 0.0199811 +trainer/policy/std Max 0.445174 +trainer/policy/std Min 0.390583 +trainer/Advantage Weights Mean 5.13877 +trainer/Advantage Weights Std 17.8821 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.69916e-23 +trainer/Advantage Score Mean -0.378529 +trainer/Advantage Score Std 0.74527 +trainer/Advantage Score Max 0.810516 +trainer/Advantage Score Min -5.12191 +trainer/V1 Predictions Mean -71.9738 +trainer/V1 Predictions Std 20.1951 +trainer/V1 Predictions Max -0.466362 +trainer/V1 Predictions Min -85.8357 +trainer/VF Loss 0.0854717 +expl/num steps total 343000 +expl/num paths total 388 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0166547 +expl/Actions Std 0.788072 +expl/Actions Max 2.46154 +expl/Actions Min -2.21358 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 334006 +eval/num paths total 343 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.102473 +eval/Actions Std 0.711945 +eval/Actions Max 0.99844 +eval/Actions Min -0.997277 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66032e-06 +time/evaluation sampling (s) 2.46278 +time/exploration sampling (s) 2.6507 +time/logging (s) 0.00728532 +time/saving (s) 0.0102949 +time/training (s) 12.5575 +time/epoch (s) 17.6886 +time/total (s) 6725.34 +Epoch -658 +------------------------------ ---------------- +2022-05-15 19:54:49.493138 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -657 finished +------------------------------ ---------------- +epoch -657 +replay_buffer/size 999047 +trainer/num train calls 344000 +trainer/QF1 Loss 1.08579 +trainer/QF2 Loss 1.09075 +trainer/Policy Loss 1.36731 +trainer/Q1 Predictions Mean -74.4386 +trainer/Q1 Predictions Std 16.4418 +trainer/Q1 Predictions Max -0.840318 +trainer/Q1 Predictions Min -86.6327 +trainer/Q2 Predictions Mean -74.3178 +trainer/Q2 Predictions Std 16.4449 +trainer/Q2 Predictions Max -0.569348 +trainer/Q2 Predictions Min -86.491 +trainer/Q Targets Mean -73.7931 +trainer/Q Targets Std 16.6196 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5708 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00534265 +trainer/policy/mean Std 0.710082 +trainer/policy/mean Max 0.998141 +trainer/policy/mean Min -0.997243 +trainer/policy/std Mean 0.424524 +trainer/policy/std Std 0.0202653 +trainer/policy/std Max 0.4462 +trainer/policy/std Min 0.390968 +trainer/Advantage Weights Mean 0.248144 +trainer/Advantage Weights Std 1.60479 +trainer/Advantage Weights Max 17.3836 +trainer/Advantage Weights Min 2.43898e-16 +trainer/Advantage Score Mean -0.661293 +trainer/Advantage Score Std 0.562518 +trainer/Advantage Score Max 0.285553 +trainer/Advantage Score Min -3.59498 +trainer/V1 Predictions Mean -73.5684 +trainer/V1 Predictions Std 16.6559 +trainer/V1 Predictions Max 0.46766 +trainer/V1 Predictions Min -85.8021 +trainer/VF Loss 0.0761139 +expl/num steps total 344000 +expl/num paths total 389 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.108787 +expl/Actions Std 0.888229 +expl/Actions Max 2.3666 +expl/Actions Min -2.56426 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 335006 +eval/num paths total 344 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.052161 +eval/Actions Std 0.762961 +eval/Actions Max 0.999393 +eval/Actions Min -0.999419 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.05381e-06 +time/evaluation sampling (s) 2.4764 +time/exploration sampling (s) 2.78342 +time/logging (s) 0.00914035 +time/saving (s) 0.012296 +time/training (s) 12.3499 +time/epoch (s) 17.6311 +time/total (s) 6742.98 +Epoch -657 +------------------------------ ---------------- +2022-05-15 19:55:06.853376 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -656 finished +------------------------------ ---------------- +epoch -656 +replay_buffer/size 999047 +trainer/num train calls 345000 +trainer/QF1 Loss 1.70574 +trainer/QF2 Loss 1.62148 +trainer/Policy Loss 27.6737 +trainer/Q1 Predictions Mean -72.7867 +trainer/Q1 Predictions Std 18.008 +trainer/Q1 Predictions Max -1.04009 +trainer/Q1 Predictions Min -86.7985 +trainer/Q2 Predictions Mean -72.7406 +trainer/Q2 Predictions Std 17.9069 +trainer/Q2 Predictions Max -1.24886 +trainer/Q2 Predictions Min -86.5951 +trainer/Q Targets Mean -72.9898 +trainer/Q Targets Std 18.2533 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6311 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00387598 +trainer/policy/mean Std 0.71713 +trainer/policy/mean Max 0.997584 +trainer/policy/mean Min -0.99755 +trainer/policy/std Mean 0.423542 +trainer/policy/std Std 0.0202749 +trainer/policy/std Max 0.444286 +trainer/policy/std Min 0.39328 +trainer/Advantage Weights Mean 7.5868 +trainer/Advantage Weights Std 23.7801 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.53597e-22 +trainer/Advantage Score Mean -0.234858 +trainer/Advantage Score Std 0.586661 +trainer/Advantage Score Max 2.33606 +trainer/Advantage Score Min -4.97263 +trainer/V1 Predictions Mean -72.8672 +trainer/V1 Predictions Std 18.0112 +trainer/V1 Predictions Max -0.450777 +trainer/V1 Predictions Min -86.6828 +trainer/VF Loss 0.102232 +expl/num steps total 345000 +expl/num paths total 391 +expl/path length Mean 500 +expl/path length Std 411 +expl/path length Max 911 +expl/path length Min 89 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.023107 +expl/Actions Std 0.833491 +expl/Actions Max 2.31195 +expl/Actions Min -2.28015 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 335742 +eval/num paths total 345 +eval/path length Mean 736 +eval/path length Std 0 +eval/path length Max 736 +eval/path length Min 736 +eval/Rewards Mean 0.0013587 +eval/Rewards Std 0.0368354 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0280281 +eval/Actions Std 0.75035 +eval/Actions Max 0.99876 +eval/Actions Min -0.999638 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.82237e-06 +time/evaluation sampling (s) 2.46816 +time/exploration sampling (s) 2.58291 +time/logging (s) 0.00667049 +time/saving (s) 0.0102231 +time/training (s) 12.2799 +time/epoch (s) 17.3479 +time/total (s) 6760.33 +Epoch -656 +------------------------------ ---------------- +2022-05-15 19:55:24.607306 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -655 finished +------------------------------ ---------------- +epoch -655 +replay_buffer/size 999047 +trainer/num train calls 346000 +trainer/QF1 Loss 0.717546 +trainer/QF2 Loss 0.627331 +trainer/Policy Loss 28.0183 +trainer/Q1 Predictions Mean -73.2195 +trainer/Q1 Predictions Std 19.4324 +trainer/Q1 Predictions Max -0.576973 +trainer/Q1 Predictions Min -86.8565 +trainer/Q2 Predictions Mean -73.2923 +trainer/Q2 Predictions Std 19.4152 +trainer/Q2 Predictions Max -0.0318717 +trainer/Q2 Predictions Min -87.0178 +trainer/Q Targets Mean -72.9617 +trainer/Q Targets Std 19.3073 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6306 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00685277 +trainer/policy/mean Std 0.720862 +trainer/policy/mean Max 0.999746 +trainer/policy/mean Min -0.995818 +trainer/policy/std Mean 0.422592 +trainer/policy/std Std 0.0207128 +trainer/policy/std Max 0.446719 +trainer/policy/std Min 0.389566 +trainer/Advantage Weights Mean 6.67091 +trainer/Advantage Weights Std 22.2259 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.27936e-12 +trainer/Advantage Score Mean -0.233691 +trainer/Advantage Score Std 0.494469 +trainer/Advantage Score Max 2.10143 +trainer/Advantage Score Min -2.68071 +trainer/V1 Predictions Mean -72.6788 +trainer/V1 Predictions Std 19.3863 +trainer/V1 Predictions Max 0.216697 +trainer/V1 Predictions Min -86.4708 +trainer/VF Loss 0.0625296 +expl/num steps total 346000 +expl/num paths total 392 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0507251 +expl/Actions Std 0.832559 +expl/Actions Max 2.50658 +expl/Actions Min -2.41362 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 336742 +eval/num paths total 346 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0897786 +eval/Actions Std 0.704069 +eval/Actions Max 0.998845 +eval/Actions Min -0.997834 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.07709e-06 +time/evaluation sampling (s) 2.52317 +time/exploration sampling (s) 2.70958 +time/logging (s) 0.00661591 +time/saving (s) 0.00946376 +time/training (s) 12.498 +time/epoch (s) 17.7469 +time/total (s) 6778.08 +Epoch -655 +------------------------------ ---------------- +2022-05-15 19:55:42.338724 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -654 finished +------------------------------ ---------------- +epoch -654 +replay_buffer/size 999047 +trainer/num train calls 347000 +trainer/QF1 Loss 1.03825 +trainer/QF2 Loss 1.14961 +trainer/Policy Loss 29.1307 +trainer/Q1 Predictions Mean -74.0565 +trainer/Q1 Predictions Std 16.1242 +trainer/Q1 Predictions Max -0.279428 +trainer/Q1 Predictions Min -86.1494 +trainer/Q2 Predictions Mean -74.0372 +trainer/Q2 Predictions Std 16.1651 +trainer/Q2 Predictions Max -0.28188 +trainer/Q2 Predictions Min -86.0108 +trainer/Q Targets Mean -74.218 +trainer/Q Targets Std 16.1273 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.6269 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00597524 +trainer/policy/mean Std 0.714716 +trainer/policy/mean Max 0.999419 +trainer/policy/mean Min -0.999134 +trainer/policy/std Mean 0.424913 +trainer/policy/std Std 0.0204756 +trainer/policy/std Max 0.447433 +trainer/policy/std Min 0.390344 +trainer/Advantage Weights Mean 6.10238 +trainer/Advantage Weights Std 20.4702 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.18697e-15 +trainer/Advantage Score Mean -0.395044 +trainer/Advantage Score Std 0.559313 +trainer/Advantage Score Max 1.31072 +trainer/Advantage Score Min -3.43674 +trainer/V1 Predictions Mean -73.9643 +trainer/V1 Predictions Std 16.1514 +trainer/V1 Predictions Max 0.0894496 +trainer/V1 Predictions Min -85.4533 +trainer/VF Loss 0.0733795 +expl/num steps total 347000 +expl/num paths total 393 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0231968 +expl/Actions Std 0.821228 +expl/Actions Max 2.43049 +expl/Actions Min -2.4038 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 337451 +eval/num paths total 347 +eval/path length Mean 709 +eval/path length Std 0 +eval/path length Max 709 +eval/path length Min 709 +eval/Rewards Mean 0.00141044 +eval/Rewards Std 0.0375293 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0391311 +eval/Actions Std 0.707708 +eval/Actions Max 0.998703 +eval/Actions Min -0.996823 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.65613e-06 +time/evaluation sampling (s) 2.53325 +time/exploration sampling (s) 2.67467 +time/logging (s) 0.00828293 +time/saving (s) 0.0127914 +time/training (s) 12.4972 +time/epoch (s) 17.7262 +time/total (s) 6795.81 +Epoch -654 +------------------------------ ---------------- +2022-05-15 19:56:00.024492 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -653 finished +------------------------------ ---------------- +epoch -653 +replay_buffer/size 999047 +trainer/num train calls 348000 +trainer/QF1 Loss 0.50676 +trainer/QF2 Loss 0.426837 +trainer/Policy Loss 15.9735 +trainer/Q1 Predictions Mean -73.4557 +trainer/Q1 Predictions Std 18.2275 +trainer/Q1 Predictions Max -0.288454 +trainer/Q1 Predictions Min -86.3194 +trainer/Q2 Predictions Mean -73.5487 +trainer/Q2 Predictions Std 18.1803 +trainer/Q2 Predictions Max -0.405853 +trainer/Q2 Predictions Min -86.2711 +trainer/Q Targets Mean -73.5591 +trainer/Q Targets Std 18.1707 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5137 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0049775 +trainer/policy/mean Std 0.719682 +trainer/policy/mean Max 0.999399 +trainer/policy/mean Min -0.997726 +trainer/policy/std Mean 0.424863 +trainer/policy/std Std 0.0207892 +trainer/policy/std Max 0.446346 +trainer/policy/std Min 0.387596 +trainer/Advantage Weights Mean 3.59016 +trainer/Advantage Weights Std 15.0172 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.22675e-14 +trainer/Advantage Score Mean -0.366553 +trainer/Advantage Score Std 0.59672 +trainer/Advantage Score Max 1.50467 +trainer/Advantage Score Min -3.20318 +trainer/V1 Predictions Mean -73.2569 +trainer/V1 Predictions Std 18.4141 +trainer/V1 Predictions Max 0.825321 +trainer/V1 Predictions Min -86.2456 +trainer/VF Loss 0.0664099 +expl/num steps total 348000 +expl/num paths total 395 +expl/path length Mean 500 +expl/path length Std 311 +expl/path length Max 811 +expl/path length Min 189 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0453177 +expl/Actions Std 0.825346 +expl/Actions Max 2.308 +expl/Actions Min -2.11195 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 338451 +eval/num paths total 348 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.148501 +eval/Actions Std 0.734695 +eval/Actions Max 0.999028 +eval/Actions Min -0.998497 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.2573e-06 +time/evaluation sampling (s) 2.52058 +time/exploration sampling (s) 2.63172 +time/logging (s) 0.00785744 +time/saving (s) 0.0131723 +time/training (s) 12.5036 +time/epoch (s) 17.6769 +time/total (s) 6813.5 +Epoch -653 +------------------------------ ---------------- +2022-05-15 19:56:17.638618 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -652 finished +------------------------------ ---------------- +epoch -652 +replay_buffer/size 999047 +trainer/num train calls 349000 +trainer/QF1 Loss 1.1121 +trainer/QF2 Loss 1.07842 +trainer/Policy Loss 5.71039 +trainer/Q1 Predictions Mean -72.9046 +trainer/Q1 Predictions Std 18.8249 +trainer/Q1 Predictions Max -0.13583 +trainer/Q1 Predictions Min -86.971 +trainer/Q2 Predictions Mean -72.9296 +trainer/Q2 Predictions Std 18.8218 +trainer/Q2 Predictions Max -0.31305 +trainer/Q2 Predictions Min -87.1003 +trainer/Q Targets Mean -72.5187 +trainer/Q Targets Std 18.6706 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6947 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0196415 +trainer/policy/mean Std 0.701673 +trainer/policy/mean Max 0.999832 +trainer/policy/mean Min -0.998351 +trainer/policy/std Mean 0.425923 +trainer/policy/std Std 0.0210839 +trainer/policy/std Max 0.447826 +trainer/policy/std Min 0.390053 +trainer/Advantage Weights Mean 2.00746 +trainer/Advantage Weights Std 11.2604 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.47356e-12 +trainer/Advantage Score Mean -0.404343 +trainer/Advantage Score Std 0.454301 +trainer/Advantage Score Max 0.637222 +trainer/Advantage Score Min -2.63858 +trainer/V1 Predictions Mean -72.2279 +trainer/V1 Predictions Std 18.8814 +trainer/V1 Predictions Max 0.213051 +trainer/V1 Predictions Min -86.7251 +trainer/VF Loss 0.0429294 +expl/num steps total 349000 +expl/num paths total 396 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.122017 +expl/Actions Std 0.757646 +expl/Actions Max 2.38711 +expl/Actions Min -2.24526 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 339094 +eval/num paths total 349 +eval/path length Mean 643 +eval/path length Std 0 +eval/path length Max 643 +eval/path length Min 643 +eval/Rewards Mean 0.00155521 +eval/Rewards Std 0.0394055 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0368054 +eval/Actions Std 0.729574 +eval/Actions Max 0.998856 +eval/Actions Min -0.999686 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.20841e-06 +time/evaluation sampling (s) 2.49025 +time/exploration sampling (s) 2.67767 +time/logging (s) 0.0057817 +time/saving (s) 0.0094744 +time/training (s) 12.4196 +time/epoch (s) 17.6028 +time/total (s) 6831.1 +Epoch -652 +------------------------------ ---------------- +2022-05-15 19:56:35.004873 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -651 finished +------------------------------ ---------------- +epoch -651 +replay_buffer/size 999047 +trainer/num train calls 350000 +trainer/QF1 Loss 1.08477 +trainer/QF2 Loss 1.16891 +trainer/Policy Loss 59.7126 +trainer/Q1 Predictions Mean -72.012 +trainer/Q1 Predictions Std 19.5527 +trainer/Q1 Predictions Max -0.732939 +trainer/Q1 Predictions Min -87.8043 +trainer/Q2 Predictions Mean -71.9386 +trainer/Q2 Predictions Std 19.4968 +trainer/Q2 Predictions Max 0.637542 +trainer/Q2 Predictions Min -87.0806 +trainer/Q Targets Mean -72.496 +trainer/Q Targets Std 19.2607 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0614 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0233031 +trainer/policy/mean Std 0.712923 +trainer/policy/mean Max 0.996513 +trainer/policy/mean Min -0.996151 +trainer/policy/std Mean 0.424909 +trainer/policy/std Std 0.0212708 +trainer/policy/std Max 0.44611 +trainer/policy/std Min 0.388636 +trainer/Advantage Weights Mean 12.9694 +trainer/Advantage Weights Std 30.6885 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3423e-07 +trainer/Advantage Score Mean -0.0954771 +trainer/Advantage Score Std 0.55218 +trainer/Advantage Score Max 2.66341 +trainer/Advantage Score Min -1.58237 +trainer/V1 Predictions Mean -72.2308 +trainer/V1 Predictions Std 19.3685 +trainer/V1 Predictions Max -1.05491 +trainer/V1 Predictions Min -87.5019 +trainer/VF Loss 0.176048 +expl/num steps total 350000 +expl/num paths total 397 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0458663 +expl/Actions Std 0.791797 +expl/Actions Max 2.23675 +expl/Actions Min -2.21365 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 340094 +eval/num paths total 350 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0268001 +eval/Actions Std 0.698147 +eval/Actions Max 0.999561 +eval/Actions Min -0.998701 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95322e-06 +time/evaluation sampling (s) 2.4827 +time/exploration sampling (s) 2.67143 +time/logging (s) 0.00813977 +time/saving (s) 0.0129764 +time/training (s) 12.1867 +time/epoch (s) 17.3619 +time/total (s) 6848.47 +Epoch -651 +------------------------------ ---------------- +2022-05-15 19:56:52.907961 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -650 finished +------------------------------ ---------------- +epoch -650 +replay_buffer/size 999047 +trainer/num train calls 351000 +trainer/QF1 Loss 1.06635 +trainer/QF2 Loss 1.14454 +trainer/Policy Loss 11.9675 +trainer/Q1 Predictions Mean -72.2161 +trainer/Q1 Predictions Std 18.9562 +trainer/Q1 Predictions Max -1.03171 +trainer/Q1 Predictions Min -85.9247 +trainer/Q2 Predictions Mean -72.1832 +trainer/Q2 Predictions Std 18.9377 +trainer/Q2 Predictions Max -0.809263 +trainer/Q2 Predictions Min -85.8226 +trainer/Q Targets Mean -72.663 +trainer/Q Targets Std 19.1273 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3588 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00910454 +trainer/policy/mean Std 0.70857 +trainer/policy/mean Max 0.998687 +trainer/policy/mean Min -0.99933 +trainer/policy/std Mean 0.42353 +trainer/policy/std Std 0.0209172 +trainer/policy/std Max 0.447116 +trainer/policy/std Min 0.387349 +trainer/Advantage Weights Mean 3.32173 +trainer/Advantage Weights Std 14.048 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.88984e-17 +trainer/Advantage Score Mean -0.322645 +trainer/Advantage Score Std 0.601374 +trainer/Advantage Score Max 0.966641 +trainer/Advantage Score Min -3.6959 +trainer/V1 Predictions Mean -72.388 +trainer/V1 Predictions Std 19.1772 +trainer/V1 Predictions Max 0.343136 +trainer/V1 Predictions Min -86.2226 +trainer/VF Loss 0.0583909 +expl/num steps total 351000 +expl/num paths total 398 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.235548 +expl/Actions Std 0.849132 +expl/Actions Max 2.34658 +expl/Actions Min -2.55032 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 341094 +eval/num paths total 351 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.152832 +eval/Actions Std 0.801814 +eval/Actions Max 0.998588 +eval/Actions Min -0.99928 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.9318e-06 +time/evaluation sampling (s) 2.60648 +time/exploration sampling (s) 2.74808 +time/logging (s) 0.00726805 +time/saving (s) 0.0216607 +time/training (s) 12.5095 +time/epoch (s) 17.893 +time/total (s) 6866.37 +Epoch -650 +------------------------------ ---------------- +2022-05-15 19:57:10.334247 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -649 finished +------------------------------ ---------------- +epoch -649 +replay_buffer/size 999047 +trainer/num train calls 352000 +trainer/QF1 Loss 1.32092 +trainer/QF2 Loss 1.33141 +trainer/Policy Loss 4.01796 +trainer/Q1 Predictions Mean -73.8434 +trainer/Q1 Predictions Std 17.6195 +trainer/Q1 Predictions Max -0.568288 +trainer/Q1 Predictions Min -86.2012 +trainer/Q2 Predictions Mean -73.8964 +trainer/Q2 Predictions Std 17.5762 +trainer/Q2 Predictions Max -0.528858 +trainer/Q2 Predictions Min -86.8098 +trainer/Q Targets Mean -73.7606 +trainer/Q Targets Std 17.9011 +trainer/Q Targets Max 0.633063 +trainer/Q Targets Min -86.2453 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00849125 +trainer/policy/mean Std 0.721859 +trainer/policy/mean Max 0.999732 +trainer/policy/mean Min -0.998546 +trainer/policy/std Mean 0.426205 +trainer/policy/std Std 0.0199769 +trainer/policy/std Max 0.448541 +trainer/policy/std Min 0.389417 +trainer/Advantage Weights Mean 1.22708 +trainer/Advantage Weights Std 9.17501 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.77369e-19 +trainer/Advantage Score Mean -0.62714 +trainer/Advantage Score Std 0.648125 +trainer/Advantage Score Max 0.672173 +trainer/Advantage Score Min -4.27289 +trainer/V1 Predictions Mean -73.4735 +trainer/V1 Predictions Std 18.0554 +trainer/V1 Predictions Max 1.38325 +trainer/V1 Predictions Min -86.1455 +trainer/VF Loss 0.0848585 +expl/num steps total 352000 +expl/num paths total 400 +expl/path length Mean 500 +expl/path length Std 207 +expl/path length Max 707 +expl/path length Min 293 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.032764 +expl/Actions Std 0.830031 +expl/Actions Max 2.29379 +expl/Actions Min -2.48192 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 341807 +eval/num paths total 352 +eval/path length Mean 713 +eval/path length Std 0 +eval/path length Max 713 +eval/path length Min 713 +eval/Rewards Mean 0.00140252 +eval/Rewards Std 0.037424 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.025624 +eval/Actions Std 0.731248 +eval/Actions Max 0.999367 +eval/Actions Min -0.998626 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.03099e-06 +time/evaluation sampling (s) 2.61067 +time/exploration sampling (s) 2.69788 +time/logging (s) 0.00588534 +time/saving (s) 0.0156973 +time/training (s) 12.0857 +time/epoch (s) 17.4158 +time/total (s) 6883.79 +Epoch -649 +------------------------------ ---------------- +2022-05-15 19:57:27.696525 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -648 finished +------------------------------ ---------------- +epoch -648 +replay_buffer/size 999047 +trainer/num train calls 353000 +trainer/QF1 Loss 0.701742 +trainer/QF2 Loss 0.712328 +trainer/Policy Loss 21.2322 +trainer/Q1 Predictions Mean -72.0116 +trainer/Q1 Predictions Std 19.5654 +trainer/Q1 Predictions Max -0.0761791 +trainer/Q1 Predictions Min -89.3515 +trainer/Q2 Predictions Mean -71.9375 +trainer/Q2 Predictions Std 19.5434 +trainer/Q2 Predictions Max -0.456914 +trainer/Q2 Predictions Min -88.7826 +trainer/Q Targets Mean -71.7393 +trainer/Q Targets Std 19.9623 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4772 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0186249 +trainer/policy/mean Std 0.710885 +trainer/policy/mean Max 0.999019 +trainer/policy/mean Min -0.998694 +trainer/policy/std Mean 0.425887 +trainer/policy/std Std 0.0197513 +trainer/policy/std Max 0.446311 +trainer/policy/std Min 0.389295 +trainer/Advantage Weights Mean 4.55424 +trainer/Advantage Weights Std 17.0447 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.81116e-21 +trainer/Advantage Score Mean -0.402143 +trainer/Advantage Score Std 0.72093 +trainer/Advantage Score Max 1.43122 +trainer/Advantage Score Min -4.73207 +trainer/V1 Predictions Mean -71.5224 +trainer/V1 Predictions Std 20.1043 +trainer/V1 Predictions Max 1.21602 +trainer/V1 Predictions Min -88.3659 +trainer/VF Loss 0.0933982 +expl/num steps total 353000 +expl/num paths total 401 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.089263 +expl/Actions Std 0.905874 +expl/Actions Max 2.46416 +expl/Actions Min -2.38048 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 342807 +eval/num paths total 353 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0364674 +eval/Actions Std 0.729868 +eval/Actions Max 0.999825 +eval/Actions Min -0.999645 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.1211e-06 +time/evaluation sampling (s) 2.44534 +time/exploration sampling (s) 2.82494 +time/logging (s) 0.00712779 +time/saving (s) 0.0100421 +time/training (s) 12.069 +time/epoch (s) 17.3565 +time/total (s) 6901.15 +Epoch -648 +------------------------------ ---------------- +2022-05-15 19:57:46.037664 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -647 finished +------------------------------ ---------------- +epoch -647 +replay_buffer/size 999047 +trainer/num train calls 354000 +trainer/QF1 Loss 0.71096 +trainer/QF2 Loss 0.640939 +trainer/Policy Loss 16.5256 +trainer/Q1 Predictions Mean -73.6772 +trainer/Q1 Predictions Std 18.7011 +trainer/Q1 Predictions Max -0.200033 +trainer/Q1 Predictions Min -86.8665 +trainer/Q2 Predictions Mean -73.5767 +trainer/Q2 Predictions Std 18.7239 +trainer/Q2 Predictions Max -0.168544 +trainer/Q2 Predictions Min -86.9807 +trainer/Q Targets Mean -73.3602 +trainer/Q Targets Std 18.8059 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.486 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0213444 +trainer/policy/mean Std 0.730251 +trainer/policy/mean Max 0.999032 +trainer/policy/mean Min -0.997386 +trainer/policy/std Mean 0.425589 +trainer/policy/std Std 0.0206102 +trainer/policy/std Max 0.447554 +trainer/policy/std Min 0.389558 +trainer/Advantage Weights Mean 3.74767 +trainer/Advantage Weights Std 15.8074 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.83792e-15 +trainer/Advantage Score Mean -0.38792 +trainer/Advantage Score Std 0.614618 +trainer/Advantage Score Max 1.64814 +trainer/Advantage Score Min -3.34957 +trainer/V1 Predictions Mean -73.1149 +trainer/V1 Predictions Std 18.8931 +trainer/V1 Predictions Max 2.07166 +trainer/V1 Predictions Min -86.3996 +trainer/VF Loss 0.0730208 +expl/num steps total 354000 +expl/num paths total 402 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0045142 +expl/Actions Std 0.826369 +expl/Actions Max 2.57015 +expl/Actions Min -2.57878 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 343807 +eval/num paths total 354 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0236515 +eval/Actions Std 0.714107 +eval/Actions Max 0.99983 +eval/Actions Min -0.99978 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.01621e-05 +time/evaluation sampling (s) 2.59617 +time/exploration sampling (s) 2.81724 +time/logging (s) 0.00736723 +time/saving (s) 0.0107413 +time/training (s) 12.902 +time/epoch (s) 18.3335 +time/total (s) 6919.48 +Epoch -647 +------------------------------ ---------------- +2022-05-15 19:58:03.143851 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -646 finished +------------------------------ ---------------- +epoch -646 +replay_buffer/size 999047 +trainer/num train calls 355000 +trainer/QF1 Loss 0.484258 +trainer/QF2 Loss 0.44142 +trainer/Policy Loss 12.9011 +trainer/Q1 Predictions Mean -73.535 +trainer/Q1 Predictions Std 17.816 +trainer/Q1 Predictions Max -0.883572 +trainer/Q1 Predictions Min -86.9508 +trainer/Q2 Predictions Mean -73.5868 +trainer/Q2 Predictions Std 17.8139 +trainer/Q2 Predictions Max -0.74542 +trainer/Q2 Predictions Min -87.0252 +trainer/Q Targets Mean -73.401 +trainer/Q Targets Std 17.812 +trainer/Q Targets Max 0.234548 +trainer/Q Targets Min -86.9313 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0394879 +trainer/policy/mean Std 0.71824 +trainer/policy/mean Max 0.99952 +trainer/policy/mean Min -0.999221 +trainer/policy/std Mean 0.42537 +trainer/policy/std Std 0.0201763 +trainer/policy/std Max 0.445869 +trainer/policy/std Min 0.390721 +trainer/Advantage Weights Mean 2.49217 +trainer/Advantage Weights Std 12.9003 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.52682e-24 +trainer/Advantage Score Mean -0.38448 +trainer/Advantage Score Std 0.620245 +trainer/Advantage Score Max 1.03125 +trainer/Advantage Score Min -5.3752 +trainer/V1 Predictions Mean -73.0829 +trainer/V1 Predictions Std 18.086 +trainer/V1 Predictions Max 0.895812 +trainer/V1 Predictions Min -86.8929 +trainer/VF Loss 0.0628923 +expl/num steps total 355000 +expl/num paths total 403 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0230325 +expl/Actions Std 0.817209 +expl/Actions Max 2.17754 +expl/Actions Min -2.34858 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 344807 +eval/num paths total 355 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.223886 +eval/Actions Std 0.678085 +eval/Actions Max 0.999219 +eval/Actions Min -0.999849 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.4431e-06 +time/evaluation sampling (s) 2.51649 +time/exploration sampling (s) 2.78397 +time/logging (s) 0.00803134 +time/saving (s) 0.013046 +time/training (s) 11.7774 +time/epoch (s) 17.0989 +time/total (s) 6936.59 +Epoch -646 +------------------------------ ---------------- +2022-05-15 19:58:20.446754 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -645 finished +------------------------------ ---------------- +epoch -645 +replay_buffer/size 999047 +trainer/num train calls 356000 +trainer/QF1 Loss 0.64797 +trainer/QF2 Loss 0.712619 +trainer/Policy Loss 9.85988 +trainer/Q1 Predictions Mean -72.4315 +trainer/Q1 Predictions Std 18.6579 +trainer/Q1 Predictions Max -0.430839 +trainer/Q1 Predictions Min -88.1162 +trainer/Q2 Predictions Mean -72.4994 +trainer/Q2 Predictions Std 18.6734 +trainer/Q2 Predictions Max -0.732778 +trainer/Q2 Predictions Min -88.2092 +trainer/Q Targets Mean -72.4236 +trainer/Q Targets Std 18.7171 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.672 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00382752 +trainer/policy/mean Std 0.711939 +trainer/policy/mean Max 0.997476 +trainer/policy/mean Min -0.999607 +trainer/policy/std Mean 0.424169 +trainer/policy/std Std 0.0202547 +trainer/policy/std Max 0.445331 +trainer/policy/std Min 0.388349 +trainer/Advantage Weights Mean 2.65643 +trainer/Advantage Weights Std 13.197 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.4811e-19 +trainer/Advantage Score Mean -0.380206 +trainer/Advantage Score Std 0.53707 +trainer/Advantage Score Max 1.11507 +trainer/Advantage Score Min -4.18802 +trainer/V1 Predictions Mean -72.1086 +trainer/V1 Predictions Std 18.8131 +trainer/V1 Predictions Max 0.339884 +trainer/V1 Predictions Min -87.7444 +trainer/VF Loss 0.0526007 +expl/num steps total 356000 +expl/num paths total 404 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00638162 +expl/Actions Std 0.833766 +expl/Actions Max 2.4 +expl/Actions Min -2.39178 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 345807 +eval/num paths total 356 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0128508 +eval/Actions Std 0.765129 +eval/Actions Max 0.999285 +eval/Actions Min -0.999514 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83495e-06 +time/evaluation sampling (s) 2.4344 +time/exploration sampling (s) 2.8221 +time/logging (s) 0.00748036 +time/saving (s) 0.0129444 +time/training (s) 12.0161 +time/epoch (s) 17.293 +time/total (s) 6953.88 +Epoch -645 +------------------------------ ---------------- +2022-05-15 19:58:38.138280 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -644 finished +------------------------------ ---------------- +epoch -644 +replay_buffer/size 999047 +trainer/num train calls 357000 +trainer/QF1 Loss 0.715076 +trainer/QF2 Loss 0.856683 +trainer/Policy Loss 17.0109 +trainer/Q1 Predictions Mean -73.5315 +trainer/Q1 Predictions Std 17.8204 +trainer/Q1 Predictions Max -0.592314 +trainer/Q1 Predictions Min -86.6744 +trainer/Q2 Predictions Mean -73.4622 +trainer/Q2 Predictions Std 17.7981 +trainer/Q2 Predictions Max -0.574592 +trainer/Q2 Predictions Min -86.7899 +trainer/Q Targets Mean -73.8925 +trainer/Q Targets Std 17.7148 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4237 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0458689 +trainer/policy/mean Std 0.729136 +trainer/policy/mean Max 0.999189 +trainer/policy/mean Min -0.999676 +trainer/policy/std Mean 0.42628 +trainer/policy/std Std 0.0196093 +trainer/policy/std Max 0.449133 +trainer/policy/std Min 0.392518 +trainer/Advantage Weights Mean 3.26113 +trainer/Advantage Weights Std 15.5783 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.96029e-20 +trainer/Advantage Score Mean -0.446294 +trainer/Advantage Score Std 0.580878 +trainer/Advantage Score Max 1.09736 +trainer/Advantage Score Min -4.41115 +trainer/V1 Predictions Mean -73.6603 +trainer/V1 Predictions Std 17.8759 +trainer/V1 Predictions Max -0.465879 +trainer/V1 Predictions Min -86.3755 +trainer/VF Loss 0.0686108 +expl/num steps total 357000 +expl/num paths total 405 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0472332 +expl/Actions Std 0.775228 +expl/Actions Max 2.23354 +expl/Actions Min -2.40285 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 346807 +eval/num paths total 357 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0482839 +eval/Actions Std 0.729151 +eval/Actions Max 0.999874 +eval/Actions Min -0.999771 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.82918e-06 +time/evaluation sampling (s) 2.49336 +time/exploration sampling (s) 2.90614 +time/logging (s) 0.00920819 +time/saving (s) 0.0134556 +time/training (s) 12.262 +time/epoch (s) 17.6842 +time/total (s) 6971.57 +Epoch -644 +------------------------------ ---------------- +2022-05-15 19:58:55.746648 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -643 finished +------------------------------ ---------------- +epoch -643 +replay_buffer/size 999047 +trainer/num train calls 358000 +trainer/QF1 Loss 0.920504 +trainer/QF2 Loss 0.883537 +trainer/Policy Loss 27.8648 +trainer/Q1 Predictions Mean -72.5178 +trainer/Q1 Predictions Std 20.1945 +trainer/Q1 Predictions Max -1.01668 +trainer/Q1 Predictions Min -87.6216 +trainer/Q2 Predictions Mean -72.5489 +trainer/Q2 Predictions Std 20.1757 +trainer/Q2 Predictions Max -0.986528 +trainer/Q2 Predictions Min -87.5946 +trainer/Q Targets Mean -72.6416 +trainer/Q Targets Std 20.0798 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2052 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0216929 +trainer/policy/mean Std 0.719207 +trainer/policy/mean Max 0.997671 +trainer/policy/mean Min -0.998241 +trainer/policy/std Mean 0.424904 +trainer/policy/std Std 0.0215364 +trainer/policy/std Max 0.450079 +trainer/policy/std Min 0.387533 +trainer/Advantage Weights Mean 7.66587 +trainer/Advantage Weights Std 23.9255 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.0443e-16 +trainer/Advantage Score Mean -0.326914 +trainer/Advantage Score Std 0.618662 +trainer/Advantage Score Max 1.26507 +trainer/Advantage Score Min -3.6798 +trainer/V1 Predictions Mean -72.4118 +trainer/V1 Predictions Std 20.0382 +trainer/V1 Predictions Max 0.575693 +trainer/V1 Predictions Min -87.0893 +trainer/VF Loss 0.0805219 +expl/num steps total 358000 +expl/num paths total 406 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0689455 +expl/Actions Std 0.844101 +expl/Actions Max 2.21189 +expl/Actions Min -2.37515 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 347807 +eval/num paths total 358 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0109811 +eval/Actions Std 0.684905 +eval/Actions Max 0.999305 +eval/Actions Min -0.999743 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66777e-06 +time/evaluation sampling (s) 2.5641 +time/exploration sampling (s) 2.91632 +time/logging (s) 0.00738454 +time/saving (s) 0.0112606 +time/training (s) 12.0984 +time/epoch (s) 17.5975 +time/total (s) 6989.18 +Epoch -643 +------------------------------ ---------------- +2022-05-15 19:59:13.489974 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -642 finished +------------------------------ ---------------- +epoch -642 +replay_buffer/size 999047 +trainer/num train calls 359000 +trainer/QF1 Loss 0.52727 +trainer/QF2 Loss 0.645476 +trainer/Policy Loss 16.9744 +trainer/Q1 Predictions Mean -73.792 +trainer/Q1 Predictions Std 19.6631 +trainer/Q1 Predictions Max -0.696654 +trainer/Q1 Predictions Min -86.2277 +trainer/Q2 Predictions Mean -73.7852 +trainer/Q2 Predictions Std 19.7188 +trainer/Q2 Predictions Max 0.00977761 +trainer/Q2 Predictions Min -86.4346 +trainer/Q Targets Mean -73.83 +trainer/Q Targets Std 19.4132 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5348 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00588819 +trainer/policy/mean Std 0.7141 +trainer/policy/mean Max 0.99968 +trainer/policy/mean Min -0.997634 +trainer/policy/std Mean 0.424889 +trainer/policy/std Std 0.0212877 +trainer/policy/std Max 0.450342 +trainer/policy/std Min 0.387136 +trainer/Advantage Weights Mean 3.04519 +trainer/Advantage Weights Std 14.7221 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.4491e-13 +trainer/Advantage Score Mean -0.332774 +trainer/Advantage Score Std 0.425616 +trainer/Advantage Score Max 0.902565 +trainer/Advantage Score Min -2.95627 +trainer/V1 Predictions Mean -73.5481 +trainer/V1 Predictions Std 19.5713 +trainer/V1 Predictions Max 0.0423526 +trainer/V1 Predictions Min -86.4835 +trainer/VF Loss 0.0396147 +expl/num steps total 359000 +expl/num paths total 408 +expl/path length Mean 500 +expl/path length Std 408 +expl/path length Max 908 +expl/path length Min 92 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0193458 +expl/Actions Std 0.830016 +expl/Actions Max 2.42158 +expl/Actions Min -2.30105 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 348807 +eval/num paths total 359 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0287871 +eval/Actions Std 0.72317 +eval/Actions Max 0.999783 +eval/Actions Min -0.998833 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.24473e-06 +time/evaluation sampling (s) 2.47872 +time/exploration sampling (s) 2.81326 +time/logging (s) 0.00743015 +time/saving (s) 0.0120087 +time/training (s) 12.4236 +time/epoch (s) 17.7351 +time/total (s) 7006.91 +Epoch -642 +------------------------------ ---------------- +2022-05-15 19:59:30.531892 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -641 finished +------------------------------ ---------------- +epoch -641 +replay_buffer/size 999047 +trainer/num train calls 360000 +trainer/QF1 Loss 1.11697 +trainer/QF2 Loss 1.29289 +trainer/Policy Loss 10.9211 +trainer/Q1 Predictions Mean -71.4414 +trainer/Q1 Predictions Std 20.9687 +trainer/Q1 Predictions Max -0.130397 +trainer/Q1 Predictions Min -86.2925 +trainer/Q2 Predictions Mean -71.5197 +trainer/Q2 Predictions Std 20.8923 +trainer/Q2 Predictions Max -0.327383 +trainer/Q2 Predictions Min -86.3095 +trainer/Q Targets Mean -71.3068 +trainer/Q Targets Std 20.9777 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0056 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0103231 +trainer/policy/mean Std 0.713129 +trainer/policy/mean Max 0.996553 +trainer/policy/mean Min -0.998885 +trainer/policy/std Mean 0.423901 +trainer/policy/std Std 0.0203119 +trainer/policy/std Max 0.45111 +trainer/policy/std Min 0.390186 +trainer/Advantage Weights Mean 2.11075 +trainer/Advantage Weights Std 12.8476 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.60703e-15 +trainer/Advantage Score Mean -0.495914 +trainer/Advantage Score Std 0.597822 +trainer/Advantage Score Max 1.01995 +trainer/Advantage Score Min -3.40644 +trainer/V1 Predictions Mean -70.9916 +trainer/V1 Predictions Std 21.1908 +trainer/V1 Predictions Max 1.73156 +trainer/V1 Predictions Min -85.8635 +trainer/VF Loss 0.067997 +expl/num steps total 360000 +expl/num paths total 410 +expl/path length Mean 500 +expl/path length Std 365 +expl/path length Max 865 +expl/path length Min 135 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00593929 +expl/Actions Std 0.796533 +expl/Actions Max 2.43613 +expl/Actions Min -2.4295 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 349807 +eval/num paths total 360 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00281666 +eval/Actions Std 0.760999 +eval/Actions Max 0.999019 +eval/Actions Min -0.999359 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02851e-05 +time/evaluation sampling (s) 2.58187 +time/exploration sampling (s) 2.72051 +time/logging (s) 0.00705134 +time/saving (s) 0.0100679 +time/training (s) 11.7131 +time/epoch (s) 17.0326 +time/total (s) 7023.95 +Epoch -641 +------------------------------ ---------------- +2022-05-15 19:59:47.043365 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -640 finished +------------------------------ ---------------- +epoch -640 +replay_buffer/size 999047 +trainer/num train calls 361000 +trainer/QF1 Loss 0.537732 +trainer/QF2 Loss 0.55503 +trainer/Policy Loss 20.2225 +trainer/Q1 Predictions Mean -74.128 +trainer/Q1 Predictions Std 18.0431 +trainer/Q1 Predictions Max -0.309193 +trainer/Q1 Predictions Min -87.121 +trainer/Q2 Predictions Mean -74.093 +trainer/Q2 Predictions Std 17.9927 +trainer/Q2 Predictions Max -0.263379 +trainer/Q2 Predictions Min -87.093 +trainer/Q Targets Mean -74.2518 +trainer/Q Targets Std 18.1927 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7241 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00874723 +trainer/policy/mean Std 0.721612 +trainer/policy/mean Max 0.998744 +trainer/policy/mean Min -0.997484 +trainer/policy/std Mean 0.426734 +trainer/policy/std Std 0.021046 +trainer/policy/std Max 0.449179 +trainer/policy/std Min 0.393136 +trainer/Advantage Weights Mean 5.80509 +trainer/Advantage Weights Std 19.6396 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.36449e-10 +trainer/Advantage Score Mean -0.161228 +trainer/Advantage Score Std 0.435427 +trainer/Advantage Score Max 2.11794 +trainer/Advantage Score Min -2.11751 +trainer/V1 Predictions Mean -74.0858 +trainer/V1 Predictions Std 18.1031 +trainer/V1 Predictions Max 0.091765 +trainer/V1 Predictions Min -87.2645 +trainer/VF Loss 0.0680085 +expl/num steps total 361000 +expl/num paths total 411 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0495066 +expl/Actions Std 0.814211 +expl/Actions Max 2.18254 +expl/Actions Min -2.36986 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 350545 +eval/num paths total 361 +eval/path length Mean 738 +eval/path length Std 0 +eval/path length Max 738 +eval/path length Min 738 +eval/Rewards Mean 0.00135501 +eval/Rewards Std 0.0367856 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0532843 +eval/Actions Std 0.70987 +eval/Actions Max 0.998705 +eval/Actions Min -0.999179 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.80375e-06 +time/evaluation sampling (s) 2.44056 +time/exploration sampling (s) 2.66254 +time/logging (s) 0.00702043 +time/saving (s) 0.0147193 +time/training (s) 11.3791 +time/epoch (s) 16.504 +time/total (s) 7040.46 +Epoch -640 +------------------------------ ---------------- +2022-05-15 20:00:03.440688 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -639 finished +------------------------------ ---------------- +epoch -639 +replay_buffer/size 999047 +trainer/num train calls 362000 +trainer/QF1 Loss 0.938376 +trainer/QF2 Loss 0.872639 +trainer/Policy Loss 10.1193 +trainer/Q1 Predictions Mean -74.5581 +trainer/Q1 Predictions Std 16.1503 +trainer/Q1 Predictions Max -2.58016 +trainer/Q1 Predictions Min -87.9504 +trainer/Q2 Predictions Mean -74.5572 +trainer/Q2 Predictions Std 16.2063 +trainer/Q2 Predictions Max -2.29571 +trainer/Q2 Predictions Min -87.6271 +trainer/Q Targets Mean -74.142 +trainer/Q Targets Std 16.2955 +trainer/Q Targets Max -2.99393 +trainer/Q Targets Min -87.3449 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00419327 +trainer/policy/mean Std 0.727255 +trainer/policy/mean Max 0.996994 +trainer/policy/mean Min -0.999616 +trainer/policy/std Mean 0.426683 +trainer/policy/std Std 0.0195461 +trainer/policy/std Max 0.444988 +trainer/policy/std Min 0.394368 +trainer/Advantage Weights Mean 2.09727 +trainer/Advantage Weights Std 9.89352 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.18917e-14 +trainer/Advantage Score Mean -0.334993 +trainer/Advantage Score Std 0.46565 +trainer/Advantage Score Max 1.25025 +trainer/Advantage Score Min -3.10764 +trainer/V1 Predictions Mean -73.8978 +trainer/V1 Predictions Std 16.2819 +trainer/V1 Predictions Max -1.71805 +trainer/V1 Predictions Min -87.0888 +trainer/VF Loss 0.044912 +expl/num steps total 362000 +expl/num paths total 412 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0342862 +expl/Actions Std 0.817478 +expl/Actions Max 2.36248 +expl/Actions Min -2.36591 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 351449 +eval/num paths total 362 +eval/path length Mean 904 +eval/path length Std 0 +eval/path length Max 904 +eval/path length Min 904 +eval/Rewards Mean 0.00110619 +eval/Rewards Std 0.0332411 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0365641 +eval/Actions Std 0.737339 +eval/Actions Max 0.999321 +eval/Actions Min -0.999412 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.10782e-06 +time/evaluation sampling (s) 2.45843 +time/exploration sampling (s) 2.63726 +time/logging (s) 0.00633969 +time/saving (s) 0.00920674 +time/training (s) 11.2752 +time/epoch (s) 16.3864 +time/total (s) 7056.85 +Epoch -639 +------------------------------ ---------------- +2022-05-15 20:00:19.949290 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -638 finished +------------------------------ ---------------- +epoch -638 +replay_buffer/size 999047 +trainer/num train calls 363000 +trainer/QF1 Loss 0.657207 +trainer/QF2 Loss 0.811878 +trainer/Policy Loss 22.2934 +trainer/Q1 Predictions Mean -73.8385 +trainer/Q1 Predictions Std 17.8105 +trainer/Q1 Predictions Max -0.6251 +trainer/Q1 Predictions Min -86.9967 +trainer/Q2 Predictions Mean -74.0522 +trainer/Q2 Predictions Std 17.8221 +trainer/Q2 Predictions Max -0.741209 +trainer/Q2 Predictions Min -87.1923 +trainer/Q Targets Mean -73.8631 +trainer/Q Targets Std 17.745 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2565 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00164448 +trainer/policy/mean Std 0.718637 +trainer/policy/mean Max 0.996394 +trainer/policy/mean Min -0.996632 +trainer/policy/std Mean 0.427192 +trainer/policy/std Std 0.0202046 +trainer/policy/std Max 0.446967 +trainer/policy/std Min 0.396336 +trainer/Advantage Weights Mean 6.4251 +trainer/Advantage Weights Std 21.3743 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.13919e-20 +trainer/Advantage Score Mean -0.17844 +trainer/Advantage Score Std 0.537581 +trainer/Advantage Score Max 1.88336 +trainer/Advantage Score Min -4.44148 +trainer/V1 Predictions Mean -73.7036 +trainer/V1 Predictions Std 17.6624 +trainer/V1 Predictions Max -1.16632 +trainer/V1 Predictions Min -87.1303 +trainer/VF Loss 0.0678266 +expl/num steps total 363000 +expl/num paths total 413 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0569889 +expl/Actions Std 0.826871 +expl/Actions Max 2.75415 +expl/Actions Min -2.47692 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 352449 +eval/num paths total 363 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.132276 +eval/Actions Std 0.700347 +eval/Actions Max 0.999112 +eval/Actions Min -0.998148 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.08501e-06 +time/evaluation sampling (s) 2.45981 +time/exploration sampling (s) 2.60856 +time/logging (s) 0.0066398 +time/saving (s) 0.0105477 +time/training (s) 11.4164 +time/epoch (s) 16.5019 +time/total (s) 7073.36 +Epoch -638 +------------------------------ ---------------- +2022-05-15 20:00:35.527243 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -637 finished +------------------------------ ---------------- +epoch -637 +replay_buffer/size 999047 +trainer/num train calls 364000 +trainer/QF1 Loss 0.903904 +trainer/QF2 Loss 0.96565 +trainer/Policy Loss 13.3565 +trainer/Q1 Predictions Mean -73.1903 +trainer/Q1 Predictions Std 18.671 +trainer/Q1 Predictions Max -2.39481 +trainer/Q1 Predictions Min -85.9965 +trainer/Q2 Predictions Mean -73.2249 +trainer/Q2 Predictions Std 18.5738 +trainer/Q2 Predictions Max -2.12405 +trainer/Q2 Predictions Min -86.1008 +trainer/Q Targets Mean -73.2264 +trainer/Q Targets Std 18.6818 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1218 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0439973 +trainer/policy/mean Std 0.703018 +trainer/policy/mean Max 0.996914 +trainer/policy/mean Min -0.997302 +trainer/policy/std Mean 0.427294 +trainer/policy/std Std 0.0206755 +trainer/policy/std Max 0.447168 +trainer/policy/std Min 0.391495 +trainer/Advantage Weights Mean 3.46536 +trainer/Advantage Weights Std 15.8456 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.97799e-15 +trainer/Advantage Score Mean -0.420755 +trainer/Advantage Score Std 0.515636 +trainer/Advantage Score Max 1.30884 +trainer/Advantage Score Min -3.2596 +trainer/V1 Predictions Mean -73.0117 +trainer/V1 Predictions Std 18.6552 +trainer/V1 Predictions Max -1.98824 +trainer/V1 Predictions Min -86.0173 +trainer/VF Loss 0.0624878 +expl/num steps total 364000 +expl/num paths total 414 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.226635 +expl/Actions Std 0.881079 +expl/Actions Max 2.34668 +expl/Actions Min -2.41933 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 353152 +eval/num paths total 364 +eval/path length Mean 703 +eval/path length Std 0 +eval/path length Max 703 +eval/path length Min 703 +eval/Rewards Mean 0.00142248 +eval/Rewards Std 0.0376889 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0344561 +eval/Actions Std 0.716039 +eval/Actions Max 0.999126 +eval/Actions Min -0.999387 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.64682e-06 +time/evaluation sampling (s) 2.4142 +time/exploration sampling (s) 2.65381 +time/logging (s) 0.00642203 +time/saving (s) 0.0113894 +time/training (s) 10.4847 +time/epoch (s) 15.5706 +time/total (s) 7088.93 +Epoch -637 +------------------------------ ---------------- +2022-05-15 20:00:51.010717 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -636 finished +------------------------------ ---------------- +epoch -636 +replay_buffer/size 999047 +trainer/num train calls 365000 +trainer/QF1 Loss 1.44734 +trainer/QF2 Loss 1.20122 +trainer/Policy Loss 19.9669 +trainer/Q1 Predictions Mean -73.0564 +trainer/Q1 Predictions Std 19.7305 +trainer/Q1 Predictions Max -1.28612 +trainer/Q1 Predictions Min -86.4531 +trainer/Q2 Predictions Mean -72.8667 +trainer/Q2 Predictions Std 19.7059 +trainer/Q2 Predictions Max -0.765863 +trainer/Q2 Predictions Min -86.6172 +trainer/Q Targets Mean -72.6051 +trainer/Q Targets Std 19.6816 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.586 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00397826 +trainer/policy/mean Std 0.714334 +trainer/policy/mean Max 0.999165 +trainer/policy/mean Min -0.999788 +trainer/policy/std Mean 0.425993 +trainer/policy/std Std 0.0203262 +trainer/policy/std Max 0.451684 +trainer/policy/std Min 0.392259 +trainer/Advantage Weights Mean 5.35705 +trainer/Advantage Weights Std 21.2614 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.30154e-18 +trainer/Advantage Score Mean -0.489749 +trainer/Advantage Score Std 0.684408 +trainer/Advantage Score Max 2.69431 +trainer/Advantage Score Min -4.0613 +trainer/V1 Predictions Mean -72.4159 +trainer/V1 Predictions Std 19.6429 +trainer/V1 Predictions Max -0.475712 +trainer/V1 Predictions Min -86.4376 +trainer/VF Loss 0.125656 +expl/num steps total 365000 +expl/num paths total 416 +expl/path length Mean 500 +expl/path length Std 370 +expl/path length Max 870 +expl/path length Min 130 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0461172 +expl/Actions Std 0.829952 +expl/Actions Max 2.23996 +expl/Actions Min -2.35334 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 354152 +eval/num paths total 365 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0298852 +eval/Actions Std 0.734167 +eval/Actions Max 0.999514 +eval/Actions Min -0.999658 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.39979e-06 +time/evaluation sampling (s) 2.47169 +time/exploration sampling (s) 2.61743 +time/logging (s) 0.00662599 +time/saving (s) 0.00925918 +time/training (s) 10.3716 +time/epoch (s) 15.4766 +time/total (s) 7104.41 +Epoch -636 +------------------------------ ---------------- +2022-05-15 20:01:06.808981 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -635 finished +------------------------------ ---------------- +epoch -635 +replay_buffer/size 999047 +trainer/num train calls 366000 +trainer/QF1 Loss 1.26495 +trainer/QF2 Loss 1.28487 +trainer/Policy Loss 13.728 +trainer/Q1 Predictions Mean -74.0775 +trainer/Q1 Predictions Std 18.3212 +trainer/Q1 Predictions Max -0.411047 +trainer/Q1 Predictions Min -87.0234 +trainer/Q2 Predictions Mean -74.1134 +trainer/Q2 Predictions Std 18.3805 +trainer/Q2 Predictions Max 0.493368 +trainer/Q2 Predictions Min -87.4281 +trainer/Q Targets Mean -73.9578 +trainer/Q Targets Std 18.4565 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9086 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00594805 +trainer/policy/mean Std 0.72041 +trainer/policy/mean Max 0.99755 +trainer/policy/mean Min -0.99957 +trainer/policy/std Mean 0.426079 +trainer/policy/std Std 0.0203641 +trainer/policy/std Max 0.453087 +trainer/policy/std Min 0.395165 +trainer/Advantage Weights Mean 3.52102 +trainer/Advantage Weights Std 14.3813 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.78489e-15 +trainer/Advantage Score Mean -0.359836 +trainer/Advantage Score Std 0.524365 +trainer/Advantage Score Max 1.1103 +trainer/Advantage Score Min -3.27835 +trainer/V1 Predictions Mean -73.7772 +trainer/V1 Predictions Std 18.3701 +trainer/V1 Predictions Max 0.453197 +trainer/V1 Predictions Min -86.8509 +trainer/VF Loss 0.0554755 +expl/num steps total 366000 +expl/num paths total 417 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.171408 +expl/Actions Std 0.84638 +expl/Actions Max 2.27846 +expl/Actions Min -2.44838 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 355152 +eval/num paths total 366 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.143505 +eval/Actions Std 0.673642 +eval/Actions Max 0.999886 +eval/Actions Min -0.997985 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.26987e-06 +time/evaluation sampling (s) 2.52004 +time/exploration sampling (s) 2.91778 +time/logging (s) 0.00651564 +time/saving (s) 0.00917006 +time/training (s) 10.3378 +time/epoch (s) 15.7913 +time/total (s) 7120.21 +Epoch -635 +------------------------------ ---------------- +2022-05-15 20:01:22.524979 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -634 finished +------------------------------ ---------------- +epoch -634 +replay_buffer/size 999047 +trainer/num train calls 367000 +trainer/QF1 Loss 1.0922 +trainer/QF2 Loss 1.06261 +trainer/Policy Loss 18.1473 +trainer/Q1 Predictions Mean -74.0459 +trainer/Q1 Predictions Std 16.729 +trainer/Q1 Predictions Max -1.56631 +trainer/Q1 Predictions Min -87.0516 +trainer/Q2 Predictions Mean -74.0211 +trainer/Q2 Predictions Std 16.7404 +trainer/Q2 Predictions Max -1.6963 +trainer/Q2 Predictions Min -86.877 +trainer/Q Targets Mean -73.4617 +trainer/Q Targets Std 16.5544 +trainer/Q Targets Max -1.63451 +trainer/Q Targets Min -86.4216 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0067409 +trainer/policy/mean Std 0.713481 +trainer/policy/mean Max 0.998544 +trainer/policy/mean Min -0.997939 +trainer/policy/std Mean 0.425575 +trainer/policy/std Std 0.0204065 +trainer/policy/std Max 0.449693 +trainer/policy/std Min 0.392503 +trainer/Advantage Weights Mean 2.99522 +trainer/Advantage Weights Std 15.13 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.73236e-20 +trainer/Advantage Score Mean -0.37821 +trainer/Advantage Score Std 0.583328 +trainer/Advantage Score Max 0.731861 +trainer/Advantage Score Min -4.37762 +trainer/V1 Predictions Mean -73.1146 +trainer/V1 Predictions Std 16.8933 +trainer/V1 Predictions Max -0.687003 +trainer/V1 Predictions Min -86.3249 +trainer/VF Loss 0.0568471 +expl/num steps total 367000 +expl/num paths total 419 +expl/path length Mean 500 +expl/path length Std 375 +expl/path length Max 875 +expl/path length Min 125 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0235345 +expl/Actions Std 0.823757 +expl/Actions Max 2.59358 +expl/Actions Min -2.18474 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 356152 +eval/num paths total 367 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.185922 +eval/Actions Std 0.561957 +eval/Actions Max 0.997051 +eval/Actions Min -0.997055 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.44589e-06 +time/evaluation sampling (s) 2.47375 +time/exploration sampling (s) 2.8373 +time/logging (s) 0.00664546 +time/saving (s) 0.00936643 +time/training (s) 10.3819 +time/epoch (s) 15.7089 +time/total (s) 7135.92 +Epoch -634 +------------------------------ ---------------- +2022-05-15 20:01:38.202357 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -633 finished +------------------------------ ---------------- +epoch -633 +replay_buffer/size 999047 +trainer/num train calls 368000 +trainer/QF1 Loss 1.76783 +trainer/QF2 Loss 1.82817 +trainer/Policy Loss 24.7441 +trainer/Q1 Predictions Mean -73.5511 +trainer/Q1 Predictions Std 18.0805 +trainer/Q1 Predictions Max -0.650583 +trainer/Q1 Predictions Min -86.9786 +trainer/Q2 Predictions Mean -73.5346 +trainer/Q2 Predictions Std 18.0789 +trainer/Q2 Predictions Max -1.11924 +trainer/Q2 Predictions Min -86.8615 +trainer/Q Targets Mean -74.0346 +trainer/Q Targets Std 17.4719 +trainer/Q Targets Max -3.23686 +trainer/Q Targets Min -86.9562 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0104606 +trainer/policy/mean Std 0.700319 +trainer/policy/mean Max 0.9989 +trainer/policy/mean Min -0.998894 +trainer/policy/std Mean 0.426933 +trainer/policy/std Std 0.0191994 +trainer/policy/std Max 0.44925 +trainer/policy/std Min 0.397196 +trainer/Advantage Weights Mean 6.13059 +trainer/Advantage Weights Std 21.7818 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.64601e-09 +trainer/Advantage Score Mean -0.315711 +trainer/Advantage Score Std 0.492592 +trainer/Advantage Score Max 1.64736 +trainer/Advantage Score Min -2.02249 +trainer/V1 Predictions Mean -73.7475 +trainer/V1 Predictions Std 17.6959 +trainer/V1 Predictions Max -2.22973 +trainer/V1 Predictions Min -86.9366 +trainer/VF Loss 0.071038 +expl/num steps total 368000 +expl/num paths total 420 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0212512 +expl/Actions Std 0.798461 +expl/Actions Max 2.39773 +expl/Actions Min -2.27195 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 356728 +eval/num paths total 368 +eval/path length Mean 576 +eval/path length Std 0 +eval/path length Max 576 +eval/path length Min 576 +eval/Rewards Mean 0.00173611 +eval/Rewards Std 0.0416305 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0209174 +eval/Actions Std 0.729082 +eval/Actions Max 0.999432 +eval/Actions Min -0.998958 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.59373e-06 +time/evaluation sampling (s) 2.35792 +time/exploration sampling (s) 2.90989 +time/logging (s) 0.00601723 +time/saving (s) 0.0101843 +time/training (s) 10.3859 +time/epoch (s) 15.6699 +time/total (s) 7151.59 +Epoch -633 +------------------------------ ---------------- +2022-05-15 20:01:53.792575 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -632 finished +------------------------------ ---------------- +epoch -632 +replay_buffer/size 999047 +trainer/num train calls 369000 +trainer/QF1 Loss 0.971327 +trainer/QF2 Loss 0.95561 +trainer/Policy Loss 16.064 +trainer/Q1 Predictions Mean -72.8447 +trainer/Q1 Predictions Std 17.1103 +trainer/Q1 Predictions Max -1.88295 +trainer/Q1 Predictions Min -85.7785 +trainer/Q2 Predictions Mean -72.8176 +trainer/Q2 Predictions Std 17.1208 +trainer/Q2 Predictions Max -2.2474 +trainer/Q2 Predictions Min -85.8957 +trainer/Q Targets Mean -73.1358 +trainer/Q Targets Std 17.511 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6421 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.013382 +trainer/policy/mean Std 0.705232 +trainer/policy/mean Max 0.999458 +trainer/policy/mean Min -0.998303 +trainer/policy/std Mean 0.42598 +trainer/policy/std Std 0.0187225 +trainer/policy/std Max 0.446966 +trainer/policy/std Min 0.395612 +trainer/Advantage Weights Mean 3.61013 +trainer/Advantage Weights Std 16.7002 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.69665e-21 +trainer/Advantage Score Mean -0.446342 +trainer/Advantage Score Std 0.641848 +trainer/Advantage Score Max 1.96346 +trainer/Advantage Score Min -4.70469 +trainer/V1 Predictions Mean -72.8772 +trainer/V1 Predictions Std 17.5185 +trainer/V1 Predictions Max -2.16178 +trainer/V1 Predictions Min -86.716 +trainer/VF Loss 0.0908292 +expl/num steps total 369000 +expl/num paths total 422 +expl/path length Mean 500 +expl/path length Std 335 +expl/path length Max 835 +expl/path length Min 165 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0172169 +expl/Actions Std 0.80642 +expl/Actions Max 2.19291 +expl/Actions Min -2.4653 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 357728 +eval/num paths total 369 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0358489 +eval/Actions Std 0.716018 +eval/Actions Max 0.999775 +eval/Actions Min -0.999152 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.36394e-06 +time/evaluation sampling (s) 2.31633 +time/exploration sampling (s) 2.94237 +time/logging (s) 0.00650945 +time/saving (s) 0.0104822 +time/training (s) 10.308 +time/epoch (s) 15.5837 +time/total (s) 7167.18 +Epoch -632 +------------------------------ ---------------- +2022-05-15 20:02:09.414327 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -631 finished +------------------------------ ---------------- +epoch -631 +replay_buffer/size 999047 +trainer/num train calls 370000 +trainer/QF1 Loss 2.03364 +trainer/QF2 Loss 1.9434 +trainer/Policy Loss 36.315 +trainer/Q1 Predictions Mean -72.268 +trainer/Q1 Predictions Std 19.8707 +trainer/Q1 Predictions Max -1.413 +trainer/Q1 Predictions Min -87.1858 +trainer/Q2 Predictions Mean -72.2971 +trainer/Q2 Predictions Std 19.8035 +trainer/Q2 Predictions Max -1.2334 +trainer/Q2 Predictions Min -87.1883 +trainer/Q Targets Mean -72.4408 +trainer/Q Targets Std 19.9745 +trainer/Q Targets Max 2.00349 +trainer/Q Targets Min -87.2374 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00316342 +trainer/policy/mean Std 0.699396 +trainer/policy/mean Max 0.998558 +trainer/policy/mean Min -0.996819 +trainer/policy/std Mean 0.425178 +trainer/policy/std Std 0.0199952 +trainer/policy/std Max 0.444837 +trainer/policy/std Min 0.38712 +trainer/Advantage Weights Mean 10.484 +trainer/Advantage Weights Std 25.2376 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.94227e-18 +trainer/Advantage Score Mean -0.177448 +trainer/Advantage Score Std 0.573822 +trainer/Advantage Score Max 1.0475 +trainer/Advantage Score Min -3.96644 +trainer/V1 Predictions Mean -72.2703 +trainer/V1 Predictions Std 19.9212 +trainer/V1 Predictions Max 1.06831 +trainer/V1 Predictions Min -86.9049 +trainer/VF Loss 0.0729718 +expl/num steps total 370000 +expl/num paths total 424 +expl/path length Mean 500 +expl/path length Std 387 +expl/path length Max 887 +expl/path length Min 113 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0243267 +expl/Actions Std 0.789238 +expl/Actions Max 2.23014 +expl/Actions Min -2.37027 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 358378 +eval/num paths total 370 +eval/path length Mean 650 +eval/path length Std 0 +eval/path length Max 650 +eval/path length Min 650 +eval/Rewards Mean 0.00153846 +eval/Rewards Std 0.039193 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0146322 +eval/Actions Std 0.729709 +eval/Actions Max 0.998696 +eval/Actions Min -0.998096 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.99606e-06 +time/evaluation sampling (s) 2.28509 +time/exploration sampling (s) 2.91018 +time/logging (s) 0.00564458 +time/saving (s) 0.00934025 +time/training (s) 10.4035 +time/epoch (s) 15.6137 +time/total (s) 7182.8 +Epoch -631 +------------------------------ ---------------- +2022-05-15 20:02:26.720100 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -630 finished +------------------------------ ---------------- +epoch -630 +replay_buffer/size 999047 +trainer/num train calls 371000 +trainer/QF1 Loss 0.74961 +trainer/QF2 Loss 0.720003 +trainer/Policy Loss 17.6825 +trainer/Q1 Predictions Mean -74.4469 +trainer/Q1 Predictions Std 15.9354 +trainer/Q1 Predictions Max -1.61319 +trainer/Q1 Predictions Min -86.719 +trainer/Q2 Predictions Mean -74.4757 +trainer/Q2 Predictions Std 15.9685 +trainer/Q2 Predictions Max -1.56406 +trainer/Q2 Predictions Min -86.7281 +trainer/Q Targets Mean -74.4201 +trainer/Q Targets Std 16.0203 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4238 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00289616 +trainer/policy/mean Std 0.715025 +trainer/policy/mean Max 0.997792 +trainer/policy/mean Min -0.998569 +trainer/policy/std Mean 0.425386 +trainer/policy/std Std 0.0204068 +trainer/policy/std Max 0.448617 +trainer/policy/std Min 0.390869 +trainer/Advantage Weights Mean 3.70417 +trainer/Advantage Weights Std 16.4452 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.46664e-14 +trainer/Advantage Score Mean -0.356177 +trainer/Advantage Score Std 0.472305 +trainer/Advantage Score Max 0.975573 +trainer/Advantage Score Min -3.18532 +trainer/V1 Predictions Mean -74.2251 +trainer/V1 Predictions Std 16.0097 +trainer/V1 Predictions Max -0.958875 +trainer/V1 Predictions Min -86.27 +trainer/VF Loss 0.0484751 +expl/num steps total 371000 +expl/num paths total 425 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0244646 +expl/Actions Std 0.811544 +expl/Actions Max 2.34067 +expl/Actions Min -2.37191 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 359378 +eval/num paths total 371 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.431883 +eval/Actions Std 0.673845 +eval/Actions Max 0.998507 +eval/Actions Min -0.998519 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.24892e-06 +time/evaluation sampling (s) 2.35762 +time/exploration sampling (s) 2.85566 +time/logging (s) 0.00671155 +time/saving (s) 0.00972542 +time/training (s) 12.0702 +time/epoch (s) 17.2999 +time/total (s) 7200.1 +Epoch -630 +------------------------------ ---------------- +2022-05-15 20:02:43.685430 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -629 finished +------------------------------ ---------------- +epoch -629 +replay_buffer/size 999047 +trainer/num train calls 372000 +trainer/QF1 Loss 0.87676 +trainer/QF2 Loss 0.847407 +trainer/Policy Loss 7.01013 +trainer/Q1 Predictions Mean -73.5279 +trainer/Q1 Predictions Std 19.8381 +trainer/Q1 Predictions Max -1.04106 +trainer/Q1 Predictions Min -86.646 +trainer/Q2 Predictions Mean -73.4261 +trainer/Q2 Predictions Std 19.7847 +trainer/Q2 Predictions Max -0.518787 +trainer/Q2 Predictions Min -86.3557 +trainer/Q Targets Mean -73.0944 +trainer/Q Targets Std 20.0226 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4218 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0147791 +trainer/policy/mean Std 0.713878 +trainer/policy/mean Max 0.998465 +trainer/policy/mean Min -0.999219 +trainer/policy/std Mean 0.427043 +trainer/policy/std Std 0.0207691 +trainer/policy/std Max 0.448986 +trainer/policy/std Min 0.391076 +trainer/Advantage Weights Mean 2.03358 +trainer/Advantage Weights Std 11.537 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.01622e-16 +trainer/Advantage Score Mean -0.481431 +trainer/Advantage Score Std 0.523429 +trainer/Advantage Score Max 1.54591 +trainer/Advantage Score Min -3.50469 +trainer/V1 Predictions Mean -72.9129 +trainer/V1 Predictions Std 19.871 +trainer/V1 Predictions Max -0.528523 +trainer/V1 Predictions Min -86.5999 +trainer/VF Loss 0.0636805 +expl/num steps total 372000 +expl/num paths total 427 +expl/path length Mean 500 +expl/path length Std 127 +expl/path length Max 627 +expl/path length Min 373 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0328678 +expl/Actions Std 0.827439 +expl/Actions Max 2.59103 +expl/Actions Min -2.44344 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 360378 +eval/num paths total 372 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0116598 +eval/Actions Std 0.691525 +eval/Actions Max 0.999443 +eval/Actions Min -0.998664 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.68174e-06 +time/evaluation sampling (s) 2.30101 +time/exploration sampling (s) 2.79728 +time/logging (s) 0.00721136 +time/saving (s) 0.010127 +time/training (s) 11.843 +time/epoch (s) 16.9586 +time/total (s) 7217.06 +Epoch -629 +------------------------------ ---------------- +2022-05-15 20:03:00.145878 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -628 finished +------------------------------ ---------------- +epoch -628 +replay_buffer/size 999047 +trainer/num train calls 373000 +trainer/QF1 Loss 0.671837 +trainer/QF2 Loss 0.645367 +trainer/Policy Loss 24.035 +trainer/Q1 Predictions Mean -70.1638 +trainer/Q1 Predictions Std 21.0765 +trainer/Q1 Predictions Max -0.353426 +trainer/Q1 Predictions Min -86.5486 +trainer/Q2 Predictions Mean -70.1925 +trainer/Q2 Predictions Std 21.0547 +trainer/Q2 Predictions Max 0.238886 +trainer/Q2 Predictions Min -86.5863 +trainer/Q Targets Mean -70.3003 +trainer/Q Targets Std 20.9719 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7043 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0229189 +trainer/policy/mean Std 0.715747 +trainer/policy/mean Max 0.998276 +trainer/policy/mean Min -0.999256 +trainer/policy/std Mean 0.424222 +trainer/policy/std Std 0.0197394 +trainer/policy/std Max 0.445248 +trainer/policy/std Min 0.386823 +trainer/Advantage Weights Mean 5.42228 +trainer/Advantage Weights Std 18.6467 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.24531e-15 +trainer/Advantage Score Mean -0.309587 +trainer/Advantage Score Std 0.552134 +trainer/Advantage Score Max 1.24379 +trainer/Advantage Score Min -3.25584 +trainer/V1 Predictions Mean -70.0042 +trainer/V1 Predictions Std 21.2352 +trainer/V1 Predictions Max -0.093549 +trainer/V1 Predictions Min -86.5087 +trainer/VF Loss 0.0639687 +expl/num steps total 373000 +expl/num paths total 429 +expl/path length Mean 500 +expl/path length Std 453 +expl/path length Max 953 +expl/path length Min 47 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.000627771 +expl/Actions Std 0.824447 +expl/Actions Max 2.40262 +expl/Actions Min -2.1503 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 361378 +eval/num paths total 373 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00698994 +eval/Actions Std 0.698074 +eval/Actions Max 0.99967 +eval/Actions Min -0.999561 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.65078e-06 +time/evaluation sampling (s) 2.30164 +time/exploration sampling (s) 2.85491 +time/logging (s) 0.00665022 +time/saving (s) 0.00942071 +time/training (s) 11.2798 +time/epoch (s) 16.4524 +time/total (s) 7233.52 +Epoch -628 +------------------------------ ---------------- +2022-05-15 20:03:17.084996 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -627 finished +------------------------------ ---------------- +epoch -627 +replay_buffer/size 999047 +trainer/num train calls 374000 +trainer/QF1 Loss 0.923593 +trainer/QF2 Loss 0.804841 +trainer/Policy Loss 50.2037 +trainer/Q1 Predictions Mean -72.9044 +trainer/Q1 Predictions Std 18.2718 +trainer/Q1 Predictions Max -1.80807 +trainer/Q1 Predictions Min -85.8286 +trainer/Q2 Predictions Mean -72.909 +trainer/Q2 Predictions Std 18.2617 +trainer/Q2 Predictions Max -1.88933 +trainer/Q2 Predictions Min -86.1181 +trainer/Q Targets Mean -73.3017 +trainer/Q Targets Std 18.2844 +trainer/Q Targets Max -3.14078 +trainer/Q Targets Min -86.3932 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0233672 +trainer/policy/mean Std 0.708786 +trainer/policy/mean Max 0.998977 +trainer/policy/mean Min -0.998032 +trainer/policy/std Mean 0.425561 +trainer/policy/std Std 0.0198534 +trainer/policy/std Max 0.446121 +trainer/policy/std Min 0.392466 +trainer/Advantage Weights Mean 6.65839 +trainer/Advantage Weights Std 19.5028 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.38609e-21 +trainer/Advantage Score Mean -0.248542 +trainer/Advantage Score Std 0.681306 +trainer/Advantage Score Max 1.07838 +trainer/Advantage Score Min -4.80278 +trainer/V1 Predictions Mean -72.9593 +trainer/V1 Predictions Std 18.6061 +trainer/V1 Predictions Max -1.40707 +trainer/V1 Predictions Min -86.2566 +trainer/VF Loss 0.0754396 +expl/num steps total 374000 +expl/num paths total 431 +expl/path length Mean 500 +expl/path length Std 362 +expl/path length Max 862 +expl/path length Min 138 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0343995 +expl/Actions Std 0.827578 +expl/Actions Max 2.46914 +expl/Actions Min -2.25388 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 362378 +eval/num paths total 374 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0365453 +eval/Actions Std 0.706092 +eval/Actions Max 0.999102 +eval/Actions Min -0.999043 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83681e-06 +time/evaluation sampling (s) 2.28055 +time/exploration sampling (s) 2.85389 +time/logging (s) 0.00659938 +time/saving (s) 0.0157522 +time/training (s) 11.7754 +time/epoch (s) 16.9322 +time/total (s) 7250.45 +Epoch -627 +------------------------------ ---------------- +2022-05-15 20:03:34.095819 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -626 finished +------------------------------ ---------------- +epoch -626 +replay_buffer/size 999047 +trainer/num train calls 375000 +trainer/QF1 Loss 1.38711 +trainer/QF2 Loss 1.36809 +trainer/Policy Loss 69.1174 +trainer/Q1 Predictions Mean -74.4632 +trainer/Q1 Predictions Std 15.9537 +trainer/Q1 Predictions Max -10.5133 +trainer/Q1 Predictions Min -86.5763 +trainer/Q2 Predictions Mean -74.5038 +trainer/Q2 Predictions Std 15.9202 +trainer/Q2 Predictions Max -9.03565 +trainer/Q2 Predictions Min -86.6499 +trainer/Q Targets Mean -75.0242 +trainer/Q Targets Std 15.7899 +trainer/Q Targets Max -9.69472 +trainer/Q Targets Min -86.7576 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 5.41098e-06 +trainer/policy/mean Std 0.719985 +trainer/policy/mean Max 0.999383 +trainer/policy/mean Min -0.999373 +trainer/policy/std Mean 0.425016 +trainer/policy/std Std 0.0201778 +trainer/policy/std Max 0.443688 +trainer/policy/std Min 0.390104 +trainer/Advantage Weights Mean 13.231 +trainer/Advantage Weights Std 28.5382 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.13957e-15 +trainer/Advantage Score Mean -0.0783092 +trainer/Advantage Score Std 0.50002 +trainer/Advantage Score Max 1.02628 +trainer/Advantage Score Min -3.37782 +trainer/V1 Predictions Mean -74.7737 +trainer/V1 Predictions Std 16.0789 +trainer/V1 Predictions Max -9.38662 +trainer/V1 Predictions Min -86.7397 +trainer/VF Loss 0.0688864 +expl/num steps total 375000 +expl/num paths total 433 +expl/path length Mean 500 +expl/path length Std 376 +expl/path length Max 876 +expl/path length Min 124 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0195005 +expl/Actions Std 0.823539 +expl/Actions Max 2.28951 +expl/Actions Min -2.23418 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 363378 +eval/num paths total 375 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0993855 +eval/Actions Std 0.720083 +eval/Actions Max 0.999834 +eval/Actions Min -0.998956 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.58094e-06 +time/evaluation sampling (s) 2.32534 +time/exploration sampling (s) 2.892 +time/logging (s) 0.00668783 +time/saving (s) 0.0097838 +time/training (s) 11.7692 +time/epoch (s) 17.0031 +time/total (s) 7267.46 +Epoch -626 +------------------------------ ---------------- +2022-05-15 20:03:51.077620 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -625 finished +------------------------------ ---------------- +epoch -625 +replay_buffer/size 999047 +trainer/num train calls 376000 +trainer/QF1 Loss 0.668075 +trainer/QF2 Loss 0.661318 +trainer/Policy Loss 24.3207 +trainer/Q1 Predictions Mean -74.7379 +trainer/Q1 Predictions Std 15.6991 +trainer/Q1 Predictions Max -1.97496 +trainer/Q1 Predictions Min -86.5467 +trainer/Q2 Predictions Mean -74.7074 +trainer/Q2 Predictions Std 15.7414 +trainer/Q2 Predictions Max -1.74024 +trainer/Q2 Predictions Min -86.5211 +trainer/Q Targets Mean -74.6974 +trainer/Q Targets Std 16.1099 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2188 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00508093 +trainer/policy/mean Std 0.709952 +trainer/policy/mean Max 0.996359 +trainer/policy/mean Min -0.998211 +trainer/policy/std Mean 0.423663 +trainer/policy/std Std 0.0206714 +trainer/policy/std Max 0.44405 +trainer/policy/std Min 0.389118 +trainer/Advantage Weights Mean 3.59102 +trainer/Advantage Weights Std 15.1023 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.46362e-16 +trainer/Advantage Score Mean -0.385269 +trainer/Advantage Score Std 0.536576 +trainer/Advantage Score Max 1.09826 +trainer/Advantage Score Min -3.45939 +trainer/V1 Predictions Mean -74.5172 +trainer/V1 Predictions Std 15.9573 +trainer/V1 Predictions Max -0.600835 +trainer/V1 Predictions Min -86.0883 +trainer/VF Loss 0.0565409 +expl/num steps total 376000 +expl/num paths total 434 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0395158 +expl/Actions Std 0.86516 +expl/Actions Max 2.42558 +expl/Actions Min -2.36749 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 364378 +eval/num paths total 376 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0129876 +eval/Actions Std 0.698897 +eval/Actions Max 0.998295 +eval/Actions Min -0.999394 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.67196e-06 +time/evaluation sampling (s) 2.35567 +time/exploration sampling (s) 2.8987 +time/logging (s) 0.00711528 +time/saving (s) 0.00993209 +time/training (s) 11.7028 +time/epoch (s) 16.9742 +time/total (s) 7284.44 +Epoch -625 +------------------------------ ---------------- +2022-05-15 20:04:08.762965 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -624 finished +------------------------------ ---------------- +epoch -624 +replay_buffer/size 999047 +trainer/num train calls 377000 +trainer/QF1 Loss 0.669262 +trainer/QF2 Loss 0.678542 +trainer/Policy Loss 10.8148 +trainer/Q1 Predictions Mean -72.0891 +trainer/Q1 Predictions Std 19.0848 +trainer/Q1 Predictions Max -0.308391 +trainer/Q1 Predictions Min -86.533 +trainer/Q2 Predictions Mean -72.1718 +trainer/Q2 Predictions Std 19.097 +trainer/Q2 Predictions Max -0.424519 +trainer/Q2 Predictions Min -86.5855 +trainer/Q Targets Mean -72.0795 +trainer/Q Targets Std 19.2307 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3359 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0562703 +trainer/policy/mean Std 0.725106 +trainer/policy/mean Max 0.999027 +trainer/policy/mean Min -0.99801 +trainer/policy/std Mean 0.422931 +trainer/policy/std Std 0.0204611 +trainer/policy/std Max 0.445964 +trainer/policy/std Min 0.390505 +trainer/Advantage Weights Mean 1.79976 +trainer/Advantage Weights Std 5.99598 +trainer/Advantage Weights Max 61.5702 +trainer/Advantage Weights Min 2.43198e-19 +trainer/Advantage Score Mean -0.436171 +trainer/Advantage Score Std 0.664417 +trainer/Advantage Score Max 0.412018 +trainer/Advantage Score Min -4.28604 +trainer/V1 Predictions Mean -71.8062 +trainer/V1 Predictions Std 19.434 +trainer/V1 Predictions Max 0.391464 +trainer/V1 Predictions Min -86.2931 +trainer/VF Loss 0.068667 +expl/num steps total 377000 +expl/num paths total 436 +expl/path length Mean 500 +expl/path length Std 55 +expl/path length Max 555 +expl/path length Min 445 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0861726 +expl/Actions Std 0.878062 +expl/Actions Max 2.33961 +expl/Actions Min -2.25064 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 365358 +eval/num paths total 377 +eval/path length Mean 980 +eval/path length Std 0 +eval/path length Max 980 +eval/path length Min 980 +eval/Rewards Mean 0.00102041 +eval/Rewards Std 0.0319275 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0458602 +eval/Actions Std 0.720071 +eval/Actions Max 0.998841 +eval/Actions Min -0.999623 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.03285e-06 +time/evaluation sampling (s) 2.31359 +time/exploration sampling (s) 2.88787 +time/logging (s) 0.00808353 +time/saving (s) 0.0106166 +time/training (s) 12.4588 +time/epoch (s) 17.679 +time/total (s) 7302.12 +Epoch -624 +------------------------------ ---------------- +2022-05-15 20:04:26.122894 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -623 finished +------------------------------ ---------------- +epoch -623 +replay_buffer/size 999047 +trainer/num train calls 378000 +trainer/QF1 Loss 1.09473 +trainer/QF2 Loss 1.05877 +trainer/Policy Loss 6.15403 +trainer/Q1 Predictions Mean -71.6839 +trainer/Q1 Predictions Std 21.3064 +trainer/Q1 Predictions Max -0.491264 +trainer/Q1 Predictions Min -86.7472 +trainer/Q2 Predictions Mean -71.7013 +trainer/Q2 Predictions Std 21.1335 +trainer/Q2 Predictions Max -0.542974 +trainer/Q2 Predictions Min -86.3902 +trainer/Q Targets Mean -71.4347 +trainer/Q Targets Std 21.1922 +trainer/Q Targets Max 1.05802 +trainer/Q Targets Min -86.8371 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0234027 +trainer/policy/mean Std 0.715874 +trainer/policy/mean Max 0.999813 +trainer/policy/mean Min -0.998278 +trainer/policy/std Mean 0.421892 +trainer/policy/std Std 0.0212726 +trainer/policy/std Max 0.446929 +trainer/policy/std Min 0.389291 +trainer/Advantage Weights Mean 1.61715 +trainer/Advantage Weights Std 8.60413 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.56733e-20 +trainer/Advantage Score Mean -0.561007 +trainer/Advantage Score Std 0.807526 +trainer/Advantage Score Max 1.54837 +trainer/Advantage Score Min -4.43348 +trainer/V1 Predictions Mean -71.1009 +trainer/V1 Predictions Std 21.6313 +trainer/V1 Predictions Max 1.58027 +trainer/V1 Predictions Min -86.7261 +trainer/VF Loss 0.107461 +expl/num steps total 378000 +expl/num paths total 437 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.132522 +expl/Actions Std 0.802466 +expl/Actions Max 2.39273 +expl/Actions Min -2.17055 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 366358 +eval/num paths total 378 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.000709638 +eval/Actions Std 0.698843 +eval/Actions Max 0.99967 +eval/Actions Min -0.999101 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.98606e-06 +time/evaluation sampling (s) 2.39834 +time/exploration sampling (s) 2.86988 +time/logging (s) 0.00866737 +time/saving (s) 0.0128183 +time/training (s) 12.0631 +time/epoch (s) 17.3528 +time/total (s) 7319.48 +Epoch -623 +------------------------------ ---------------- +2022-05-15 20:04:43.836895 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -622 finished +------------------------------ ---------------- +epoch -622 +replay_buffer/size 999047 +trainer/num train calls 379000 +trainer/QF1 Loss 0.845738 +trainer/QF2 Loss 0.764107 +trainer/Policy Loss 21.8002 +trainer/Q1 Predictions Mean -72.6615 +trainer/Q1 Predictions Std 18.6333 +trainer/Q1 Predictions Max -1.54768 +trainer/Q1 Predictions Min -86.7314 +trainer/Q2 Predictions Mean -72.6604 +trainer/Q2 Predictions Std 18.6427 +trainer/Q2 Predictions Max -1.10918 +trainer/Q2 Predictions Min -87.1192 +trainer/Q Targets Mean -72.5996 +trainer/Q Targets Std 18.891 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8748 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00705796 +trainer/policy/mean Std 0.717828 +trainer/policy/mean Max 0.998737 +trainer/policy/mean Min -0.998989 +trainer/policy/std Mean 0.423715 +trainer/policy/std Std 0.0220076 +trainer/policy/std Max 0.447106 +trainer/policy/std Min 0.388856 +trainer/Advantage Weights Mean 5.20454 +trainer/Advantage Weights Std 17.8179 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.30991e-12 +trainer/Advantage Score Mean -0.277917 +trainer/Advantage Score Std 0.501939 +trainer/Advantage Score Max 1.18021 +trainer/Advantage Score Min -2.67938 +trainer/V1 Predictions Mean -72.3939 +trainer/V1 Predictions Std 18.9241 +trainer/V1 Predictions Max 1.05083 +trainer/V1 Predictions Min -86.7192 +trainer/VF Loss 0.0531445 +expl/num steps total 379000 +expl/num paths total 439 +expl/path length Mean 500 +expl/path length Std 422 +expl/path length Max 922 +expl/path length Min 78 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0172771 +expl/Actions Std 0.799313 +expl/Actions Max 2.32572 +expl/Actions Min -2.47037 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 366789 +eval/num paths total 379 +eval/path length Mean 431 +eval/path length Std 0 +eval/path length Max 431 +eval/path length Min 431 +eval/Rewards Mean 0.00232019 +eval/Rewards Std 0.0481124 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0348172 +eval/Actions Std 0.742915 +eval/Actions Max 0.998968 +eval/Actions Min -0.999534 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.0254e-06 +time/evaluation sampling (s) 2.53262 +time/exploration sampling (s) 2.81513 +time/logging (s) 0.00655701 +time/saving (s) 0.0151158 +time/training (s) 12.3343 +time/epoch (s) 17.7037 +time/total (s) 7337.19 +Epoch -622 +------------------------------ ---------------- +2022-05-15 20:05:08.809201 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -621 finished +------------------------------ ---------------- +epoch -621 +replay_buffer/size 999047 +trainer/num train calls 380000 +trainer/QF1 Loss 0.603997 +trainer/QF2 Loss 0.533858 +trainer/Policy Loss 13.8477 +trainer/Q1 Predictions Mean -74.088 +trainer/Q1 Predictions Std 18.3763 +trainer/Q1 Predictions Max 0.224184 +trainer/Q1 Predictions Min -87.3879 +trainer/Q2 Predictions Mean -74.141 +trainer/Q2 Predictions Std 18.4069 +trainer/Q2 Predictions Max 0.742978 +trainer/Q2 Predictions Min -87.2526 +trainer/Q Targets Mean -74.1878 +trainer/Q Targets Std 18.373 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7085 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0201668 +trainer/policy/mean Std 0.714584 +trainer/policy/mean Max 0.998965 +trainer/policy/mean Min -0.999754 +trainer/policy/std Mean 0.423236 +trainer/policy/std Std 0.0206431 +trainer/policy/std Max 0.445686 +trainer/policy/std Min 0.391078 +trainer/Advantage Weights Mean 3.77929 +trainer/Advantage Weights Std 16.4801 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.11157e-11 +trainer/Advantage Score Mean -0.322319 +trainer/Advantage Score Std 0.497296 +trainer/Advantage Score Max 2.03962 +trainer/Advantage Score Min -2.52227 +trainer/V1 Predictions Mean -73.8893 +trainer/V1 Predictions Std 18.4978 +trainer/V1 Predictions Max 0.700696 +trainer/V1 Predictions Min -86.684 +trainer/VF Loss 0.0610263 +expl/num steps total 380000 +expl/num paths total 440 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0411179 +expl/Actions Std 0.766502 +expl/Actions Max 2.10608 +expl/Actions Min -2.27278 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 367789 +eval/num paths total 380 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.125798 +eval/Actions Std 0.694817 +eval/Actions Max 0.999436 +eval/Actions Min -0.997875 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94903e-06 +time/evaluation sampling (s) 5.33692 +time/exploration sampling (s) 3.02464 +time/logging (s) 0.0133508 +time/saving (s) 0.01796 +time/training (s) 16.5789 +time/epoch (s) 24.9718 +time/total (s) 7362.17 +Epoch -621 +------------------------------ ---------------- +2022-05-15 20:05:40.240678 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -620 finished +------------------------------ ---------------- +epoch -620 +replay_buffer/size 999047 +trainer/num train calls 381000 +trainer/QF1 Loss 0.835784 +trainer/QF2 Loss 0.687889 +trainer/Policy Loss 14.4574 +trainer/Q1 Predictions Mean -73.9557 +trainer/Q1 Predictions Std 18.5484 +trainer/Q1 Predictions Max -0.321807 +trainer/Q1 Predictions Min -87.4058 +trainer/Q2 Predictions Mean -73.8503 +trainer/Q2 Predictions Std 18.6362 +trainer/Q2 Predictions Max -0.478722 +trainer/Q2 Predictions Min -87.5554 +trainer/Q Targets Mean -73.6905 +trainer/Q Targets Std 18.7547 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4007 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.020664 +trainer/policy/mean Std 0.722833 +trainer/policy/mean Max 0.998277 +trainer/policy/mean Min -0.999776 +trainer/policy/std Mean 0.425487 +trainer/policy/std Std 0.0210133 +trainer/policy/std Max 0.449683 +trainer/policy/std Min 0.389978 +trainer/Advantage Weights Mean 3.97765 +trainer/Advantage Weights Std 16.1693 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.87362e-16 +trainer/Advantage Score Mean -0.328974 +trainer/Advantage Score Std 0.54504 +trainer/Advantage Score Max 1.13489 +trainer/Advantage Score Min -3.57858 +trainer/V1 Predictions Mean -73.4752 +trainer/V1 Predictions Std 18.7591 +trainer/V1 Predictions Max 1.94012 +trainer/V1 Predictions Min -87.3381 +trainer/VF Loss 0.0544773 +expl/num steps total 381000 +expl/num paths total 441 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0226311 +expl/Actions Std 0.844276 +expl/Actions Max 2.71816 +expl/Actions Min -2.42704 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 368496 +eval/num paths total 381 +eval/path length Mean 707 +eval/path length Std 0 +eval/path length Max 707 +eval/path length Min 707 +eval/Rewards Mean 0.00141443 +eval/Rewards Std 0.0375823 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0349045 +eval/Actions Std 0.731277 +eval/Actions Max 0.998498 +eval/Actions Min -0.998589 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.3615e-05 +time/evaluation sampling (s) 4.01262 +time/exploration sampling (s) 4.74821 +time/logging (s) 0.00976549 +time/saving (s) 0.0138858 +time/training (s) 22.627 +time/epoch (s) 31.4115 +time/total (s) 7393.58 +Epoch -620 +------------------------------ ---------------- +2022-05-15 20:05:59.904389 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -619 finished +------------------------------ ---------------- +epoch -619 +replay_buffer/size 999047 +trainer/num train calls 382000 +trainer/QF1 Loss 1.27685 +trainer/QF2 Loss 1.48319 +trainer/Policy Loss 15.6603 +trainer/Q1 Predictions Mean -72.2562 +trainer/Q1 Predictions Std 19.3129 +trainer/Q1 Predictions Max -1.92253 +trainer/Q1 Predictions Min -86.5357 +trainer/Q2 Predictions Mean -72.236 +trainer/Q2 Predictions Std 19.35 +trainer/Q2 Predictions Max -1.31991 +trainer/Q2 Predictions Min -86.7728 +trainer/Q Targets Mean -72.3754 +trainer/Q Targets Std 19.4473 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7159 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0252652 +trainer/policy/mean Std 0.708994 +trainer/policy/mean Max 0.998022 +trainer/policy/mean Min -0.995966 +trainer/policy/std Mean 0.426402 +trainer/policy/std Std 0.0211548 +trainer/policy/std Max 0.447245 +trainer/policy/std Min 0.389529 +trainer/Advantage Weights Mean 4.92239 +trainer/Advantage Weights Std 17.5232 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.36651e-13 +trainer/Advantage Score Mean -0.26044 +trainer/Advantage Score Std 0.476278 +trainer/Advantage Score Max 1.03939 +trainer/Advantage Score Min -2.90722 +trainer/V1 Predictions Mean -72.0958 +trainer/V1 Predictions Std 19.565 +trainer/V1 Predictions Max -0.626661 +trainer/V1 Predictions Min -86.5584 +trainer/VF Loss 0.0477882 +expl/num steps total 382000 +expl/num paths total 443 +expl/path length Mean 500 +expl/path length Std 14 +expl/path length Max 514 +expl/path length Min 486 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0301186 +expl/Actions Std 0.830991 +expl/Actions Max 2.31026 +expl/Actions Min -2.42172 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 369496 +eval/num paths total 382 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0940605 +eval/Actions Std 0.632631 +eval/Actions Max 0.999575 +eval/Actions Min -0.998677 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.63005e-06 +time/evaluation sampling (s) 3.01568 +time/exploration sampling (s) 3.0697 +time/logging (s) 0.0075189 +time/saving (s) 0.0132776 +time/training (s) 13.5453 +time/epoch (s) 19.6515 +time/total (s) 7413.24 +Epoch -619 +------------------------------ ---------------- +2022-05-15 20:06:19.402290 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -618 finished +------------------------------ ---------------- +epoch -618 +replay_buffer/size 999047 +trainer/num train calls 383000 +trainer/QF1 Loss 0.619912 +trainer/QF2 Loss 0.489922 +trainer/Policy Loss 13.7078 +trainer/Q1 Predictions Mean -72.4072 +trainer/Q1 Predictions Std 18.4167 +trainer/Q1 Predictions Max -1.29288 +trainer/Q1 Predictions Min -86.6722 +trainer/Q2 Predictions Mean -72.3592 +trainer/Q2 Predictions Std 18.4291 +trainer/Q2 Predictions Max -1.56066 +trainer/Q2 Predictions Min -86.5083 +trainer/Q Targets Mean -72.5128 +trainer/Q Targets Std 18.5747 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6073 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0145597 +trainer/policy/mean Std 0.7134 +trainer/policy/mean Max 0.999306 +trainer/policy/mean Min -0.999117 +trainer/policy/std Mean 0.42453 +trainer/policy/std Std 0.0214254 +trainer/policy/std Max 0.446915 +trainer/policy/std Min 0.391039 +trainer/Advantage Weights Mean 3.91029 +trainer/Advantage Weights Std 15.3009 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.03672e-11 +trainer/Advantage Score Mean -0.33765 +trainer/Advantage Score Std 0.479814 +trainer/Advantage Score Max 0.993721 +trainer/Advantage Score Min -2.35306 +trainer/V1 Predictions Mean -72.2617 +trainer/V1 Predictions Std 18.572 +trainer/V1 Predictions Max 0.167431 +trainer/V1 Predictions Min -86.4994 +trainer/VF Loss 0.0482966 +expl/num steps total 383000 +expl/num paths total 444 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0861401 +expl/Actions Std 0.87112 +expl/Actions Max 2.36101 +expl/Actions Min -2.40462 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 370496 +eval/num paths total 383 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.122403 +eval/Actions Std 0.687454 +eval/Actions Max 0.995679 +eval/Actions Min -0.997354 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94903e-06 +time/evaluation sampling (s) 2.54416 +time/exploration sampling (s) 2.99958 +time/logging (s) 0.00781639 +time/saving (s) 0.0128299 +time/training (s) 13.925 +time/epoch (s) 19.4894 +time/total (s) 7432.74 +Epoch -618 +------------------------------ ---------------- +2022-05-15 20:06:38.385646 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -617 finished +------------------------------ ---------------- +epoch -617 +replay_buffer/size 999047 +trainer/num train calls 384000 +trainer/QF1 Loss 1.56333 +trainer/QF2 Loss 1.57304 +trainer/Policy Loss 26.7357 +trainer/Q1 Predictions Mean -71.5262 +trainer/Q1 Predictions Std 20.2775 +trainer/Q1 Predictions Max -0.898368 +trainer/Q1 Predictions Min -85.6235 +trainer/Q2 Predictions Mean -71.4767 +trainer/Q2 Predictions Std 20.4061 +trainer/Q2 Predictions Max -0.283633 +trainer/Q2 Predictions Min -85.7192 +trainer/Q Targets Mean -71.7287 +trainer/Q Targets Std 20.5068 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3035 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00365344 +trainer/policy/mean Std 0.708629 +trainer/policy/mean Max 0.997803 +trainer/policy/mean Min -0.998897 +trainer/policy/std Mean 0.423072 +trainer/policy/std Std 0.0218247 +trainer/policy/std Max 0.445337 +trainer/policy/std Min 0.388891 +trainer/Advantage Weights Mean 5.64626 +trainer/Advantage Weights Std 19.3262 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.52108e-19 +trainer/Advantage Score Mean -0.342004 +trainer/Advantage Score Std 0.621228 +trainer/Advantage Score Max 1.57678 +trainer/Advantage Score Min -4.17314 +trainer/V1 Predictions Mean -71.4474 +trainer/V1 Predictions Std 20.5745 +trainer/V1 Predictions Max -0.653814 +trainer/V1 Predictions Min -86.0608 +trainer/VF Loss 0.0813743 +expl/num steps total 384000 +expl/num paths total 446 +expl/path length Mean 500 +expl/path length Std 230 +expl/path length Max 730 +expl/path length Min 270 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0183425 +expl/Actions Std 0.823148 +expl/Actions Max 2.13792 +expl/Actions Min -2.31155 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 371496 +eval/num paths total 384 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0359462 +eval/Actions Std 0.731337 +eval/Actions Max 0.999553 +eval/Actions Min -0.998683 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.59606e-06 +time/evaluation sampling (s) 2.45666 +time/exploration sampling (s) 2.79317 +time/logging (s) 0.00821822 +time/saving (s) 0.0141383 +time/training (s) 13.701 +time/epoch (s) 18.9732 +time/total (s) 7451.71 +Epoch -617 +------------------------------ ---------------- +2022-05-15 20:06:57.018411 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -616 finished +------------------------------ ---------------- +epoch -616 +replay_buffer/size 999047 +trainer/num train calls 385000 +trainer/QF1 Loss 0.606868 +trainer/QF2 Loss 0.515934 +trainer/Policy Loss 9.79959 +trainer/Q1 Predictions Mean -74.5614 +trainer/Q1 Predictions Std 15.993 +trainer/Q1 Predictions Max -3.89578 +trainer/Q1 Predictions Min -87.5227 +trainer/Q2 Predictions Mean -74.4803 +trainer/Q2 Predictions Std 16.0602 +trainer/Q2 Predictions Max -4.26273 +trainer/Q2 Predictions Min -87.14 +trainer/Q Targets Mean -74.3549 +trainer/Q Targets Std 16.048 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0939 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0117153 +trainer/policy/mean Std 0.719777 +trainer/policy/mean Max 0.998931 +trainer/policy/mean Min -0.999566 +trainer/policy/std Mean 0.424592 +trainer/policy/std Std 0.0208491 +trainer/policy/std Max 0.449841 +trainer/policy/std Min 0.390101 +trainer/Advantage Weights Mean 1.15718 +trainer/Advantage Weights Std 8.88654 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.99015e-19 +trainer/Advantage Score Mean -0.511763 +trainer/Advantage Score Std 0.640596 +trainer/Advantage Score Max 0.895828 +trainer/Advantage Score Min -4.1959 +trainer/V1 Predictions Mean -74.0675 +trainer/V1 Predictions Std 16.2587 +trainer/V1 Predictions Max -1.9029 +trainer/V1 Predictions Min -86.8864 +trainer/VF Loss 0.0715575 +expl/num steps total 385000 +expl/num paths total 447 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.10106 +expl/Actions Std 0.799313 +expl/Actions Max 2.63762 +expl/Actions Min -2.37441 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 372496 +eval/num paths total 385 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.228801 +eval/Actions Std 0.685511 +eval/Actions Max 0.994295 +eval/Actions Min -0.995602 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.0743e-06 +time/evaluation sampling (s) 2.43741 +time/exploration sampling (s) 2.88906 +time/logging (s) 0.0120868 +time/saving (s) 0.0155714 +time/training (s) 13.2711 +time/epoch (s) 18.6252 +time/total (s) 7470.35 +Epoch -616 +------------------------------ ---------------- +2022-05-15 20:07:15.318970 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -615 finished +------------------------------ ---------------- +epoch -615 +replay_buffer/size 999047 +trainer/num train calls 386000 +trainer/QF1 Loss 0.925276 +trainer/QF2 Loss 0.912639 +trainer/Policy Loss 7.51363 +trainer/Q1 Predictions Mean -74.1444 +trainer/Q1 Predictions Std 17.9239 +trainer/Q1 Predictions Max 0.393308 +trainer/Q1 Predictions Min -87.1208 +trainer/Q2 Predictions Mean -74.1476 +trainer/Q2 Predictions Std 17.8722 +trainer/Q2 Predictions Max -0.571649 +trainer/Q2 Predictions Min -87.0033 +trainer/Q Targets Mean -73.749 +trainer/Q Targets Std 17.8242 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4646 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.043436 +trainer/policy/mean Std 0.721453 +trainer/policy/mean Max 0.999774 +trainer/policy/mean Min -0.999732 +trainer/policy/std Mean 0.423499 +trainer/policy/std Std 0.0211164 +trainer/policy/std Max 0.449642 +trainer/policy/std Min 0.387499 +trainer/Advantage Weights Mean 2.22023 +trainer/Advantage Weights Std 11.6551 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.1918e-20 +trainer/Advantage Score Mean -0.535369 +trainer/Advantage Score Std 0.623771 +trainer/Advantage Score Max 0.898161 +trainer/Advantage Score Min -4.58762 +trainer/V1 Predictions Mean -73.464 +trainer/V1 Predictions Std 18.0121 +trainer/V1 Predictions Max -0.728587 +trainer/V1 Predictions Min -86.3269 +trainer/VF Loss 0.075925 +expl/num steps total 386000 +expl/num paths total 449 +expl/path length Mean 500 +expl/path length Std 278 +expl/path length Max 778 +expl/path length Min 222 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.00685686 +expl/Actions Std 0.811736 +expl/Actions Max 2.35282 +expl/Actions Min -2.25693 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 373496 +eval/num paths total 386 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0255389 +eval/Actions Std 0.729088 +eval/Actions Max 0.999518 +eval/Actions Min -0.999772 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.48315e-06 +time/evaluation sampling (s) 2.33233 +time/exploration sampling (s) 2.87169 +time/logging (s) 0.00911719 +time/saving (s) 0.0140079 +time/training (s) 13.0575 +time/epoch (s) 18.2847 +time/total (s) 7488.64 +Epoch -615 +------------------------------ ---------------- +2022-05-15 20:07:34.362316 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -614 finished +------------------------------ ---------------- +epoch -614 +replay_buffer/size 999047 +trainer/num train calls 387000 +trainer/QF1 Loss 0.777353 +trainer/QF2 Loss 0.787208 +trainer/Policy Loss 17.4822 +trainer/Q1 Predictions Mean -73.3137 +trainer/Q1 Predictions Std 17.6242 +trainer/Q1 Predictions Max -0.869567 +trainer/Q1 Predictions Min -86.1112 +trainer/Q2 Predictions Mean -73.2853 +trainer/Q2 Predictions Std 17.6268 +trainer/Q2 Predictions Max 0.0658262 +trainer/Q2 Predictions Min -86.4013 +trainer/Q Targets Mean -73.6236 +trainer/Q Targets Std 17.5003 +trainer/Q Targets Max -0.713267 +trainer/Q Targets Min -86.8122 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0172121 +trainer/policy/mean Std 0.71287 +trainer/policy/mean Max 0.997822 +trainer/policy/mean Min -0.999196 +trainer/policy/std Mean 0.423967 +trainer/policy/std Std 0.0216808 +trainer/policy/std Max 0.448832 +trainer/policy/std Min 0.386206 +trainer/Advantage Weights Mean 2.47395 +trainer/Advantage Weights Std 12.7483 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.64221e-22 +trainer/Advantage Score Mean -0.37586 +trainer/Advantage Score Std 0.51573 +trainer/Advantage Score Max 1.32 +trainer/Advantage Score Min -4.85002 +trainer/V1 Predictions Mean -73.3666 +trainer/V1 Predictions Std 17.6982 +trainer/V1 Predictions Max -0.581376 +trainer/V1 Predictions Min -86.4733 +trainer/VF Loss 0.0564978 +expl/num steps total 387000 +expl/num paths total 450 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.200534 +expl/Actions Std 0.804258 +expl/Actions Max 2.41471 +expl/Actions Min -2.22982 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 374496 +eval/num paths total 387 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0192518 +eval/Actions Std 0.704355 +eval/Actions Max 0.999831 +eval/Actions Min -0.999503 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.7162e-06 +time/evaluation sampling (s) 2.34647 +time/exploration sampling (s) 2.92013 +time/logging (s) 0.00829409 +time/saving (s) 0.0130598 +time/training (s) 13.7445 +time/epoch (s) 19.0325 +time/total (s) 7507.67 +Epoch -614 +------------------------------ ---------------- +2022-05-15 20:07:52.959252 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -613 finished +------------------------------ ---------------- +epoch -613 +replay_buffer/size 999047 +trainer/num train calls 388000 +trainer/QF1 Loss 0.828409 +trainer/QF2 Loss 0.861603 +trainer/Policy Loss 25.0865 +trainer/Q1 Predictions Mean -73.302 +trainer/Q1 Predictions Std 17.8322 +trainer/Q1 Predictions Max -4.84655 +trainer/Q1 Predictions Min -86.569 +trainer/Q2 Predictions Mean -73.2898 +trainer/Q2 Predictions Std 17.8774 +trainer/Q2 Predictions Max -4.62127 +trainer/Q2 Predictions Min -86.5384 +trainer/Q Targets Mean -73.8339 +trainer/Q Targets Std 17.8214 +trainer/Q Targets Max -3.78802 +trainer/Q Targets Min -86.6381 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0144046 +trainer/policy/mean Std 0.717995 +trainer/policy/mean Max 0.997649 +trainer/policy/mean Min -0.999745 +trainer/policy/std Mean 0.424223 +trainer/policy/std Std 0.0210241 +trainer/policy/std Max 0.446856 +trainer/policy/std Min 0.388711 +trainer/Advantage Weights Mean 6.59651 +trainer/Advantage Weights Std 19.8677 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.64496e-13 +trainer/Advantage Score Mean -0.205804 +trainer/Advantage Score Std 0.507922 +trainer/Advantage Score Max 1.35864 +trainer/Advantage Score Min -2.86403 +trainer/V1 Predictions Mean -73.5233 +trainer/V1 Predictions Std 17.9791 +trainer/V1 Predictions Max -3.91235 +trainer/V1 Predictions Min -86.4318 +trainer/VF Loss 0.0597603 +expl/num steps total 388000 +expl/num paths total 452 +expl/path length Mean 500 +expl/path length Std 171 +expl/path length Max 671 +expl/path length Min 329 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0144775 +expl/Actions Std 0.82245 +expl/Actions Max 2.3728 +expl/Actions Min -2.41393 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 375496 +eval/num paths total 388 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.176422 +eval/Actions Std 0.763621 +eval/Actions Max 0.999385 +eval/Actions Min -0.999904 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.21819e-06 +time/evaluation sampling (s) 2.51177 +time/exploration sampling (s) 2.98169 +time/logging (s) 0.0113538 +time/saving (s) 0.0155384 +time/training (s) 13.0697 +time/epoch (s) 18.5901 +time/total (s) 7526.27 +Epoch -613 +------------------------------ ---------------- +2022-05-15 20:08:11.574460 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -612 finished +------------------------------ ---------------- +epoch -612 +replay_buffer/size 999047 +trainer/num train calls 389000 +trainer/QF1 Loss 0.4945 +trainer/QF2 Loss 0.524182 +trainer/Policy Loss 24.4689 +trainer/Q1 Predictions Mean -73.1852 +trainer/Q1 Predictions Std 18.8783 +trainer/Q1 Predictions Max -0.296352 +trainer/Q1 Predictions Min -86.7997 +trainer/Q2 Predictions Mean -73.2061 +trainer/Q2 Predictions Std 18.8807 +trainer/Q2 Predictions Max -0.37157 +trainer/Q2 Predictions Min -86.7994 +trainer/Q Targets Mean -73.2987 +trainer/Q Targets Std 18.9253 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5154 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0117907 +trainer/policy/mean Std 0.725717 +trainer/policy/mean Max 0.99864 +trainer/policy/mean Min -0.999607 +trainer/policy/std Mean 0.424137 +trainer/policy/std Std 0.02064 +trainer/policy/std Max 0.445519 +trainer/policy/std Min 0.390858 +trainer/Advantage Weights Mean 5.68798 +trainer/Advantage Weights Std 20.6257 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.26246e-13 +trainer/Advantage Score Mean -0.260105 +trainer/Advantage Score Std 0.510632 +trainer/Advantage Score Max 1.50882 +trainer/Advantage Score Min -2.97005 +trainer/V1 Predictions Mean -73.105 +trainer/V1 Predictions Std 18.8931 +trainer/V1 Predictions Max -0.0470046 +trainer/V1 Predictions Min -86.5407 +trainer/VF Loss 0.0590166 +expl/num steps total 389000 +expl/num paths total 454 +expl/path length Mean 500 +expl/path length Std 381 +expl/path length Max 881 +expl/path length Min 119 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0142025 +expl/Actions Std 0.826457 +expl/Actions Max 2.54033 +expl/Actions Min -2.50631 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 376496 +eval/num paths total 389 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0925818 +eval/Actions Std 0.628966 +eval/Actions Max 0.998722 +eval/Actions Min -0.999192 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.75002e-06 +time/evaluation sampling (s) 2.40842 +time/exploration sampling (s) 2.92982 +time/logging (s) 0.00783158 +time/saving (s) 0.0179146 +time/training (s) 13.2375 +time/epoch (s) 18.6015 +time/total (s) 7544.88 +Epoch -612 +------------------------------ ---------------- +2022-05-15 20:08:30.135140 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -611 finished +------------------------------ ---------------- +epoch -611 +replay_buffer/size 999047 +trainer/num train calls 390000 +trainer/QF1 Loss 0.708616 +trainer/QF2 Loss 0.803175 +trainer/Policy Loss 10.763 +trainer/Q1 Predictions Mean -73.6599 +trainer/Q1 Predictions Std 18.513 +trainer/Q1 Predictions Max -0.475024 +trainer/Q1 Predictions Min -88.2709 +trainer/Q2 Predictions Mean -73.6721 +trainer/Q2 Predictions Std 18.5863 +trainer/Q2 Predictions Max -0.71388 +trainer/Q2 Predictions Min -87.9611 +trainer/Q Targets Mean -73.3537 +trainer/Q Targets Std 18.2494 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7764 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00598281 +trainer/policy/mean Std 0.720708 +trainer/policy/mean Max 0.998801 +trainer/policy/mean Min -0.999418 +trainer/policy/std Mean 0.422656 +trainer/policy/std Std 0.020496 +trainer/policy/std Max 0.445225 +trainer/policy/std Min 0.391459 +trainer/Advantage Weights Mean 3.16447 +trainer/Advantage Weights Std 16.0276 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.81588e-17 +trainer/Advantage Score Mean -0.657114 +trainer/Advantage Score Std 0.575665 +trainer/Advantage Score Max 1.29363 +trainer/Advantage Score Min -3.69674 +trainer/V1 Predictions Mean -73.1424 +trainer/V1 Predictions Std 18.2789 +trainer/V1 Predictions Max -0.514988 +trainer/V1 Predictions Min -87.7864 +trainer/VF Loss 0.0946204 +expl/num steps total 390000 +expl/num paths total 455 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00648407 +expl/Actions Std 0.838947 +expl/Actions Max 2.31728 +expl/Actions Min -2.28862 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 377144 +eval/num paths total 390 +eval/path length Mean 648 +eval/path length Std 0 +eval/path length Max 648 +eval/path length Min 648 +eval/Rewards Mean 0.00154321 +eval/Rewards Std 0.0392534 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0284052 +eval/Actions Std 0.712168 +eval/Actions Max 0.998719 +eval/Actions Min -0.997958 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.11481e-06 +time/evaluation sampling (s) 2.41696 +time/exploration sampling (s) 3.06518 +time/logging (s) 0.00754119 +time/saving (s) 0.0140009 +time/training (s) 13.047 +time/epoch (s) 18.5507 +time/total (s) 7563.43 +Epoch -611 +------------------------------ ---------------- +2022-05-15 20:08:49.421991 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -610 finished +------------------------------ ---------------- +epoch -610 +replay_buffer/size 999047 +trainer/num train calls 391000 +trainer/QF1 Loss 0.575806 +trainer/QF2 Loss 0.560297 +trainer/Policy Loss 28.1086 +trainer/Q1 Predictions Mean -73.9802 +trainer/Q1 Predictions Std 17.6038 +trainer/Q1 Predictions Max -0.568268 +trainer/Q1 Predictions Min -86.4465 +trainer/Q2 Predictions Mean -73.9471 +trainer/Q2 Predictions Std 17.578 +trainer/Q2 Predictions Max -0.695337 +trainer/Q2 Predictions Min -86.7137 +trainer/Q Targets Mean -74.2354 +trainer/Q Targets Std 17.6804 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5386 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00285732 +trainer/policy/mean Std 0.723287 +trainer/policy/mean Max 0.999441 +trainer/policy/mean Min -0.998082 +trainer/policy/std Mean 0.423695 +trainer/policy/std Std 0.0207579 +trainer/policy/std Max 0.447624 +trainer/policy/std Min 0.388979 +trainer/Advantage Weights Mean 6.58914 +trainer/Advantage Weights Std 20.2614 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.55126e-13 +trainer/Advantage Score Mean -0.190191 +trainer/Advantage Score Std 0.563634 +trainer/Advantage Score Max 2.76167 +trainer/Advantage Score Min -2.8997 +trainer/V1 Predictions Mean -73.9417 +trainer/V1 Predictions Std 17.8343 +trainer/V1 Predictions Max -0.241291 +trainer/V1 Predictions Min -86.4314 +trainer/VF Loss 0.090352 +expl/num steps total 391000 +expl/num paths total 457 +expl/path length Mean 500 +expl/path length Std 349 +expl/path length Max 849 +expl/path length Min 151 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0220686 +expl/Actions Std 0.824549 +expl/Actions Max 2.37788 +expl/Actions Min -2.2156 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 378144 +eval/num paths total 391 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0877206 +eval/Actions Std 0.722064 +eval/Actions Max 0.999884 +eval/Actions Min -0.998413 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.63285e-06 +time/evaluation sampling (s) 2.70177 +time/exploration sampling (s) 3.06959 +time/logging (s) 0.00914842 +time/saving (s) 0.0139488 +time/training (s) 13.4842 +time/epoch (s) 19.2787 +time/total (s) 7582.72 +Epoch -610 +------------------------------ ---------------- +2022-05-15 20:09:08.135971 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -609 finished +------------------------------ ---------------- +epoch -609 +replay_buffer/size 999047 +trainer/num train calls 392000 +trainer/QF1 Loss 0.750142 +trainer/QF2 Loss 0.847839 +trainer/Policy Loss 12.7473 +trainer/Q1 Predictions Mean -73.4586 +trainer/Q1 Predictions Std 18.2294 +trainer/Q1 Predictions Max -0.094791 +trainer/Q1 Predictions Min -86.2788 +trainer/Q2 Predictions Mean -73.4715 +trainer/Q2 Predictions Std 18.0949 +trainer/Q2 Predictions Max -0.32413 +trainer/Q2 Predictions Min -86.1756 +trainer/Q Targets Mean -73.3672 +trainer/Q Targets Std 18.3338 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6876 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.015757 +trainer/policy/mean Std 0.72552 +trainer/policy/mean Max 0.998629 +trainer/policy/mean Min -0.99735 +trainer/policy/std Mean 0.424522 +trainer/policy/std Std 0.0217718 +trainer/policy/std Max 0.447795 +trainer/policy/std Min 0.390083 +trainer/Advantage Weights Mean 3.84146 +trainer/Advantage Weights Std 17.1184 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.2211e-16 +trainer/Advantage Score Mean -0.454325 +trainer/Advantage Score Std 0.620253 +trainer/Advantage Score Max 1.62636 +trainer/Advantage Score Min -3.47347 +trainer/V1 Predictions Mean -73.1404 +trainer/V1 Predictions Std 18.3438 +trainer/V1 Predictions Max -0.362449 +trainer/V1 Predictions Min -86.3366 +trainer/VF Loss 0.0851368 +expl/num steps total 392000 +expl/num paths total 459 +expl/path length Mean 500 +expl/path length Std 194 +expl/path length Max 694 +expl/path length Min 306 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0106902 +expl/Actions Std 0.82617 +expl/Actions Max 2.84347 +expl/Actions Min -2.22404 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 379144 +eval/num paths total 392 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0135629 +eval/Actions Std 0.723336 +eval/Actions Max 0.999065 +eval/Actions Min -0.999379 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.51131e-06 +time/evaluation sampling (s) 2.44275 +time/exploration sampling (s) 2.93703 +time/logging (s) 0.00935735 +time/saving (s) 0.0139725 +time/training (s) 13.3035 +time/epoch (s) 18.7066 +time/total (s) 7601.43 +Epoch -609 +------------------------------ ---------------- +2022-05-15 20:09:31.351989 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -608 finished +------------------------------ ---------------- +epoch -608 +replay_buffer/size 999047 +trainer/num train calls 393000 +trainer/QF1 Loss 0.680341 +trainer/QF2 Loss 0.548735 +trainer/Policy Loss 15.7322 +trainer/Q1 Predictions Mean -73.2301 +trainer/Q1 Predictions Std 17.2391 +trainer/Q1 Predictions Max -2.03393 +trainer/Q1 Predictions Min -85.7063 +trainer/Q2 Predictions Mean -73.4021 +trainer/Q2 Predictions Std 17.3018 +trainer/Q2 Predictions Max -2.5937 +trainer/Q2 Predictions Min -85.8728 +trainer/Q Targets Mean -73.5898 +trainer/Q Targets Std 17.5386 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0687 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0151166 +trainer/policy/mean Std 0.726958 +trainer/policy/mean Max 0.999652 +trainer/policy/mean Min -0.997886 +trainer/policy/std Mean 0.42529 +trainer/policy/std Std 0.0217951 +trainer/policy/std Max 0.446715 +trainer/policy/std Min 0.389035 +trainer/Advantage Weights Mean 3.49423 +trainer/Advantage Weights Std 14.7253 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.2218e-14 +trainer/Advantage Score Mean -0.385591 +trainer/Advantage Score Std 0.562486 +trainer/Advantage Score Max 1.3972 +trainer/Advantage Score Min -3.20359 +trainer/V1 Predictions Mean -73.3006 +trainer/V1 Predictions Std 17.7243 +trainer/V1 Predictions Max -1.78794 +trainer/V1 Predictions Min -85.8238 +trainer/VF Loss 0.0605922 +expl/num steps total 393000 +expl/num paths total 460 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0699154 +expl/Actions Std 0.811028 +expl/Actions Max 2.38068 +expl/Actions Min -2.20562 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 380144 +eval/num paths total 393 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0828173 +eval/Actions Std 0.675699 +eval/Actions Max 0.999904 +eval/Actions Min -0.999475 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.04123e-06 +time/evaluation sampling (s) 2.56085 +time/exploration sampling (s) 3.04903 +time/logging (s) 0.00992346 +time/saving (s) 0.0153143 +time/training (s) 17.5709 +time/epoch (s) 23.206 +time/total (s) 7624.64 +Epoch -608 +------------------------------ ---------------- +2022-05-15 20:10:00.981140 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -607 finished +------------------------------ ---------------- +epoch -607 +replay_buffer/size 999047 +trainer/num train calls 394000 +trainer/QF1 Loss 0.632463 +trainer/QF2 Loss 0.67391 +trainer/Policy Loss 3.02683 +trainer/Q1 Predictions Mean -72.5645 +trainer/Q1 Predictions Std 18.7953 +trainer/Q1 Predictions Max -1.80978 +trainer/Q1 Predictions Min -87.4713 +trainer/Q2 Predictions Mean -72.5634 +trainer/Q2 Predictions Std 18.8269 +trainer/Q2 Predictions Max -0.943266 +trainer/Q2 Predictions Min -87.1053 +trainer/Q Targets Mean -72.7781 +trainer/Q Targets Std 18.7002 +trainer/Q Targets Max -1.42963 +trainer/Q Targets Min -86.7209 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0262069 +trainer/policy/mean Std 0.717891 +trainer/policy/mean Max 0.998566 +trainer/policy/mean Min -0.998466 +trainer/policy/std Mean 0.424416 +trainer/policy/std Std 0.0216595 +trainer/policy/std Max 0.44719 +trainer/policy/std Min 0.385272 +trainer/Advantage Weights Mean 0.873301 +trainer/Advantage Weights Std 6.57017 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.09494e-13 +trainer/Advantage Score Mean -0.486616 +trainer/Advantage Score Std 0.505777 +trainer/Advantage Score Max 0.540512 +trainer/Advantage Score Min -2.83054 +trainer/V1 Predictions Mean -72.5006 +trainer/V1 Predictions Std 18.9623 +trainer/V1 Predictions Max -0.758268 +trainer/V1 Predictions Min -86.7262 +trainer/VF Loss 0.0514142 +expl/num steps total 394000 +expl/num paths total 462 +expl/path length Mean 500 +expl/path length Std 120 +expl/path length Max 620 +expl/path length Min 380 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00458273 +expl/Actions Std 0.819527 +expl/Actions Max 2.60902 +expl/Actions Min -2.20611 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 380645 +eval/num paths total 394 +eval/path length Mean 501 +eval/path length Std 0 +eval/path length Max 501 +eval/path length Min 501 +eval/Rewards Mean 0.00199601 +eval/Rewards Std 0.0446321 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0279287 +eval/Actions Std 0.745043 +eval/Actions Max 0.997945 +eval/Actions Min -0.999291 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.94112e-06 +time/evaluation sampling (s) 4.711 +time/exploration sampling (s) 6.70174 +time/logging (s) 0.00610747 +time/saving (s) 0.0126367 +time/training (s) 18.1851 +time/epoch (s) 29.6166 +time/total (s) 7654.26 +Epoch -607 +------------------------------ ---------------- +2022-05-15 20:10:31.181261 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -606 finished +------------------------------ ---------------- +epoch -606 +replay_buffer/size 999047 +trainer/num train calls 395000 +trainer/QF1 Loss 0.447125 +trainer/QF2 Loss 0.540438 +trainer/Policy Loss 24.7463 +trainer/Q1 Predictions Mean -74.127 +trainer/Q1 Predictions Std 18.1427 +trainer/Q1 Predictions Max -0.627328 +trainer/Q1 Predictions Min -86.3882 +trainer/Q2 Predictions Mean -74.1225 +trainer/Q2 Predictions Std 18.1621 +trainer/Q2 Predictions Max -0.659404 +trainer/Q2 Predictions Min -86.7098 +trainer/Q Targets Mean -74.0163 +trainer/Q Targets Std 18.1429 +trainer/Q Targets Max 0.97109 +trainer/Q Targets Min -86.3807 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00246262 +trainer/policy/mean Std 0.709681 +trainer/policy/mean Max 0.999268 +trainer/policy/mean Min -0.999532 +trainer/policy/std Mean 0.424589 +trainer/policy/std Std 0.0208962 +trainer/policy/std Max 0.446168 +trainer/policy/std Min 0.385875 +trainer/Advantage Weights Mean 5.53142 +trainer/Advantage Weights Std 19.7412 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.04647e-21 +trainer/Advantage Score Mean -0.258241 +trainer/Advantage Score Std 0.547404 +trainer/Advantage Score Max 1.7289 +trainer/Advantage Score Min -4.76382 +trainer/V1 Predictions Mean -73.8415 +trainer/V1 Predictions Std 18.099 +trainer/V1 Predictions Max -0.900952 +trainer/V1 Predictions Min -86.2899 +trainer/VF Loss 0.0713387 +expl/num steps total 395000 +expl/num paths total 464 +expl/path length Mean 500 +expl/path length Std 288 +expl/path length Max 788 +expl/path length Min 212 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0579034 +expl/Actions Std 0.813916 +expl/Actions Max 2.62048 +expl/Actions Min -2.2066 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 381287 +eval/num paths total 395 +eval/path length Mean 642 +eval/path length Std 0 +eval/path length Max 642 +eval/path length Min 642 +eval/Rewards Mean 0.00155763 +eval/Rewards Std 0.0394361 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0241297 +eval/Actions Std 0.711735 +eval/Actions Max 0.999231 +eval/Actions Min -0.998888 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.17419e-06 +time/evaluation sampling (s) 4.36698 +time/exploration sampling (s) 6.63295 +time/logging (s) 0.00970735 +time/saving (s) 0.0160941 +time/training (s) 19.17 +time/epoch (s) 30.1957 +time/total (s) 7684.46 +Epoch -606 +------------------------------ ---------------- +2022-05-15 20:11:02.094778 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -605 finished +------------------------------ ---------------- +epoch -605 +replay_buffer/size 999047 +trainer/num train calls 396000 +trainer/QF1 Loss 0.799091 +trainer/QF2 Loss 0.704586 +trainer/Policy Loss 42.5007 +trainer/Q1 Predictions Mean -74.0705 +trainer/Q1 Predictions Std 17.5065 +trainer/Q1 Predictions Max -0.616634 +trainer/Q1 Predictions Min -86.332 +trainer/Q2 Predictions Mean -74.1346 +trainer/Q2 Predictions Std 17.3866 +trainer/Q2 Predictions Max 0.573459 +trainer/Q2 Predictions Min -86.2268 +trainer/Q Targets Mean -74.3387 +trainer/Q Targets Std 17.0176 +trainer/Q Targets Max -0.640852 +trainer/Q Targets Min -86.5228 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000478838 +trainer/policy/mean Std 0.712722 +trainer/policy/mean Max 0.998245 +trainer/policy/mean Min -0.998539 +trainer/policy/std Mean 0.423385 +trainer/policy/std Std 0.0199763 +trainer/policy/std Max 0.445358 +trainer/policy/std Min 0.389618 +trainer/Advantage Weights Mean 8.27675 +trainer/Advantage Weights Std 24.8373 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.22601e-30 +trainer/Advantage Score Mean -0.256222 +trainer/Advantage Score Std 0.679669 +trainer/Advantage Score Max 2.11006 +trainer/Advantage Score Min -6.88738 +trainer/V1 Predictions Mean -74.0099 +trainer/V1 Predictions Std 17.4196 +trainer/V1 Predictions Max 0.485761 +trainer/V1 Predictions Min -86.406 +trainer/VF Loss 0.1098 +expl/num steps total 396000 +expl/num paths total 465 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.103911 +expl/Actions Std 0.860531 +expl/Actions Max 2.45168 +expl/Actions Min -2.40712 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 382287 +eval/num paths total 396 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.111088 +eval/Actions Std 0.714369 +eval/Actions Max 0.999111 +eval/Actions Min -0.998953 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.22096e-05 +time/evaluation sampling (s) 4.9398 +time/exploration sampling (s) 6.88832 +time/logging (s) 0.0118625 +time/saving (s) 0.0163446 +time/training (s) 19.0465 +time/epoch (s) 30.9029 +time/total (s) 7715.37 +Epoch -605 +------------------------------ ---------------- +2022-05-15 20:11:34.186600 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -604 finished +------------------------------ ---------------- +epoch -604 +replay_buffer/size 999047 +trainer/num train calls 397000 +trainer/QF1 Loss 0.738805 +trainer/QF2 Loss 0.813566 +trainer/Policy Loss 49.8679 +trainer/Q1 Predictions Mean -73.3582 +trainer/Q1 Predictions Std 18.8555 +trainer/Q1 Predictions Max -2.12611 +trainer/Q1 Predictions Min -86.4423 +trainer/Q2 Predictions Mean -73.4836 +trainer/Q2 Predictions Std 18.8439 +trainer/Q2 Predictions Max -1.81021 +trainer/Q2 Predictions Min -86.5174 +trainer/Q Targets Mean -73.6931 +trainer/Q Targets Std 19.169 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.01 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0140984 +trainer/policy/mean Std 0.714953 +trainer/policy/mean Max 0.998204 +trainer/policy/mean Min -0.999374 +trainer/policy/std Mean 0.42273 +trainer/policy/std Std 0.0213433 +trainer/policy/std Max 0.444628 +trainer/policy/std Min 0.385815 +trainer/Advantage Weights Mean 7.54772 +trainer/Advantage Weights Std 21.8171 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.81613e-11 +trainer/Advantage Score Mean -0.181263 +trainer/Advantage Score Std 0.451136 +trainer/Advantage Score Max 1.75032 +trainer/Advantage Score Min -2.35678 +trainer/V1 Predictions Mean -73.5054 +trainer/V1 Predictions Std 19.1861 +trainer/V1 Predictions Max -0.379977 +trainer/V1 Predictions Min -86.3916 +trainer/VF Loss 0.0561084 +expl/num steps total 397000 +expl/num paths total 466 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0306366 +expl/Actions Std 0.857842 +expl/Actions Max 2.32588 +expl/Actions Min -2.45277 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 383287 +eval/num paths total 397 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0855635 +eval/Actions Std 0.668035 +eval/Actions Max 0.999904 +eval/Actions Min -0.999661 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.94788e-06 +time/evaluation sampling (s) 5.14653 +time/exploration sampling (s) 7.47347 +time/logging (s) 0.0122158 +time/saving (s) 0.0183924 +time/training (s) 19.4286 +time/epoch (s) 32.0793 +time/total (s) 7747.45 +Epoch -604 +------------------------------ ---------------- +2022-05-15 20:12:05.572580 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -603 finished +------------------------------ ---------------- +epoch -603 +replay_buffer/size 999047 +trainer/num train calls 398000 +trainer/QF1 Loss 0.712329 +trainer/QF2 Loss 0.645703 +trainer/Policy Loss 26.2817 +trainer/Q1 Predictions Mean -71.8318 +trainer/Q1 Predictions Std 20.1418 +trainer/Q1 Predictions Max -0.891968 +trainer/Q1 Predictions Min -87.7005 +trainer/Q2 Predictions Mean -71.8165 +trainer/Q2 Predictions Std 20.203 +trainer/Q2 Predictions Max -1.05208 +trainer/Q2 Predictions Min -87.2884 +trainer/Q Targets Mean -71.8912 +trainer/Q Targets Std 20.3364 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6998 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0147242 +trainer/policy/mean Std 0.734349 +trainer/policy/mean Max 0.999205 +trainer/policy/mean Min -0.996912 +trainer/policy/std Mean 0.424592 +trainer/policy/std Std 0.0202163 +trainer/policy/std Max 0.444157 +trainer/policy/std Min 0.391027 +trainer/Advantage Weights Mean 3.75897 +trainer/Advantage Weights Std 16.8369 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.05358e-10 +trainer/Advantage Score Mean -0.371994 +trainer/Advantage Score Std 0.455008 +trainer/Advantage Score Max 1.28607 +trainer/Advantage Score Min -2.14058 +trainer/V1 Predictions Mean -71.6959 +trainer/V1 Predictions Std 20.2272 +trainer/V1 Predictions Max -0.505185 +trainer/V1 Predictions Min -86.7394 +trainer/VF Loss 0.0506369 +expl/num steps total 398000 +expl/num paths total 467 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.150244 +expl/Actions Std 0.807981 +expl/Actions Max 2.42034 +expl/Actions Min -2.23635 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 384287 +eval/num paths total 398 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00602308 +eval/Actions Std 0.725228 +eval/Actions Max 0.999128 +eval/Actions Min -0.99927 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.1377e-05 +time/evaluation sampling (s) 4.69837 +time/exploration sampling (s) 7.57903 +time/logging (s) 0.0115752 +time/saving (s) 0.0150745 +time/training (s) 19.0668 +time/epoch (s) 31.3709 +time/total (s) 7778.83 +Epoch -603 +------------------------------ ---------------- +2022-05-15 20:12:38.175339 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -602 finished +------------------------------ ---------------- +epoch -602 +replay_buffer/size 999047 +trainer/num train calls 399000 +trainer/QF1 Loss 1.38336 +trainer/QF2 Loss 1.34081 +trainer/Policy Loss 19.1121 +trainer/Q1 Predictions Mean -74.7731 +trainer/Q1 Predictions Std 14.8346 +trainer/Q1 Predictions Max -1.10559 +trainer/Q1 Predictions Min -85.6983 +trainer/Q2 Predictions Mean -74.6874 +trainer/Q2 Predictions Std 14.8605 +trainer/Q2 Predictions Max -0.791431 +trainer/Q2 Predictions Min -85.6478 +trainer/Q Targets Mean -75.1843 +trainer/Q Targets Std 15.1712 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3678 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0151007 +trainer/policy/mean Std 0.721646 +trainer/policy/mean Max 0.99933 +trainer/policy/mean Min -0.999242 +trainer/policy/std Mean 0.425013 +trainer/policy/std Std 0.0214673 +trainer/policy/std Max 0.446977 +trainer/policy/std Min 0.39113 +trainer/Advantage Weights Mean 5.31682 +trainer/Advantage Weights Std 19.2945 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.16274e-11 +trainer/Advantage Score Mean -0.288974 +trainer/Advantage Score Std 0.46462 +trainer/Advantage Score Max 0.816349 +trainer/Advantage Score Min -2.4177 +trainer/V1 Predictions Mean -74.9298 +trainer/V1 Predictions Std 15.157 +trainer/V1 Predictions Max 1.0741 +trainer/V1 Predictions Min -86.334 +trainer/VF Loss 0.0456175 +expl/num steps total 399000 +expl/num paths total 469 +expl/path length Mean 500 +expl/path length Std 404 +expl/path length Max 904 +expl/path length Min 96 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00433548 +expl/Actions Std 0.827199 +expl/Actions Max 2.42093 +expl/Actions Min -2.62936 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 384958 +eval/num paths total 399 +eval/path length Mean 671 +eval/path length Std 0 +eval/path length Max 671 +eval/path length Min 671 +eval/Rewards Mean 0.00149031 +eval/Rewards Std 0.0385758 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0378074 +eval/Actions Std 0.724209 +eval/Actions Max 0.999264 +eval/Actions Min -0.999321 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.28923e-05 +time/evaluation sampling (s) 5.13578 +time/exploration sampling (s) 7.62349 +time/logging (s) 0.0109827 +time/saving (s) 0.0187095 +time/training (s) 19.802 +time/epoch (s) 32.5909 +time/total (s) 7811.43 +Epoch -602 +------------------------------ ---------------- +2022-05-15 20:13:10.091325 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -601 finished +------------------------------ ---------------- +epoch -601 +replay_buffer/size 999047 +trainer/num train calls 400000 +trainer/QF1 Loss 0.605814 +trainer/QF2 Loss 0.50902 +trainer/Policy Loss 9.17615 +trainer/Q1 Predictions Mean -75.0342 +trainer/Q1 Predictions Std 15.3699 +trainer/Q1 Predictions Max -4.86699 +trainer/Q1 Predictions Min -87.6881 +trainer/Q2 Predictions Mean -74.986 +trainer/Q2 Predictions Std 15.3262 +trainer/Q2 Predictions Max -5.14356 +trainer/Q2 Predictions Min -87.574 +trainer/Q Targets Mean -74.9855 +trainer/Q Targets Std 15.4169 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.93 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00742622 +trainer/policy/mean Std 0.714325 +trainer/policy/mean Max 0.998685 +trainer/policy/mean Min -0.998871 +trainer/policy/std Mean 0.426316 +trainer/policy/std Std 0.0225953 +trainer/policy/std Max 0.448658 +trainer/policy/std Min 0.387184 +trainer/Advantage Weights Mean 2.73564 +trainer/Advantage Weights Std 12.8469 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.2047e-15 +trainer/Advantage Score Mean -0.332475 +trainer/Advantage Score Std 0.599376 +trainer/Advantage Score Max 1.80198 +trainer/Advantage Score Min -3.33742 +trainer/V1 Predictions Mean -74.6824 +trainer/V1 Predictions Std 15.5158 +trainer/V1 Predictions Max -4.78088 +trainer/V1 Predictions Min -87.3861 +trainer/VF Loss 0.0692519 +expl/num steps total 400000 +expl/num paths total 471 +expl/path length Mean 500 +expl/path length Std 394 +expl/path length Max 894 +expl/path length Min 106 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0489019 +expl/Actions Std 0.825578 +expl/Actions Max 2.51998 +expl/Actions Min -2.37428 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 385958 +eval/num paths total 400 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0212798 +eval/Actions Std 0.668575 +eval/Actions Max 0.999658 +eval/Actions Min -0.999134 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.18674e-05 +time/evaluation sampling (s) 4.81519 +time/exploration sampling (s) 7.3983 +time/logging (s) 0.0121062 +time/saving (s) 0.0175176 +time/training (s) 19.6588 +time/epoch (s) 31.902 +time/total (s) 7843.34 +Epoch -601 +------------------------------ ---------------- +2022-05-15 20:13:41.186994 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -600 finished +------------------------------ ---------------- +epoch -600 +replay_buffer/size 999047 +trainer/num train calls 401000 +trainer/QF1 Loss 0.582591 +trainer/QF2 Loss 0.586883 +trainer/Policy Loss 36.5027 +trainer/Q1 Predictions Mean -74.3387 +trainer/Q1 Predictions Std 16.1989 +trainer/Q1 Predictions Max -1.30987 +trainer/Q1 Predictions Min -86.341 +trainer/Q2 Predictions Mean -74.2237 +trainer/Q2 Predictions Std 16.2393 +trainer/Q2 Predictions Max -1.40516 +trainer/Q2 Predictions Min -86.189 +trainer/Q Targets Mean -74.4818 +trainer/Q Targets Std 16.435 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3778 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000746043 +trainer/policy/mean Std 0.728623 +trainer/policy/mean Max 0.999496 +trainer/policy/mean Min -0.998474 +trainer/policy/std Mean 0.424069 +trainer/policy/std Std 0.0220809 +trainer/policy/std Max 0.448555 +trainer/policy/std Min 0.388688 +trainer/Advantage Weights Mean 10.431 +trainer/Advantage Weights Std 26.2102 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.21603e-07 +trainer/Advantage Score Mean -0.0840567 +trainer/Advantage Score Std 0.443216 +trainer/Advantage Score Max 1.8303 +trainer/Advantage Score Min -1.59225 +trainer/V1 Predictions Mean -74.2556 +trainer/V1 Predictions Std 16.398 +trainer/V1 Predictions Max -1.46295 +trainer/V1 Predictions Min -86.2131 +trainer/VF Loss 0.072629 +expl/num steps total 401000 +expl/num paths total 473 +expl/path length Mean 500 +expl/path length Std 204 +expl/path length Max 704 +expl/path length Min 296 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0298385 +expl/Actions Std 0.828674 +expl/Actions Max 2.28849 +expl/Actions Min -2.55414 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 386676 +eval/num paths total 401 +eval/path length Mean 718 +eval/path length Std 0 +eval/path length Max 718 +eval/path length Min 718 +eval/Rewards Mean 0.00139276 +eval/Rewards Std 0.0372937 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0344475 +eval/Actions Std 0.72434 +eval/Actions Max 0.999696 +eval/Actions Min -0.999117 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.584e-06 +time/evaluation sampling (s) 5.14312 +time/exploration sampling (s) 7.16295 +time/logging (s) 0.00670305 +time/saving (s) 0.0214004 +time/training (s) 18.7418 +time/epoch (s) 31.0759 +time/total (s) 7874.42 +Epoch -600 +------------------------------ ---------------- +2022-05-15 20:14:13.307163 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -599 finished +------------------------------ ---------------- +epoch -599 +replay_buffer/size 999047 +trainer/num train calls 402000 +trainer/QF1 Loss 0.652653 +trainer/QF2 Loss 0.700846 +trainer/Policy Loss 26.3462 +trainer/Q1 Predictions Mean -73.2203 +trainer/Q1 Predictions Std 19.086 +trainer/Q1 Predictions Max -0.9792 +trainer/Q1 Predictions Min -86.8902 +trainer/Q2 Predictions Mean -73.1904 +trainer/Q2 Predictions Std 19.0844 +trainer/Q2 Predictions Max -0.772698 +trainer/Q2 Predictions Min -86.3072 +trainer/Q Targets Mean -73.5988 +trainer/Q Targets Std 18.7371 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8645 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0116385 +trainer/policy/mean Std 0.710529 +trainer/policy/mean Max 0.998185 +trainer/policy/mean Min -0.998579 +trainer/policy/std Mean 0.424086 +trainer/policy/std Std 0.0216856 +trainer/policy/std Max 0.447652 +trainer/policy/std Min 0.389519 +trainer/Advantage Weights Mean 6.53822 +trainer/Advantage Weights Std 21.668 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.04778e-15 +trainer/Advantage Score Mean -0.285079 +trainer/Advantage Score Std 0.508879 +trainer/Advantage Score Max 0.958864 +trainer/Advantage Score Min -3.3822 +trainer/V1 Predictions Mean -73.3454 +trainer/V1 Predictions Std 18.923 +trainer/V1 Predictions Max -0.988649 +trainer/V1 Predictions Min -87.05 +trainer/VF Loss 0.0552783 +expl/num steps total 402000 +expl/num paths total 475 +expl/path length Mean 500 +expl/path length Std 153 +expl/path length Max 653 +expl/path length Min 347 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0293144 +expl/Actions Std 0.826668 +expl/Actions Max 2.39506 +expl/Actions Min -2.50365 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 387187 +eval/num paths total 402 +eval/path length Mean 511 +eval/path length Std 0 +eval/path length Max 511 +eval/path length Min 511 +eval/Rewards Mean 0.00195695 +eval/Rewards Std 0.0441941 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0293939 +eval/Actions Std 0.726561 +eval/Actions Max 0.998899 +eval/Actions Min -0.999196 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.1317e-05 +time/evaluation sampling (s) 5.29689 +time/exploration sampling (s) 6.64177 +time/logging (s) 0.00980389 +time/saving (s) 0.0158865 +time/training (s) 20.1501 +time/epoch (s) 32.1144 +time/total (s) 7906.54 +Epoch -599 +------------------------------ ---------------- +2022-05-15 20:14:45.510671 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -598 finished +------------------------------ ---------------- +epoch -598 +replay_buffer/size 999047 +trainer/num train calls 403000 +trainer/QF1 Loss 0.814364 +trainer/QF2 Loss 0.845213 +trainer/Policy Loss 6.15917 +trainer/Q1 Predictions Mean -75.2071 +trainer/Q1 Predictions Std 15.0055 +trainer/Q1 Predictions Max -8.85387 +trainer/Q1 Predictions Min -87.3065 +trainer/Q2 Predictions Mean -75.2399 +trainer/Q2 Predictions Std 15.0114 +trainer/Q2 Predictions Max -8.30145 +trainer/Q2 Predictions Min -87.3878 +trainer/Q Targets Mean -75.0014 +trainer/Q Targets Std 15.2538 +trainer/Q Targets Max -5.7471 +trainer/Q Targets Min -86.376 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0238595 +trainer/policy/mean Std 0.714563 +trainer/policy/mean Max 0.99837 +trainer/policy/mean Min -0.99813 +trainer/policy/std Mean 0.424172 +trainer/policy/std Std 0.0210011 +trainer/policy/std Max 0.446517 +trainer/policy/std Min 0.391662 +trainer/Advantage Weights Mean 2.09276 +trainer/Advantage Weights Std 11.1962 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.84583e-16 +trainer/Advantage Score Mean -0.39361 +trainer/Advantage Score Std 0.58916 +trainer/Advantage Score Max 0.724976 +trainer/Advantage Score Min -3.47814 +trainer/V1 Predictions Mean -74.6753 +trainer/V1 Predictions Std 15.4848 +trainer/V1 Predictions Max -5.30469 +trainer/V1 Predictions Min -86.3257 +trainer/VF Loss 0.0561711 +expl/num steps total 403000 +expl/num paths total 476 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0419796 +expl/Actions Std 0.83565 +expl/Actions Max 2.70702 +expl/Actions Min -2.54404 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 388187 +eval/num paths total 403 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0313206 +eval/Actions Std 0.707381 +eval/Actions Max 0.99857 +eval/Actions Min -0.9987 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.7868e-06 +time/evaluation sampling (s) 4.7675 +time/exploration sampling (s) 7.07057 +time/logging (s) 0.00864853 +time/saving (s) 0.0164312 +time/training (s) 20.3303 +time/epoch (s) 32.1935 +time/total (s) 7938.74 +Epoch -598 +------------------------------ ---------------- +2022-05-15 20:15:16.931884 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -597 finished +------------------------------ ---------------- +epoch -597 +replay_buffer/size 999047 +trainer/num train calls 404000 +trainer/QF1 Loss 0.890437 +trainer/QF2 Loss 0.846288 +trainer/Policy Loss 11.1885 +trainer/Q1 Predictions Mean -72.3684 +trainer/Q1 Predictions Std 20.2062 +trainer/Q1 Predictions Max -0.322085 +trainer/Q1 Predictions Min -85.9003 +trainer/Q2 Predictions Mean -72.2926 +trainer/Q2 Predictions Std 20.1937 +trainer/Q2 Predictions Max -0.312436 +trainer/Q2 Predictions Min -86.077 +trainer/Q Targets Mean -72.0365 +trainer/Q Targets Std 20.5007 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1175 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00623973 +trainer/policy/mean Std 0.700704 +trainer/policy/mean Max 0.99816 +trainer/policy/mean Min -0.997882 +trainer/policy/std Mean 0.42524 +trainer/policy/std Std 0.0203083 +trainer/policy/std Max 0.446744 +trainer/policy/std Min 0.391453 +trainer/Advantage Weights Mean 3.00573 +trainer/Advantage Weights Std 15.3703 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.03986e-20 +trainer/Advantage Score Mean -0.520721 +trainer/Advantage Score Std 0.701547 +trainer/Advantage Score Max 1.45254 +trainer/Advantage Score Min -4.53388 +trainer/V1 Predictions Mean -71.7841 +trainer/V1 Predictions Std 20.5511 +trainer/V1 Predictions Max 0.976434 +trainer/V1 Predictions Min -85.978 +trainer/VF Loss 0.0932814 +expl/num steps total 404000 +expl/num paths total 478 +expl/path length Mean 500 +expl/path length Std 383 +expl/path length Max 883 +expl/path length Min 117 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0186043 +expl/Actions Std 0.817998 +expl/Actions Max 2.41102 +expl/Actions Min -2.37198 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 389187 +eval/num paths total 404 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.147314 +eval/Actions Std 0.75009 +eval/Actions Max 0.999388 +eval/Actions Min -0.999077 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2483e-05 +time/evaluation sampling (s) 4.35153 +time/exploration sampling (s) 7.46152 +time/logging (s) 0.0112567 +time/saving (s) 0.0148749 +time/training (s) 19.576 +time/epoch (s) 31.4152 +time/total (s) 7970.16 +Epoch -597 +------------------------------ ---------------- +2022-05-15 20:15:48.613120 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -596 finished +------------------------------ ---------------- +epoch -596 +replay_buffer/size 999047 +trainer/num train calls 405000 +trainer/QF1 Loss 0.707094 +trainer/QF2 Loss 0.609737 +trainer/Policy Loss 19.2115 +trainer/Q1 Predictions Mean -74.7317 +trainer/Q1 Predictions Std 15.3255 +trainer/Q1 Predictions Max -1.37877 +trainer/Q1 Predictions Min -86.2371 +trainer/Q2 Predictions Mean -74.7191 +trainer/Q2 Predictions Std 15.3008 +trainer/Q2 Predictions Max -0.832755 +trainer/Q2 Predictions Min -86.3091 +trainer/Q Targets Mean -74.8318 +trainer/Q Targets Std 15.207 +trainer/Q Targets Max -0.394758 +trainer/Q Targets Min -86.3551 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00340211 +trainer/policy/mean Std 0.711747 +trainer/policy/mean Max 0.999075 +trainer/policy/mean Min -0.997603 +trainer/policy/std Mean 0.424101 +trainer/policy/std Std 0.021159 +trainer/policy/std Max 0.443472 +trainer/policy/std Min 0.388669 +trainer/Advantage Weights Mean 4.92392 +trainer/Advantage Weights Std 17.0037 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.2427e-13 +trainer/Advantage Score Mean -0.279141 +trainer/Advantage Score Std 0.492494 +trainer/Advantage Score Max 0.764923 +trainer/Advantage Score Min -2.87572 +trainer/V1 Predictions Mean -74.5644 +trainer/V1 Predictions Std 15.5279 +trainer/V1 Predictions Max 1.64239 +trainer/V1 Predictions Min -86.2148 +trainer/VF Loss 0.0471247 +expl/num steps total 405000 +expl/num paths total 479 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0450214 +expl/Actions Std 0.821054 +expl/Actions Max 2.30948 +expl/Actions Min -2.49929 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 390187 +eval/num paths total 405 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.045074 +eval/Actions Std 0.726251 +eval/Actions Max 0.999848 +eval/Actions Min -0.998533 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.04359e-05 +time/evaluation sampling (s) 4.88606 +time/exploration sampling (s) 7.48099 +time/logging (s) 0.0113743 +time/saving (s) 0.0134205 +time/training (s) 19.2765 +time/epoch (s) 31.6683 +time/total (s) 8001.83 +Epoch -596 +------------------------------ ---------------- +2022-05-15 20:16:20.273042 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -595 finished +------------------------------ ---------------- +epoch -595 +replay_buffer/size 999047 +trainer/num train calls 406000 +trainer/QF1 Loss 0.991499 +trainer/QF2 Loss 0.803479 +trainer/Policy Loss 15.4957 +trainer/Q1 Predictions Mean -72.3235 +trainer/Q1 Predictions Std 19.4521 +trainer/Q1 Predictions Max -1.43249 +trainer/Q1 Predictions Min -87.6561 +trainer/Q2 Predictions Mean -72.3806 +trainer/Q2 Predictions Std 19.4047 +trainer/Q2 Predictions Max -1.27043 +trainer/Q2 Predictions Min -88.5202 +trainer/Q Targets Mean -72.5693 +trainer/Q Targets Std 19.0099 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.8046 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000154791 +trainer/policy/mean Std 0.718591 +trainer/policy/mean Max 0.999321 +trainer/policy/mean Min -0.999395 +trainer/policy/std Mean 0.424029 +trainer/policy/std Std 0.0202029 +trainer/policy/std Max 0.443257 +trainer/policy/std Min 0.38866 +trainer/Advantage Weights Mean 5.01633 +trainer/Advantage Weights Std 19.4054 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.90649e-18 +trainer/Advantage Score Mean -0.318144 +trainer/Advantage Score Std 0.518316 +trainer/Advantage Score Max 1.63717 +trainer/Advantage Score Min -4.08013 +trainer/V1 Predictions Mean -72.2807 +trainer/V1 Predictions Std 19.1568 +trainer/V1 Predictions Max -2.051 +trainer/V1 Predictions Min -87.508 +trainer/VF Loss 0.0658192 +expl/num steps total 406000 +expl/num paths total 480 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0161088 +expl/Actions Std 0.845597 +expl/Actions Max 2.47944 +expl/Actions Min -2.36675 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 391187 +eval/num paths total 406 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.467855 +eval/Actions Std 0.619019 +eval/Actions Max 0.999208 +eval/Actions Min -0.999766 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.25323e-05 +time/evaluation sampling (s) 4.95601 +time/exploration sampling (s) 7.25824 +time/logging (s) 0.0121773 +time/saving (s) 0.0177477 +time/training (s) 19.4025 +time/epoch (s) 31.6467 +time/total (s) 8033.49 +Epoch -595 +------------------------------ ---------------- +2022-05-15 20:16:51.474417 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -594 finished +------------------------------ ---------------- +epoch -594 +replay_buffer/size 999047 +trainer/num train calls 407000 +trainer/QF1 Loss 1.08867 +trainer/QF2 Loss 0.957808 +trainer/Policy Loss 8.57991 +trainer/Q1 Predictions Mean -74.8644 +trainer/Q1 Predictions Std 15.8561 +trainer/Q1 Predictions Max -1.48279 +trainer/Q1 Predictions Min -85.6342 +trainer/Q2 Predictions Mean -74.9222 +trainer/Q2 Predictions Std 15.8816 +trainer/Q2 Predictions Max -0.296016 +trainer/Q2 Predictions Min -85.8067 +trainer/Q Targets Mean -75.3269 +trainer/Q Targets Std 16.077 +trainer/Q Targets Max -0.253878 +trainer/Q Targets Min -86.1206 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0290559 +trainer/policy/mean Std 0.727036 +trainer/policy/mean Max 0.999547 +trainer/policy/mean Min -0.999537 +trainer/policy/std Mean 0.4239 +trainer/policy/std Std 0.0207727 +trainer/policy/std Max 0.445094 +trainer/policy/std Min 0.388458 +trainer/Advantage Weights Mean 2.62387 +trainer/Advantage Weights Std 13.5295 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.43754e-21 +trainer/Advantage Score Mean -0.388762 +trainer/Advantage Score Std 0.617018 +trainer/Advantage Score Max 0.931335 +trainer/Advantage Score Min -4.79913 +trainer/V1 Predictions Mean -75.1366 +trainer/V1 Predictions Std 16.2781 +trainer/V1 Predictions Max -0.0012399 +trainer/V1 Predictions Min -85.9951 +trainer/VF Loss 0.0618357 +expl/num steps total 407000 +expl/num paths total 482 +expl/path length Mean 500 +expl/path length Std 432 +expl/path length Max 932 +expl/path length Min 68 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0225281 +expl/Actions Std 0.833691 +expl/Actions Max 2.32206 +expl/Actions Min -2.37909 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 392126 +eval/num paths total 408 +eval/path length Mean 469.5 +eval/path length Std 2.5 +eval/path length Max 472 +eval/path length Min 467 +eval/Rewards Mean 0.00212993 +eval/Rewards Std 0.0461019 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0230701 +eval/Actions Std 0.737498 +eval/Actions Max 0.999419 +eval/Actions Min -0.99934 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 4.0587e-06 +time/evaluation sampling (s) 4.94901 +time/exploration sampling (s) 6.43717 +time/logging (s) 0.0112807 +time/saving (s) 0.0172771 +time/training (s) 19.7711 +time/epoch (s) 31.1858 +time/total (s) 8064.68 +Epoch -594 +------------------------------ ---------------- +2022-05-15 20:17:23.293469 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -593 finished +------------------------------ ---------------- +epoch -593 +replay_buffer/size 999047 +trainer/num train calls 408000 +trainer/QF1 Loss 1.014 +trainer/QF2 Loss 1.05093 +trainer/Policy Loss 36.6082 +trainer/Q1 Predictions Mean -74.7993 +trainer/Q1 Predictions Std 14.5557 +trainer/Q1 Predictions Max -2.00703 +trainer/Q1 Predictions Min -86.4585 +trainer/Q2 Predictions Mean -74.7586 +trainer/Q2 Predictions Std 14.6711 +trainer/Q2 Predictions Max -1.6229 +trainer/Q2 Predictions Min -86.279 +trainer/Q Targets Mean -74.9344 +trainer/Q Targets Std 14.6307 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2023 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0267346 +trainer/policy/mean Std 0.712325 +trainer/policy/mean Max 0.999936 +trainer/policy/mean Min -0.998979 +trainer/policy/std Mean 0.423741 +trainer/policy/std Std 0.0200479 +trainer/policy/std Max 0.443804 +trainer/policy/std Min 0.390267 +trainer/Advantage Weights Mean 7.52372 +trainer/Advantage Weights Std 22.4256 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.2008e-19 +trainer/Advantage Score Mean -0.22113 +trainer/Advantage Score Std 0.612879 +trainer/Advantage Score Max 0.960396 +trainer/Advantage Score Min -4.19244 +trainer/V1 Predictions Mean -74.6683 +trainer/V1 Predictions Std 14.7852 +trainer/V1 Predictions Max -0.464536 +trainer/V1 Predictions Min -85.9708 +trainer/VF Loss 0.0700192 +expl/num steps total 408000 +expl/num paths total 484 +expl/path length Mean 500 +expl/path length Std 234 +expl/path length Max 734 +expl/path length Min 266 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.027156 +expl/Actions Std 0.83227 +expl/Actions Max 2.25859 +expl/Actions Min -2.26717 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 392979 +eval/num paths total 409 +eval/path length Mean 853 +eval/path length Std 0 +eval/path length Max 853 +eval/path length Min 853 +eval/Rewards Mean 0.00117233 +eval/Rewards Std 0.0342193 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0379005 +eval/Actions Std 0.729745 +eval/Actions Max 0.999079 +eval/Actions Min -0.999445 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.20096e-06 +time/evaluation sampling (s) 4.66957 +time/exploration sampling (s) 7.31755 +time/logging (s) 0.0118299 +time/saving (s) 0.0190818 +time/training (s) 19.7876 +time/epoch (s) 31.8056 +time/total (s) 8096.49 +Epoch -593 +------------------------------ ---------------- +2022-05-15 20:17:55.199572 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -592 finished +------------------------------ ---------------- +epoch -592 +replay_buffer/size 999047 +trainer/num train calls 409000 +trainer/QF1 Loss 0.703475 +trainer/QF2 Loss 0.718375 +trainer/Policy Loss 10.7806 +trainer/Q1 Predictions Mean -73.504 +trainer/Q1 Predictions Std 17.2018 +trainer/Q1 Predictions Max -0.371896 +trainer/Q1 Predictions Min -85.8808 +trainer/Q2 Predictions Mean -73.5183 +trainer/Q2 Predictions Std 17.2483 +trainer/Q2 Predictions Max 0.112364 +trainer/Q2 Predictions Min -86.121 +trainer/Q Targets Mean -73.6524 +trainer/Q Targets Std 17.5311 +trainer/Q Targets Max -0.226899 +trainer/Q Targets Min -86.4265 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.017519 +trainer/policy/mean Std 0.716055 +trainer/policy/mean Max 0.99945 +trainer/policy/mean Min -0.99899 +trainer/policy/std Mean 0.422477 +trainer/policy/std Std 0.0208859 +trainer/policy/std Max 0.442942 +trainer/policy/std Min 0.388553 +trainer/Advantage Weights Mean 1.81241 +trainer/Advantage Weights Std 10.4337 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.88318e-16 +trainer/Advantage Score Mean -0.461701 +trainer/Advantage Score Std 0.559574 +trainer/Advantage Score Max 1.18649 +trainer/Advantage Score Min -3.49123 +trainer/V1 Predictions Mean -73.4341 +trainer/V1 Predictions Std 17.5341 +trainer/V1 Predictions Max 1.24147 +trainer/V1 Predictions Min -86.273 +trainer/VF Loss 0.0605599 +expl/num steps total 409000 +expl/num paths total 486 +expl/path length Mean 500 +expl/path length Std 7 +expl/path length Max 507 +expl/path length Min 493 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0252236 +expl/Actions Std 0.815988 +expl/Actions Max 2.19224 +expl/Actions Min -2.16945 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 393979 +eval/num paths total 410 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.264371 +eval/Actions Std 0.747301 +eval/Actions Max 0.998947 +eval/Actions Min -0.99962 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0544e-05 +time/evaluation sampling (s) 4.99973 +time/exploration sampling (s) 7.4163 +time/logging (s) 0.0127728 +time/saving (s) 0.0201248 +time/training (s) 19.4434 +time/epoch (s) 31.8923 +time/total (s) 8128.39 +Epoch -592 +------------------------------ ---------------- +2022-05-15 20:18:27.687779 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -591 finished +------------------------------ ---------------- +epoch -591 +replay_buffer/size 999047 +trainer/num train calls 410000 +trainer/QF1 Loss 1.44045 +trainer/QF2 Loss 1.37583 +trainer/Policy Loss 19.4908 +trainer/Q1 Predictions Mean -72.5909 +trainer/Q1 Predictions Std 18.5704 +trainer/Q1 Predictions Max -1.55334 +trainer/Q1 Predictions Min -85.5765 +trainer/Q2 Predictions Mean -72.5732 +trainer/Q2 Predictions Std 18.6702 +trainer/Q2 Predictions Max -1.48703 +trainer/Q2 Predictions Min -85.806 +trainer/Q Targets Mean -72.9167 +trainer/Q Targets Std 18.7562 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9873 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0308612 +trainer/policy/mean Std 0.731539 +trainer/policy/mean Max 0.999242 +trainer/policy/mean Min -0.997329 +trainer/policy/std Mean 0.422976 +trainer/policy/std Std 0.0215356 +trainer/policy/std Max 0.445154 +trainer/policy/std Min 0.385962 +trainer/Advantage Weights Mean 4.00089 +trainer/Advantage Weights Std 16.9559 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.92942e-16 +trainer/Advantage Score Mean -0.303022 +trainer/Advantage Score Std 0.559913 +trainer/Advantage Score Max 1.6833 +trainer/Advantage Score Min -3.54729 +trainer/V1 Predictions Mean -72.6551 +trainer/V1 Predictions Std 18.7603 +trainer/V1 Predictions Max -2.24814 +trainer/V1 Predictions Min -85.8302 +trainer/VF Loss 0.0719042 +expl/num steps total 410000 +expl/num paths total 488 +expl/path length Mean 500 +expl/path length Std 255 +expl/path length Max 755 +expl/path length Min 245 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0147265 +expl/Actions Std 0.813635 +expl/Actions Max 2.47567 +expl/Actions Min -2.24779 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 394979 +eval/num paths total 411 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0638374 +eval/Actions Std 0.571977 +eval/Actions Max 0.998024 +eval/Actions Min -0.999346 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.25901e-05 +time/evaluation sampling (s) 5.37773 +time/exploration sampling (s) 7.58308 +time/logging (s) 0.00950184 +time/saving (s) 0.0142056 +time/training (s) 19.4836 +time/epoch (s) 32.4681 +time/total (s) 8160.87 +Epoch -591 +------------------------------ ---------------- +2022-05-15 20:19:00.078497 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -590 finished +------------------------------ ---------------- +epoch -590 +replay_buffer/size 999047 +trainer/num train calls 411000 +trainer/QF1 Loss 0.806389 +trainer/QF2 Loss 0.820357 +trainer/Policy Loss 48.1811 +trainer/Q1 Predictions Mean -71.9637 +trainer/Q1 Predictions Std 19.5925 +trainer/Q1 Predictions Max -0.124369 +trainer/Q1 Predictions Min -87.9687 +trainer/Q2 Predictions Mean -71.9759 +trainer/Q2 Predictions Std 19.6371 +trainer/Q2 Predictions Max -0.394717 +trainer/Q2 Predictions Min -88.0618 +trainer/Q Targets Mean -72.091 +trainer/Q Targets Std 19.8611 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4437 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000364325 +trainer/policy/mean Std 0.711026 +trainer/policy/mean Max 0.998198 +trainer/policy/mean Min -0.998104 +trainer/policy/std Mean 0.422073 +trainer/policy/std Std 0.0210605 +trainer/policy/std Max 0.445187 +trainer/policy/std Min 0.387111 +trainer/Advantage Weights Mean 8.79452 +trainer/Advantage Weights Std 23.8219 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.83336e-16 +trainer/Advantage Score Mean -0.275375 +trainer/Advantage Score Std 0.594052 +trainer/Advantage Score Max 1.52269 +trainer/Advantage Score Min -3.54976 +trainer/V1 Predictions Mean -71.8284 +trainer/V1 Predictions Std 19.7928 +trainer/V1 Predictions Max 0.361364 +trainer/V1 Predictions Min -87.3061 +trainer/VF Loss 0.0855073 +expl/num steps total 411000 +expl/num paths total 490 +expl/path length Mean 500 +expl/path length Std 256 +expl/path length Max 756 +expl/path length Min 244 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0387801 +expl/Actions Std 0.806895 +expl/Actions Max 2.6884 +expl/Actions Min -2.43425 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 395979 +eval/num paths total 412 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0301766 +eval/Actions Std 0.73313 +eval/Actions Max 0.999805 +eval/Actions Min -0.999477 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.45989e-05 +time/evaluation sampling (s) 5.27375 +time/exploration sampling (s) 6.90227 +time/logging (s) 0.0127727 +time/saving (s) 0.0194779 +time/training (s) 20.1751 +time/epoch (s) 32.3833 +time/total (s) 8193.26 +Epoch -590 +------------------------------ ---------------- +2022-05-15 20:19:32.925167 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -589 finished +------------------------------ ---------------- +epoch -589 +replay_buffer/size 999047 +trainer/num train calls 412000 +trainer/QF1 Loss 0.629338 +trainer/QF2 Loss 0.611793 +trainer/Policy Loss 28.2894 +trainer/Q1 Predictions Mean -73.6539 +trainer/Q1 Predictions Std 18.4882 +trainer/Q1 Predictions Max -1.0994 +trainer/Q1 Predictions Min -87.3628 +trainer/Q2 Predictions Mean -73.6566 +trainer/Q2 Predictions Std 18.4571 +trainer/Q2 Predictions Max -0.853976 +trainer/Q2 Predictions Min -86.944 +trainer/Q Targets Mean -73.5293 +trainer/Q Targets Std 18.5826 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.3746 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0120288 +trainer/policy/mean Std 0.71777 +trainer/policy/mean Max 0.999243 +trainer/policy/mean Min -0.998698 +trainer/policy/std Mean 0.422722 +trainer/policy/std Std 0.0213147 +trainer/policy/std Max 0.444376 +trainer/policy/std Min 0.389715 +trainer/Advantage Weights Mean 7.51745 +trainer/Advantage Weights Std 23.4865 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.73069e-26 +trainer/Advantage Score Mean -0.27748 +trainer/Advantage Score Std 0.646755 +trainer/Advantage Score Max 1.42654 +trainer/Advantage Score Min -5.93187 +trainer/V1 Predictions Mean -73.3262 +trainer/V1 Predictions Std 18.603 +trainer/V1 Predictions Max 0.186842 +trainer/V1 Predictions Min -87.9155 +trainer/VF Loss 0.0775776 +expl/num steps total 412000 +expl/num paths total 491 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0204609 +expl/Actions Std 0.829503 +expl/Actions Max 3.22512 +expl/Actions Min -2.28163 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 396979 +eval/num paths total 413 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0265925 +eval/Actions Std 0.661331 +eval/Actions Max 0.998815 +eval/Actions Min -0.998581 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.48528e-06 +time/evaluation sampling (s) 5.3801 +time/exploration sampling (s) 7.4065 +time/logging (s) 0.0120991 +time/saving (s) 0.0176614 +time/training (s) 20.0137 +time/epoch (s) 32.8301 +time/total (s) 8226.1 +Epoch -589 +------------------------------ ---------------- +2022-05-15 20:20:05.568594 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -588 finished +------------------------------ ---------------- +epoch -588 +replay_buffer/size 999047 +trainer/num train calls 413000 +trainer/QF1 Loss 0.810162 +trainer/QF2 Loss 0.777182 +trainer/Policy Loss 6.27793 +trainer/Q1 Predictions Mean -71.4393 +trainer/Q1 Predictions Std 20.3569 +trainer/Q1 Predictions Max -0.713326 +trainer/Q1 Predictions Min -87.5792 +trainer/Q2 Predictions Mean -71.4616 +trainer/Q2 Predictions Std 20.3588 +trainer/Q2 Predictions Max 0.614205 +trainer/Q2 Predictions Min -87.9955 +trainer/Q Targets Mean -71.2466 +trainer/Q Targets Std 20.5686 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5329 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0137625 +trainer/policy/mean Std 0.727075 +trainer/policy/mean Max 0.999916 +trainer/policy/mean Min -0.99841 +trainer/policy/std Mean 0.42338 +trainer/policy/std Std 0.0205686 +trainer/policy/std Max 0.445765 +trainer/policy/std Min 0.39003 +trainer/Advantage Weights Mean 2.14628 +trainer/Advantage Weights Std 12.5443 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3672e-19 +trainer/Advantage Score Mean -0.587106 +trainer/Advantage Score Std 0.686887 +trainer/Advantage Score Max 1.85508 +trainer/Advantage Score Min -4.34364 +trainer/V1 Predictions Mean -70.9204 +trainer/V1 Predictions Std 20.7582 +trainer/V1 Predictions Max 1.41364 +trainer/V1 Predictions Min -87.7017 +trainer/VF Loss 0.0999079 +expl/num steps total 413000 +expl/num paths total 492 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0684334 +expl/Actions Std 0.832167 +expl/Actions Max 2.30659 +expl/Actions Min -2.51359 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 397979 +eval/num paths total 414 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0953417 +eval/Actions Std 0.753161 +eval/Actions Max 0.999276 +eval/Actions Min -0.999429 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.41859e-05 +time/evaluation sampling (s) 5.07363 +time/exploration sampling (s) 8.15152 +time/logging (s) 0.0112521 +time/saving (s) 0.0157141 +time/training (s) 19.3759 +time/epoch (s) 32.628 +time/total (s) 8258.73 +Epoch -588 +------------------------------ ---------------- +2022-05-15 20:20:38.615802 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -587 finished +------------------------------ ---------------- +epoch -587 +replay_buffer/size 999047 +trainer/num train calls 414000 +trainer/QF1 Loss 0.520722 +trainer/QF2 Loss 0.530366 +trainer/Policy Loss 27.4029 +trainer/Q1 Predictions Mean -74.8515 +trainer/Q1 Predictions Std 16.0997 +trainer/Q1 Predictions Max -1.49244 +trainer/Q1 Predictions Min -86.552 +trainer/Q2 Predictions Mean -74.8879 +trainer/Q2 Predictions Std 16.1397 +trainer/Q2 Predictions Max -0.919346 +trainer/Q2 Predictions Min -86.7929 +trainer/Q Targets Mean -75.0571 +trainer/Q Targets Std 15.9693 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4329 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0420931 +trainer/policy/mean Std 0.724184 +trainer/policy/mean Max 0.998911 +trainer/policy/mean Min -0.99823 +trainer/policy/std Mean 0.42314 +trainer/policy/std Std 0.0203882 +trainer/policy/std Max 0.444293 +trainer/policy/std Min 0.389245 +trainer/Advantage Weights Mean 8.94894 +trainer/Advantage Weights Std 23.9493 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.01843e-12 +trainer/Advantage Score Mean -0.119638 +trainer/Advantage Score Std 0.445547 +trainer/Advantage Score Max 1.75864 +trainer/Advantage Score Min -2.76128 +trainer/V1 Predictions Mean -74.8153 +trainer/V1 Predictions Std 16.0843 +trainer/V1 Predictions Max -2.10942 +trainer/V1 Predictions Min -86.3333 +trainer/VF Loss 0.0650739 +expl/num steps total 414000 +expl/num paths total 493 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0286087 +expl/Actions Std 0.825152 +expl/Actions Max 2.26585 +expl/Actions Min -2.44099 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 398979 +eval/num paths total 415 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.248658 +eval/Actions Std 0.685094 +eval/Actions Max 0.999725 +eval/Actions Min -0.999654 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.37212e-05 +time/evaluation sampling (s) 5.52102 +time/exploration sampling (s) 7.6525 +time/logging (s) 0.0114519 +time/saving (s) 0.0168525 +time/training (s) 19.8331 +time/epoch (s) 33.0349 +time/total (s) 8291.77 +Epoch -587 +------------------------------ ---------------- +2022-05-15 20:21:11.130667 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -586 finished +------------------------------ ---------------- +epoch -586 +replay_buffer/size 999047 +trainer/num train calls 415000 +trainer/QF1 Loss 0.776782 +trainer/QF2 Loss 0.736877 +trainer/Policy Loss 29.2872 +trainer/Q1 Predictions Mean -73.5328 +trainer/Q1 Predictions Std 18.3575 +trainer/Q1 Predictions Max -0.585359 +trainer/Q1 Predictions Min -86.6113 +trainer/Q2 Predictions Mean -73.5288 +trainer/Q2 Predictions Std 18.4385 +trainer/Q2 Predictions Max -0.462802 +trainer/Q2 Predictions Min -86.3985 +trainer/Q Targets Mean -73.7212 +trainer/Q Targets Std 18.3756 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4148 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0163269 +trainer/policy/mean Std 0.717293 +trainer/policy/mean Max 0.999564 +trainer/policy/mean Min -0.998001 +trainer/policy/std Mean 0.423971 +trainer/policy/std Std 0.0208006 +trainer/policy/std Max 0.444756 +trainer/policy/std Min 0.389575 +trainer/Advantage Weights Mean 7.64272 +trainer/Advantage Weights Std 22.5423 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.11899e-08 +trainer/Advantage Score Mean -0.142683 +trainer/Advantage Score Std 0.454296 +trainer/Advantage Score Max 1.73254 +trainer/Advantage Score Min -1.70051 +trainer/V1 Predictions Mean -73.5836 +trainer/V1 Predictions Std 18.3213 +trainer/V1 Predictions Max 0.730084 +trainer/V1 Predictions Min -86.2764 +trainer/VF Loss 0.0782499 +expl/num steps total 415000 +expl/num paths total 495 +expl/path length Mean 500 +expl/path length Std 69 +expl/path length Max 569 +expl/path length Min 431 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0238061 +expl/Actions Std 0.827942 +expl/Actions Max 2.35705 +expl/Actions Min -2.37509 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 399504 +eval/num paths total 416 +eval/path length Mean 525 +eval/path length Std 0 +eval/path length Max 525 +eval/path length Min 525 +eval/Rewards Mean 0.00190476 +eval/Rewards Std 0.043602 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.00189609 +eval/Actions Std 0.738254 +eval/Actions Max 0.998653 +eval/Actions Min -0.999317 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.02399e-05 +time/evaluation sampling (s) 5.40677 +time/exploration sampling (s) 7.2762 +time/logging (s) 0.00922269 +time/saving (s) 0.0160785 +time/training (s) 19.7896 +time/epoch (s) 32.4979 +time/total (s) 8324.28 +Epoch -586 +------------------------------ ---------------- +2022-05-15 20:21:42.687657 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -585 finished +------------------------------ ---------------- +epoch -585 +replay_buffer/size 999047 +trainer/num train calls 416000 +trainer/QF1 Loss 1.04875 +trainer/QF2 Loss 1.05483 +trainer/Policy Loss 9.5693 +trainer/Q1 Predictions Mean -72.6766 +trainer/Q1 Predictions Std 20.0386 +trainer/Q1 Predictions Max 0.0338648 +trainer/Q1 Predictions Min -87.5705 +trainer/Q2 Predictions Mean -72.6017 +trainer/Q2 Predictions Std 20.0629 +trainer/Q2 Predictions Max -0.537345 +trainer/Q2 Predictions Min -87.6364 +trainer/Q Targets Mean -72.4055 +trainer/Q Targets Std 19.9562 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4579 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0107752 +trainer/policy/mean Std 0.719162 +trainer/policy/mean Max 0.998565 +trainer/policy/mean Min -0.999842 +trainer/policy/std Mean 0.423861 +trainer/policy/std Std 0.0205022 +trainer/policy/std Max 0.444475 +trainer/policy/std Min 0.388861 +trainer/Advantage Weights Mean 1.96677 +trainer/Advantage Weights Std 11.3467 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.79044e-16 +trainer/Advantage Score Mean -0.465956 +trainer/Advantage Score Std 0.542332 +trainer/Advantage Score Max 0.677292 +trainer/Advantage Score Min -3.55089 +trainer/V1 Predictions Mean -72.1285 +trainer/V1 Predictions Std 20.1072 +trainer/V1 Predictions Max 0.222258 +trainer/V1 Predictions Min -86.4078 +trainer/VF Loss 0.0564761 +expl/num steps total 416000 +expl/num paths total 496 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.193425 +expl/Actions Std 0.834611 +expl/Actions Max 2.49796 +expl/Actions Min -2.15379 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 400504 +eval/num paths total 417 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.145216 +eval/Actions Std 0.673314 +eval/Actions Max 0.999795 +eval/Actions Min -0.998522 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.06874e-05 +time/evaluation sampling (s) 4.90184 +time/exploration sampling (s) 6.81588 +time/logging (s) 0.0124499 +time/saving (s) 0.0197853 +time/training (s) 19.7978 +time/epoch (s) 31.5478 +time/total (s) 8355.83 +Epoch -585 +------------------------------ ---------------- +2022-05-15 20:22:14.236559 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -584 finished +------------------------------ ---------------- +epoch -584 +replay_buffer/size 999047 +trainer/num train calls 417000 +trainer/QF1 Loss 1.135 +trainer/QF2 Loss 0.952159 +trainer/Policy Loss 24.1129 +trainer/Q1 Predictions Mean -73.8306 +trainer/Q1 Predictions Std 18.0606 +trainer/Q1 Predictions Max -0.397798 +trainer/Q1 Predictions Min -86.5172 +trainer/Q2 Predictions Mean -73.7001 +trainer/Q2 Predictions Std 18.0393 +trainer/Q2 Predictions Max -0.514593 +trainer/Q2 Predictions Min -86.365 +trainer/Q Targets Mean -73.709 +trainer/Q Targets Std 17.8265 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8604 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0182693 +trainer/policy/mean Std 0.728011 +trainer/policy/mean Max 0.998483 +trainer/policy/mean Min -0.997737 +trainer/policy/std Mean 0.421642 +trainer/policy/std Std 0.0210059 +trainer/policy/std Max 0.442519 +trainer/policy/std Min 0.387095 +trainer/Advantage Weights Mean 5.13891 +trainer/Advantage Weights Std 19.2287 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.8217e-12 +trainer/Advantage Score Mean -0.34405 +trainer/Advantage Score Std 0.57241 +trainer/Advantage Score Max 1.89507 +trainer/Advantage Score Min -2.70312 +trainer/V1 Predictions Mean -73.4693 +trainer/V1 Predictions Std 17.7972 +trainer/V1 Predictions Max -0.670044 +trainer/V1 Predictions Min -85.6958 +trainer/VF Loss 0.100778 +expl/num steps total 417000 +expl/num paths total 497 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0305935 +expl/Actions Std 0.808302 +expl/Actions Max 2.24695 +expl/Actions Min -2.25513 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 401504 +eval/num paths total 418 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.035646 +eval/Actions Std 0.738291 +eval/Actions Max 0.999835 +eval/Actions Min -0.998992 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.26353e-05 +time/evaluation sampling (s) 4.85213 +time/exploration sampling (s) 7.316 +time/logging (s) 0.00939024 +time/saving (s) 0.014504 +time/training (s) 19.3382 +time/epoch (s) 31.5302 +time/total (s) 8387.37 +Epoch -584 +------------------------------ ---------------- +2022-05-15 20:22:46.107256 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -583 finished +------------------------------ ---------------- +epoch -583 +replay_buffer/size 999047 +trainer/num train calls 418000 +trainer/QF1 Loss 0.642743 +trainer/QF2 Loss 0.737581 +trainer/Policy Loss 8.46106 +trainer/Q1 Predictions Mean -73.4164 +trainer/Q1 Predictions Std 17.6595 +trainer/Q1 Predictions Max -1.09293 +trainer/Q1 Predictions Min -86.1928 +trainer/Q2 Predictions Mean -73.4935 +trainer/Q2 Predictions Std 17.6194 +trainer/Q2 Predictions Max -0.613853 +trainer/Q2 Predictions Min -86.1282 +trainer/Q Targets Mean -73.4117 +trainer/Q Targets Std 17.8298 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0431 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00123577 +trainer/policy/mean Std 0.71312 +trainer/policy/mean Max 0.997662 +trainer/policy/mean Min -0.998155 +trainer/policy/std Mean 0.421538 +trainer/policy/std Std 0.0206485 +trainer/policy/std Max 0.443933 +trainer/policy/std Min 0.387382 +trainer/Advantage Weights Mean 1.72752 +trainer/Advantage Weights Std 10.8809 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.9732e-20 +trainer/Advantage Score Mean -0.64635 +trainer/Advantage Score Std 0.621324 +trainer/Advantage Score Max 0.924252 +trainer/Advantage Score Min -4.38575 +trainer/V1 Predictions Mean -73.1443 +trainer/V1 Predictions Std 17.9037 +trainer/V1 Predictions Max -0.455281 +trainer/V1 Predictions Min -86.1418 +trainer/VF Loss 0.0865612 +expl/num steps total 418000 +expl/num paths total 499 +expl/path length Mean 500 +expl/path length Std 396 +expl/path length Max 896 +expl/path length Min 104 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0327363 +expl/Actions Std 0.829536 +expl/Actions Max 2.35864 +expl/Actions Min -2.25667 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 402504 +eval/num paths total 419 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.15226 +eval/Actions Std 0.696127 +eval/Actions Max 0.999289 +eval/Actions Min -0.998219 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.00592e-05 +time/evaluation sampling (s) 5.28654 +time/exploration sampling (s) 7.54867 +time/logging (s) 0.0120972 +time/saving (s) 0.0182954 +time/training (s) 18.9977 +time/epoch (s) 31.8633 +time/total (s) 8419.24 +Epoch -583 +------------------------------ ---------------- +2022-05-15 20:23:18.250108 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -582 finished +------------------------------ ---------------- +epoch -582 +replay_buffer/size 999047 +trainer/num train calls 419000 +trainer/QF1 Loss 0.83027 +trainer/QF2 Loss 0.816127 +trainer/Policy Loss 29.453 +trainer/Q1 Predictions Mean -74.1667 +trainer/Q1 Predictions Std 17.7632 +trainer/Q1 Predictions Max -0.126895 +trainer/Q1 Predictions Min -86.046 +trainer/Q2 Predictions Mean -74.2703 +trainer/Q2 Predictions Std 17.781 +trainer/Q2 Predictions Max -0.309797 +trainer/Q2 Predictions Min -86.0321 +trainer/Q Targets Mean -74.5544 +trainer/Q Targets Std 17.7243 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1278 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0265093 +trainer/policy/mean Std 0.724462 +trainer/policy/mean Max 0.998897 +trainer/policy/mean Min -0.997863 +trainer/policy/std Mean 0.421186 +trainer/policy/std Std 0.022161 +trainer/policy/std Max 0.443948 +trainer/policy/std Min 0.385751 +trainer/Advantage Weights Mean 6.75942 +trainer/Advantage Weights Std 21.9385 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.90752e-13 +trainer/Advantage Score Mean -0.222567 +trainer/Advantage Score Std 0.477326 +trainer/Advantage Score Max 0.936298 +trainer/Advantage Score Min -2.83428 +trainer/V1 Predictions Mean -74.3257 +trainer/V1 Predictions Std 17.7729 +trainer/V1 Predictions Max 0.155294 +trainer/V1 Predictions Min -85.9573 +trainer/VF Loss 0.0504093 +expl/num steps total 419000 +expl/num paths total 501 +expl/path length Mean 500 +expl/path length Std 315 +expl/path length Max 815 +expl/path length Min 185 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0542868 +expl/Actions Std 0.829813 +expl/Actions Max 2.12353 +expl/Actions Min -2.31986 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 403504 +eval/num paths total 420 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0252147 +eval/Actions Std 0.690299 +eval/Actions Max 0.999914 +eval/Actions Min -0.999712 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 5.10318e-06 +time/evaluation sampling (s) 5.17931 +time/exploration sampling (s) 7.44871 +time/logging (s) 0.00812068 +time/saving (s) 0.0116558 +time/training (s) 19.4769 +time/epoch (s) 32.1247 +time/total (s) 8451.37 +Epoch -582 +------------------------------ ---------------- +2022-05-15 20:23:49.499675 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -581 finished +------------------------------ ---------------- +epoch -581 +replay_buffer/size 999047 +trainer/num train calls 420000 +trainer/QF1 Loss 0.720242 +trainer/QF2 Loss 0.678779 +trainer/Policy Loss 9.3895 +trainer/Q1 Predictions Mean -74.9922 +trainer/Q1 Predictions Std 16.1853 +trainer/Q1 Predictions Max -1.23711 +trainer/Q1 Predictions Min -88.4963 +trainer/Q2 Predictions Mean -74.9277 +trainer/Q2 Predictions Std 16.1844 +trainer/Q2 Predictions Max -0.827054 +trainer/Q2 Predictions Min -88.868 +trainer/Q Targets Mean -75.1435 +trainer/Q Targets Std 15.941 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.7161 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00127003 +trainer/policy/mean Std 0.71836 +trainer/policy/mean Max 0.996735 +trainer/policy/mean Min -0.99781 +trainer/policy/std Mean 0.421315 +trainer/policy/std Std 0.0221763 +trainer/policy/std Max 0.444634 +trainer/policy/std Min 0.383676 +trainer/Advantage Weights Mean 2.74565 +trainer/Advantage Weights Std 13.153 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.10539e-11 +trainer/Advantage Score Mean -0.300777 +trainer/Advantage Score Std 0.432483 +trainer/Advantage Score Max 1.68314 +trainer/Advantage Score Min -2.36981 +trainer/V1 Predictions Mean -74.8611 +trainer/V1 Predictions Std 16.1016 +trainer/V1 Predictions Max -1.01575 +trainer/V1 Predictions Min -88.884 +trainer/VF Loss 0.0446806 +expl/num steps total 420000 +expl/num paths total 502 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.188783 +expl/Actions Std 0.832584 +expl/Actions Max 2.35423 +expl/Actions Min -2.17499 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 404504 +eval/num paths total 421 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0347184 +eval/Actions Std 0.731843 +eval/Actions Max 0.999569 +eval/Actions Min -0.999622 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.13072e-05 +time/evaluation sampling (s) 4.8869 +time/exploration sampling (s) 7.36738 +time/logging (s) 0.0105368 +time/saving (s) 0.0175786 +time/training (s) 18.9603 +time/epoch (s) 31.2427 +time/total (s) 8482.62 +Epoch -581 +------------------------------ ---------------- +2022-05-15 20:24:22.155594 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -580 finished +------------------------------ ---------------- +epoch -580 +replay_buffer/size 999047 +trainer/num train calls 421000 +trainer/QF1 Loss 0.685703 +trainer/QF2 Loss 0.665669 +trainer/Policy Loss 7.23588 +trainer/Q1 Predictions Mean -73.8588 +trainer/Q1 Predictions Std 17.5042 +trainer/Q1 Predictions Max -0.557583 +trainer/Q1 Predictions Min -86.353 +trainer/Q2 Predictions Mean -73.7663 +trainer/Q2 Predictions Std 17.4785 +trainer/Q2 Predictions Max -0.313919 +trainer/Q2 Predictions Min -86.3017 +trainer/Q Targets Mean -73.7118 +trainer/Q Targets Std 17.3699 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2531 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00701805 +trainer/policy/mean Std 0.709194 +trainer/policy/mean Max 0.997953 +trainer/policy/mean Min -0.998192 +trainer/policy/std Mean 0.423939 +trainer/policy/std Std 0.0210655 +trainer/policy/std Max 0.446471 +trainer/policy/std Min 0.388132 +trainer/Advantage Weights Mean 2.25624 +trainer/Advantage Weights Std 13.9648 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.23417e-15 +trainer/Advantage Score Mean -0.521165 +trainer/Advantage Score Std 0.490738 +trainer/Advantage Score Max 0.849617 +trainer/Advantage Score Min -3.28836 +trainer/V1 Predictions Mean -73.4712 +trainer/V1 Predictions Std 17.5078 +trainer/V1 Predictions Max 1.48954 +trainer/V1 Predictions Min -86.0518 +trainer/VF Loss 0.0595227 +expl/num steps total 421000 +expl/num paths total 504 +expl/path length Mean 500 +expl/path length Std 496 +expl/path length Max 996 +expl/path length Min 4 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0295173 +expl/Actions Std 0.822921 +expl/Actions Max 2.31694 +expl/Actions Min -2.19256 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 405504 +eval/num paths total 422 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.317306 +eval/Actions Std 0.624509 +eval/Actions Max 0.997981 +eval/Actions Min -0.995186 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.23172e-05 +time/evaluation sampling (s) 5.49588 +time/exploration sampling (s) 7.06901 +time/logging (s) 0.0126229 +time/saving (s) 0.0189843 +time/training (s) 20.0463 +time/epoch (s) 32.6428 +time/total (s) 8515.27 +Epoch -580 +------------------------------ ---------------- +2022-05-15 20:24:54.267782 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -579 finished +------------------------------ ---------------- +epoch -579 +replay_buffer/size 999047 +trainer/num train calls 422000 +trainer/QF1 Loss 1.10065 +trainer/QF2 Loss 0.853476 +trainer/Policy Loss 48.6844 +trainer/Q1 Predictions Mean -72.784 +trainer/Q1 Predictions Std 18.0701 +trainer/Q1 Predictions Max -0.995693 +trainer/Q1 Predictions Min -87.9854 +trainer/Q2 Predictions Mean -72.8694 +trainer/Q2 Predictions Std 17.9982 +trainer/Q2 Predictions Max 0.256006 +trainer/Q2 Predictions Min -87.9414 +trainer/Q Targets Mean -73.2831 +trainer/Q Targets Std 17.9308 +trainer/Q Targets Max 0.34969 +trainer/Q Targets Min -88.703 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0129394 +trainer/policy/mean Std 0.717527 +trainer/policy/mean Max 0.996797 +trainer/policy/mean Min -0.997992 +trainer/policy/std Mean 0.42438 +trainer/policy/std Std 0.0207155 +trainer/policy/std Max 0.445978 +trainer/policy/std Min 0.389781 +trainer/Advantage Weights Mean 10.3934 +trainer/Advantage Weights Std 24.8057 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.82508e-12 +trainer/Advantage Score Mean -0.215781 +trainer/Advantage Score Std 0.598527 +trainer/Advantage Score Max 1.52515 +trainer/Advantage Score Min -2.70294 +trainer/V1 Predictions Mean -72.9612 +trainer/V1 Predictions Std 18.2443 +trainer/V1 Predictions Max 1.70056 +trainer/V1 Predictions Min -88.549 +trainer/VF Loss 0.0864664 +expl/num steps total 422000 +expl/num paths total 505 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0552606 +expl/Actions Std 0.818168 +expl/Actions Max 2.60566 +expl/Actions Min -2.32367 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 406502 +eval/num paths total 423 +eval/path length Mean 998 +eval/path length Std 0 +eval/path length Max 998 +eval/path length Min 998 +eval/Rewards Mean 0.001002 +eval/Rewards Std 0.0316386 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.00411807 +eval/Actions Std 0.733109 +eval/Actions Max 0.998932 +eval/Actions Min -0.999397 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2117e-05 +time/evaluation sampling (s) 5.27818 +time/exploration sampling (s) 7.27273 +time/logging (s) 0.00894599 +time/saving (s) 0.0126879 +time/training (s) 19.5205 +time/epoch (s) 32.093 +time/total (s) 8547.37 +Epoch -579 +------------------------------ ---------------- +2022-05-15 20:25:26.828417 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -578 finished +------------------------------ ---------------- +epoch -578 +replay_buffer/size 999047 +trainer/num train calls 423000 +trainer/QF1 Loss 1.60617 +trainer/QF2 Loss 1.4232 +trainer/Policy Loss 51.9105 +trainer/Q1 Predictions Mean -72.4453 +trainer/Q1 Predictions Std 18.5667 +trainer/Q1 Predictions Max -0.48403 +trainer/Q1 Predictions Min -86.5995 +trainer/Q2 Predictions Mean -72.5341 +trainer/Q2 Predictions Std 18.6271 +trainer/Q2 Predictions Max -1.10416 +trainer/Q2 Predictions Min -86.5733 +trainer/Q Targets Mean -73.1259 +trainer/Q Targets Std 18.2985 +trainer/Q Targets Max -1.00115 +trainer/Q Targets Min -86.9271 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00816386 +trainer/policy/mean Std 0.729983 +trainer/policy/mean Max 0.999301 +trainer/policy/mean Min -0.997955 +trainer/policy/std Mean 0.424401 +trainer/policy/std Std 0.019518 +trainer/policy/std Max 0.444309 +trainer/policy/std Min 0.391872 +trainer/Advantage Weights Mean 13.3014 +trainer/Advantage Weights Std 29.5235 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.47136e-20 +trainer/Advantage Score Mean -0.133302 +trainer/Advantage Score Std 0.651393 +trainer/Advantage Score Max 1.28153 +trainer/Advantage Score Min -4.56655 +trainer/V1 Predictions Mean -72.8332 +trainer/V1 Predictions Std 18.542 +trainer/V1 Predictions Max -0.944596 +trainer/V1 Predictions Min -87.0954 +trainer/VF Loss 0.0999894 +expl/num steps total 423000 +expl/num paths total 507 +expl/path length Mean 500 +expl/path length Std 347 +expl/path length Max 847 +expl/path length Min 153 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0147075 +expl/Actions Std 0.823701 +expl/Actions Max 2.51089 +expl/Actions Min -2.48014 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 406955 +eval/num paths total 424 +eval/path length Mean 453 +eval/path length Std 0 +eval/path length Max 453 +eval/path length Min 453 +eval/Rewards Mean 0.00220751 +eval/Rewards Std 0.0469322 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00704325 +eval/Actions Std 0.744634 +eval/Actions Max 0.999765 +eval/Actions Min -0.999676 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.22599e-05 +time/evaluation sampling (s) 4.70575 +time/exploration sampling (s) 7.82923 +time/logging (s) 0.00622914 +time/saving (s) 0.0126466 +time/training (s) 19.9882 +time/epoch (s) 32.5421 +time/total (s) 8579.91 +Epoch -578 +------------------------------ ---------------- +2022-05-15 20:25:58.637073 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -577 finished +------------------------------ ---------------- +epoch -577 +replay_buffer/size 999047 +trainer/num train calls 424000 +trainer/QF1 Loss 2.72414 +trainer/QF2 Loss 2.76908 +trainer/Policy Loss 63.4237 +trainer/Q1 Predictions Mean -74.4564 +trainer/Q1 Predictions Std 15.6822 +trainer/Q1 Predictions Max -0.40482 +trainer/Q1 Predictions Min -85.6153 +trainer/Q2 Predictions Mean -74.4203 +trainer/Q2 Predictions Std 15.6684 +trainer/Q2 Predictions Max -0.376814 +trainer/Q2 Predictions Min -85.5965 +trainer/Q Targets Mean -75.1845 +trainer/Q Targets Std 15.7803 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3472 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0228394 +trainer/policy/mean Std 0.71938 +trainer/policy/mean Max 0.999417 +trainer/policy/mean Min -0.999748 +trainer/policy/std Mean 0.422879 +trainer/policy/std Std 0.0189417 +trainer/policy/std Max 0.44749 +trainer/policy/std Min 0.393506 +trainer/Advantage Weights Mean 13.997 +trainer/Advantage Weights Std 28.6236 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.40534e-09 +trainer/Advantage Score Mean -0.0686028 +trainer/Advantage Score Std 0.486044 +trainer/Advantage Score Max 1.85604 +trainer/Advantage Score Min -1.85944 +trainer/V1 Predictions Mean -75.019 +trainer/V1 Predictions Std 15.8628 +trainer/V1 Predictions Max 0.419641 +trainer/V1 Predictions Min -86.1155 +trainer/VF Loss 0.090163 +expl/num steps total 424000 +expl/num paths total 509 +expl/path length Mean 500 +expl/path length Std 179 +expl/path length Max 679 +expl/path length Min 321 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0243634 +expl/Actions Std 0.833404 +expl/Actions Max 2.64347 +expl/Actions Min -2.39373 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 407955 +eval/num paths total 425 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.100812 +eval/Actions Std 0.625894 +eval/Actions Max 0.999804 +eval/Actions Min -0.999303 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.20299e-05 +time/evaluation sampling (s) 5.03913 +time/exploration sampling (s) 7.00155 +time/logging (s) 0.012501 +time/saving (s) 0.0185602 +time/training (s) 19.7341 +time/epoch (s) 31.8058 +time/total (s) 8611.73 +Epoch -577 +------------------------------ ---------------- +2022-05-15 20:26:31.600331 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -576 finished +------------------------------ ---------------- +epoch -576 +replay_buffer/size 999047 +trainer/num train calls 425000 +trainer/QF1 Loss 0.706488 +trainer/QF2 Loss 0.754336 +trainer/Policy Loss 11.5735 +trainer/Q1 Predictions Mean -70.5664 +trainer/Q1 Predictions Std 21.4087 +trainer/Q1 Predictions Max -0.337098 +trainer/Q1 Predictions Min -89.5264 +trainer/Q2 Predictions Mean -70.48 +trainer/Q2 Predictions Std 21.4079 +trainer/Q2 Predictions Max 0.157942 +trainer/Q2 Predictions Min -89.0743 +trainer/Q Targets Mean -70.3883 +trainer/Q Targets Std 21.3977 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.756 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0179643 +trainer/policy/mean Std 0.717492 +trainer/policy/mean Max 0.998688 +trainer/policy/mean Min -0.998504 +trainer/policy/std Mean 0.422185 +trainer/policy/std Std 0.0203781 +trainer/policy/std Max 0.445345 +trainer/policy/std Min 0.386138 +trainer/Advantage Weights Mean 3.61901 +trainer/Advantage Weights Std 16.5431 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.84803e-15 +trainer/Advantage Score Mean -0.403615 +trainer/Advantage Score Std 0.5493 +trainer/Advantage Score Max 1.69949 +trainer/Advantage Score Min -3.39247 +trainer/V1 Predictions Mean -70.1594 +trainer/V1 Predictions Std 21.4658 +trainer/V1 Predictions Max 0.341962 +trainer/V1 Predictions Min -88.5279 +trainer/VF Loss 0.0650319 +expl/num steps total 425000 +expl/num paths total 510 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0494988 +expl/Actions Std 0.832482 +expl/Actions Max 2.61004 +expl/Actions Min -2.20565 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 408955 +eval/num paths total 426 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.271738 +eval/Actions Std 0.618404 +eval/Actions Max 0.999306 +eval/Actions Min -0.999621 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.2038e-06 +time/evaluation sampling (s) 5.13512 +time/exploration sampling (s) 8.04426 +time/logging (s) 0.00760203 +time/saving (s) 0.0104886 +time/training (s) 19.746 +time/epoch (s) 32.9435 +time/total (s) 8644.68 +Epoch -576 +------------------------------ ---------------- +2022-05-15 20:27:03.552509 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -575 finished +------------------------------ ---------------- +epoch -575 +replay_buffer/size 999047 +trainer/num train calls 426000 +trainer/QF1 Loss 0.651977 +trainer/QF2 Loss 0.616067 +trainer/Policy Loss 14.238 +trainer/Q1 Predictions Mean -72.5089 +trainer/Q1 Predictions Std 19.7421 +trainer/Q1 Predictions Max -0.425525 +trainer/Q1 Predictions Min -88.1116 +trainer/Q2 Predictions Mean -72.3975 +trainer/Q2 Predictions Std 19.8795 +trainer/Q2 Predictions Max -0.312543 +trainer/Q2 Predictions Min -87.8771 +trainer/Q Targets Mean -72.3371 +trainer/Q Targets Std 19.8768 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4772 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00363028 +trainer/policy/mean Std 0.717125 +trainer/policy/mean Max 0.999521 +trainer/policy/mean Min -0.999109 +trainer/policy/std Mean 0.4227 +trainer/policy/std Std 0.0219846 +trainer/policy/std Max 0.44773 +trainer/policy/std Min 0.388246 +trainer/Advantage Weights Mean 2.61657 +trainer/Advantage Weights Std 14.1022 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.06501e-18 +trainer/Advantage Score Mean -0.443356 +trainer/Advantage Score Std 0.591851 +trainer/Advantage Score Max 1.62246 +trainer/Advantage Score Min -4.03265 +trainer/V1 Predictions Mean -72.0916 +trainer/V1 Predictions Std 20.0402 +trainer/V1 Predictions Max 1.40271 +trainer/V1 Predictions Min -87.118 +trainer/VF Loss 0.0749991 +expl/num steps total 426000 +expl/num paths total 511 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.188668 +expl/Actions Std 0.7838 +expl/Actions Max 2.4093 +expl/Actions Min -2.19477 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 409955 +eval/num paths total 427 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0791165 +eval/Actions Std 0.731396 +eval/Actions Max 0.999316 +eval/Actions Min -0.998504 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.19188e-06 +time/evaluation sampling (s) 5.36926 +time/exploration sampling (s) 7.13086 +time/logging (s) 0.00905901 +time/saving (s) 0.0132803 +time/training (s) 19.4213 +time/epoch (s) 31.9438 +time/total (s) 8676.62 +Epoch -575 +------------------------------ ---------------- +2022-05-15 20:27:36.215381 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -574 finished +------------------------------ ---------------- +epoch -574 +replay_buffer/size 999047 +trainer/num train calls 427000 +trainer/QF1 Loss 0.611228 +trainer/QF2 Loss 0.502168 +trainer/Policy Loss 9.03134 +trainer/Q1 Predictions Mean -73.4409 +trainer/Q1 Predictions Std 17.1748 +trainer/Q1 Predictions Max -2.39928 +trainer/Q1 Predictions Min -86.3954 +trainer/Q2 Predictions Mean -73.3581 +trainer/Q2 Predictions Std 17.1902 +trainer/Q2 Predictions Max -2.89328 +trainer/Q2 Predictions Min -86.1705 +trainer/Q Targets Mean -73.3621 +trainer/Q Targets Std 17.3138 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3608 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000472573 +trainer/policy/mean Std 0.732358 +trainer/policy/mean Max 0.999128 +trainer/policy/mean Min -0.999194 +trainer/policy/std Mean 0.42161 +trainer/policy/std Std 0.0239784 +trainer/policy/std Max 0.446618 +trainer/policy/std Min 0.384049 +trainer/Advantage Weights Mean 2.86281 +trainer/Advantage Weights Std 14.1089 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.59049e-18 +trainer/Advantage Score Mean -0.421109 +trainer/Advantage Score Std 0.5421 +trainer/Advantage Score Max 1.37754 +trainer/Advantage Score Min -3.92959 +trainer/V1 Predictions Mean -73.15 +trainer/V1 Predictions Std 17.3164 +trainer/V1 Predictions Max -2.08661 +trainer/V1 Predictions Min -86.1893 +trainer/VF Loss 0.0619347 +expl/num steps total 427000 +expl/num paths total 512 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0963168 +expl/Actions Std 0.856162 +expl/Actions Max 2.24499 +expl/Actions Min -2.26811 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 410955 +eval/num paths total 428 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0481578 +eval/Actions Std 0.725483 +eval/Actions Max 0.999649 +eval/Actions Min -0.999257 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.20839e-05 +time/evaluation sampling (s) 4.69037 +time/exploration sampling (s) 8.09205 +time/logging (s) 0.0115476 +time/saving (s) 0.0149144 +time/training (s) 19.8464 +time/epoch (s) 32.6553 +time/total (s) 8709.28 +Epoch -574 +------------------------------ ---------------- +2022-05-15 20:28:07.897092 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -573 finished +------------------------------ ---------------- +epoch -573 +replay_buffer/size 999047 +trainer/num train calls 428000 +trainer/QF1 Loss 2.9124 +trainer/QF2 Loss 2.79179 +trainer/Policy Loss 13.7697 +trainer/Q1 Predictions Mean -71.8399 +trainer/Q1 Predictions Std 20.3725 +trainer/Q1 Predictions Max -0.85085 +trainer/Q1 Predictions Min -87.197 +trainer/Q2 Predictions Mean -71.8894 +trainer/Q2 Predictions Std 20.3653 +trainer/Q2 Predictions Max -0.495105 +trainer/Q2 Predictions Min -87.1804 +trainer/Q Targets Mean -71.603 +trainer/Q Targets Std 20.4761 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.0615 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0124075 +trainer/policy/mean Std 0.714854 +trainer/policy/mean Max 0.997947 +trainer/policy/mean Min -0.999524 +trainer/policy/std Mean 0.422337 +trainer/policy/std Std 0.0218896 +trainer/policy/std Max 0.442391 +trainer/policy/std Min 0.385131 +trainer/Advantage Weights Mean 3.22401 +trainer/Advantage Weights Std 15.5857 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.49465e-17 +trainer/Advantage Score Mean -0.536377 +trainer/Advantage Score Std 0.600437 +trainer/Advantage Score Max 1.08836 +trainer/Advantage Score Min -3.7273 +trainer/V1 Predictions Mean -71.3724 +trainer/V1 Predictions Std 20.5492 +trainer/V1 Predictions Max 0.602698 +trainer/V1 Predictions Min -87.5158 +trainer/VF Loss 0.0776568 +expl/num steps total 428000 +expl/num paths total 513 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00037167 +expl/Actions Std 0.849255 +expl/Actions Max 2.5889 +expl/Actions Min -2.57087 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 411329 +eval/num paths total 429 +eval/path length Mean 374 +eval/path length Std 0 +eval/path length Max 374 +eval/path length Min 374 +eval/Rewards Mean 0.0026738 +eval/Rewards Std 0.0516396 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.049951 +eval/Actions Std 0.739198 +eval/Actions Max 0.999917 +eval/Actions Min -0.998987 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 7.86409e-06 +time/evaluation sampling (s) 4.96674 +time/exploration sampling (s) 7.08319 +time/logging (s) 0.00989264 +time/saving (s) 0.0207033 +time/training (s) 19.5866 +time/epoch (s) 31.6671 +time/total (s) 8740.96 +Epoch -573 +------------------------------ ---------------- +2022-05-15 20:28:40.526445 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -572 finished +------------------------------ ---------------- +epoch -572 +replay_buffer/size 999047 +trainer/num train calls 429000 +trainer/QF1 Loss 0.61067 +trainer/QF2 Loss 0.578315 +trainer/Policy Loss 19.2088 +trainer/Q1 Predictions Mean -74.7032 +trainer/Q1 Predictions Std 16.334 +trainer/Q1 Predictions Max -1.47454 +trainer/Q1 Predictions Min -86.5753 +trainer/Q2 Predictions Mean -74.7035 +trainer/Q2 Predictions Std 16.3682 +trainer/Q2 Predictions Max -0.994097 +trainer/Q2 Predictions Min -86.2898 +trainer/Q Targets Mean -74.8158 +trainer/Q Targets Std 16.3177 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1938 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0165924 +trainer/policy/mean Std 0.7106 +trainer/policy/mean Max 0.99928 +trainer/policy/mean Min -0.999447 +trainer/policy/std Mean 0.423361 +trainer/policy/std Std 0.0214735 +trainer/policy/std Max 0.445122 +trainer/policy/std Min 0.386111 +trainer/Advantage Weights Mean 5.50951 +trainer/Advantage Weights Std 18.2983 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3161e-09 +trainer/Advantage Score Mean -0.197795 +trainer/Advantage Score Std 0.448394 +trainer/Advantage Score Max 1.21799 +trainer/Advantage Score Min -2.04486 +trainer/V1 Predictions Mean -74.5372 +trainer/V1 Predictions Std 16.4398 +trainer/V1 Predictions Max -0.211801 +trainer/V1 Predictions Min -86.0909 +trainer/VF Loss 0.0448761 +expl/num steps total 429000 +expl/num paths total 514 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00607172 +expl/Actions Std 0.834045 +expl/Actions Max 2.30802 +expl/Actions Min -2.38422 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 412329 +eval/num paths total 430 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0215894 +eval/Actions Std 0.688566 +eval/Actions Max 0.999096 +eval/Actions Min -0.999377 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.85013e-06 +time/evaluation sampling (s) 5.0746 +time/exploration sampling (s) 7.50884 +time/logging (s) 0.0100039 +time/saving (s) 0.0166307 +time/training (s) 20.0043 +time/epoch (s) 32.6144 +time/total (s) 8773.58 +Epoch -572 +------------------------------ ---------------- +2022-05-15 20:29:12.611887 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -571 finished +------------------------------ ---------------- +epoch -571 +replay_buffer/size 999047 +trainer/num train calls 430000 +trainer/QF1 Loss 0.67648 +trainer/QF2 Loss 0.816964 +trainer/Policy Loss 11.42 +trainer/Q1 Predictions Mean -72.8449 +trainer/Q1 Predictions Std 18.9728 +trainer/Q1 Predictions Max -1.569 +trainer/Q1 Predictions Min -87.1312 +trainer/Q2 Predictions Mean -72.8141 +trainer/Q2 Predictions Std 18.9056 +trainer/Q2 Predictions Max -1.39212 +trainer/Q2 Predictions Min -86.9514 +trainer/Q Targets Mean -72.7594 +trainer/Q Targets Std 18.8864 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6051 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0115196 +trainer/policy/mean Std 0.715665 +trainer/policy/mean Max 0.997682 +trainer/policy/mean Min -0.998694 +trainer/policy/std Mean 0.422436 +trainer/policy/std Std 0.020991 +trainer/policy/std Max 0.442522 +trainer/policy/std Min 0.38889 +trainer/Advantage Weights Mean 3.04449 +trainer/Advantage Weights Std 15.4002 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.67621e-13 +trainer/Advantage Score Mean -0.423151 +trainer/Advantage Score Std 0.523667 +trainer/Advantage Score Max 1.58578 +trainer/Advantage Score Min -2.89492 +trainer/V1 Predictions Mean -72.5089 +trainer/V1 Predictions Std 18.8542 +trainer/V1 Predictions Max -2.00057 +trainer/V1 Predictions Min -86.5353 +trainer/VF Loss 0.0639014 +expl/num steps total 430000 +expl/num paths total 516 +expl/path length Mean 500 +expl/path length Std 384 +expl/path length Max 884 +expl/path length Min 116 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0104246 +expl/Actions Std 0.819321 +expl/Actions Max 2.56037 +expl/Actions Min -2.48103 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 413329 +eval/num paths total 431 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.167018 +eval/Actions Std 0.699233 +eval/Actions Max 0.999321 +eval/Actions Min -0.998563 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.23801e-05 +time/evaluation sampling (s) 5.10778 +time/exploration sampling (s) 6.7818 +time/logging (s) 0.0116117 +time/saving (s) 0.0154063 +time/training (s) 20.16 +time/epoch (s) 32.0766 +time/total (s) 8805.66 +Epoch -571 +------------------------------ ---------------- +2022-05-15 20:29:45.016989 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -570 finished +------------------------------ ---------------- +epoch -570 +replay_buffer/size 999047 +trainer/num train calls 431000 +trainer/QF1 Loss 1.08612 +trainer/QF2 Loss 0.961928 +trainer/Policy Loss 17.5274 +trainer/Q1 Predictions Mean -74.3488 +trainer/Q1 Predictions Std 17.8829 +trainer/Q1 Predictions Max -1.15358 +trainer/Q1 Predictions Min -86.1108 +trainer/Q2 Predictions Mean -74.3492 +trainer/Q2 Predictions Std 17.9252 +trainer/Q2 Predictions Max -1.16717 +trainer/Q2 Predictions Min -86.1701 +trainer/Q Targets Mean -74.1328 +trainer/Q Targets Std 18.0678 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8486 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00456344 +trainer/policy/mean Std 0.710331 +trainer/policy/mean Max 0.999414 +trainer/policy/mean Min -0.999059 +trainer/policy/std Mean 0.422362 +trainer/policy/std Std 0.02164 +trainer/policy/std Max 0.444832 +trainer/policy/std Min 0.386664 +trainer/Advantage Weights Mean 3.58858 +trainer/Advantage Weights Std 15.0894 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.67405e-13 +trainer/Advantage Score Mean -0.272718 +trainer/Advantage Score Std 0.490919 +trainer/Advantage Score Max 1.35042 +trainer/Advantage Score Min -2.83916 +trainer/V1 Predictions Mean -73.9541 +trainer/V1 Predictions Std 17.9584 +trainer/V1 Predictions Max -1.21786 +trainer/V1 Predictions Min -85.7493 +trainer/VF Loss 0.0541958 +expl/num steps total 431000 +expl/num paths total 517 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.184987 +expl/Actions Std 0.784601 +expl/Actions Max 2.32985 +expl/Actions Min -2.13682 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 414034 +eval/num paths total 432 +eval/path length Mean 705 +eval/path length Std 0 +eval/path length Max 705 +eval/path length Min 705 +eval/Rewards Mean 0.00141844 +eval/Rewards Std 0.0376355 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0450476 +eval/Actions Std 0.729992 +eval/Actions Max 0.999374 +eval/Actions Min -0.999758 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0821e-05 +time/evaluation sampling (s) 5.04621 +time/exploration sampling (s) 7.18311 +time/logging (s) 0.0106388 +time/saving (s) 0.0158411 +time/training (s) 20.1344 +time/epoch (s) 32.3902 +time/total (s) 8838.06 +Epoch -570 +------------------------------ ---------------- +2022-05-15 20:30:16.533298 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -569 finished +------------------------------ ---------------- +epoch -569 +replay_buffer/size 999047 +trainer/num train calls 432000 +trainer/QF1 Loss 0.566229 +trainer/QF2 Loss 0.569135 +trainer/Policy Loss 32.8057 +trainer/Q1 Predictions Mean -72.9766 +trainer/Q1 Predictions Std 18.5165 +trainer/Q1 Predictions Max -3.53439 +trainer/Q1 Predictions Min -85.9422 +trainer/Q2 Predictions Mean -72.9158 +trainer/Q2 Predictions Std 18.4426 +trainer/Q2 Predictions Max -2.98163 +trainer/Q2 Predictions Min -86.3344 +trainer/Q Targets Mean -73.0261 +trainer/Q Targets Std 18.6845 +trainer/Q Targets Max -4.34411 +trainer/Q Targets Min -86.6523 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0098225 +trainer/policy/mean Std 0.723964 +trainer/policy/mean Max 0.996546 +trainer/policy/mean Min -0.99812 +trainer/policy/std Mean 0.421782 +trainer/policy/std Std 0.0212281 +trainer/policy/std Max 0.443439 +trainer/policy/std Min 0.388562 +trainer/Advantage Weights Mean 8.72787 +trainer/Advantage Weights Std 24.9134 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.22599e-21 +trainer/Advantage Score Mean -0.302639 +trainer/Advantage Score Std 0.742382 +trainer/Advantage Score Max 1.18194 +trainer/Advantage Score Min -4.75541 +trainer/V1 Predictions Mean -72.7468 +trainer/V1 Predictions Std 18.9056 +trainer/V1 Predictions Max -1.23842 +trainer/V1 Predictions Min -86.4533 +trainer/VF Loss 0.0989642 +expl/num steps total 432000 +expl/num paths total 518 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0602839 +expl/Actions Std 0.830282 +expl/Actions Max 2.30161 +expl/Actions Min -2.27396 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 415034 +eval/num paths total 433 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0361343 +eval/Actions Std 0.73941 +eval/Actions Max 0.999808 +eval/Actions Min -0.999441 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27102e-05 +time/evaluation sampling (s) 4.94988 +time/exploration sampling (s) 7.10979 +time/logging (s) 0.0112571 +time/saving (s) 0.0157031 +time/training (s) 19.4173 +time/epoch (s) 31.5039 +time/total (s) 8869.57 +Epoch -569 +------------------------------ ---------------- +2022-05-15 20:30:48.670200 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -568 finished +------------------------------ ---------------- +epoch -568 +replay_buffer/size 999047 +trainer/num train calls 433000 +trainer/QF1 Loss 1.10227 +trainer/QF2 Loss 0.929393 +trainer/Policy Loss 4.8078 +trainer/Q1 Predictions Mean -73.9003 +trainer/Q1 Predictions Std 16.9702 +trainer/Q1 Predictions Max -0.972348 +trainer/Q1 Predictions Min -87.0283 +trainer/Q2 Predictions Mean -73.9086 +trainer/Q2 Predictions Std 16.9437 +trainer/Q2 Predictions Max -0.84284 +trainer/Q2 Predictions Min -86.8717 +trainer/Q Targets Mean -73.3869 +trainer/Q Targets Std 16.8899 +trainer/Q Targets Max -1.3419 +trainer/Q Targets Min -86.6364 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000135461 +trainer/policy/mean Std 0.716521 +trainer/policy/mean Max 0.99869 +trainer/policy/mean Min -0.99798 +trainer/policy/std Mean 0.422322 +trainer/policy/std Std 0.0209296 +trainer/policy/std Max 0.445879 +trainer/policy/std Min 0.390434 +trainer/Advantage Weights Mean 1.54671 +trainer/Advantage Weights Std 11.3466 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.14274e-20 +trainer/Advantage Score Mean -0.695145 +trainer/Advantage Score Std 0.601895 +trainer/Advantage Score Max 0.993553 +trainer/Advantage Score Min -4.59183 +trainer/V1 Predictions Mean -73.0705 +trainer/V1 Predictions Std 17.0683 +trainer/V1 Predictions Max -0.186122 +trainer/V1 Predictions Min -86.352 +trainer/VF Loss 0.0915508 +expl/num steps total 433000 +expl/num paths total 519 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0362922 +expl/Actions Std 0.831035 +expl/Actions Max 2.50212 +expl/Actions Min -2.40934 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 415658 +eval/num paths total 434 +eval/path length Mean 624 +eval/path length Std 0 +eval/path length Max 624 +eval/path length Min 624 +eval/Rewards Mean 0.00160256 +eval/Rewards Std 0.0399999 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0330515 +eval/Actions Std 0.719879 +eval/Actions Max 0.999049 +eval/Actions Min -0.999272 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.31507e-05 +time/evaluation sampling (s) 5.26146 +time/exploration sampling (s) 7.55395 +time/logging (s) 0.0108254 +time/saving (s) 0.0193555 +time/training (s) 19.2778 +time/epoch (s) 32.1234 +time/total (s) 8901.7 +Epoch -568 +------------------------------ ---------------- +2022-05-15 20:31:21.061029 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -567 finished +------------------------------ ---------------- +epoch -567 +replay_buffer/size 999047 +trainer/num train calls 434000 +trainer/QF1 Loss 0.592346 +trainer/QF2 Loss 0.551982 +trainer/Policy Loss 7.01745 +trainer/Q1 Predictions Mean -74.3308 +trainer/Q1 Predictions Std 18.3447 +trainer/Q1 Predictions Max -1.02257 +trainer/Q1 Predictions Min -86.3779 +trainer/Q2 Predictions Mean -74.2232 +trainer/Q2 Predictions Std 18.3725 +trainer/Q2 Predictions Max -0.303523 +trainer/Q2 Predictions Min -86.2966 +trainer/Q Targets Mean -74.1313 +trainer/Q Targets Std 18.1792 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1435 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00222646 +trainer/policy/mean Std 0.722344 +trainer/policy/mean Max 0.999128 +trainer/policy/mean Min -0.998739 +trainer/policy/std Mean 0.423116 +trainer/policy/std Std 0.0208287 +trainer/policy/std Max 0.445215 +trainer/policy/std Min 0.389542 +trainer/Advantage Weights Mean 1.52655 +trainer/Advantage Weights Std 10.824 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.17636e-14 +trainer/Advantage Score Mean -0.435718 +trainer/Advantage Score Std 0.479857 +trainer/Advantage Score Max 1.8292 +trainer/Advantage Score Min -3.20738 +trainer/V1 Predictions Mean -73.8567 +trainer/V1 Predictions Std 18.1206 +trainer/V1 Predictions Max 0.196076 +trainer/V1 Predictions Min -86.2176 +trainer/VF Loss 0.0579762 +expl/num steps total 434000 +expl/num paths total 521 +expl/path length Mean 500 +expl/path length Std 360 +expl/path length Max 860 +expl/path length Min 140 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0473703 +expl/Actions Std 0.811206 +expl/Actions Max 2.42406 +expl/Actions Min -2.37988 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 416658 +eval/num paths total 435 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0155088 +eval/Actions Std 0.713482 +eval/Actions Max 0.999588 +eval/Actions Min -0.999447 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.03861e-05 +time/evaluation sampling (s) 5.48724 +time/exploration sampling (s) 7.27839 +time/logging (s) 0.0126292 +time/saving (s) 0.0194621 +time/training (s) 19.5792 +time/epoch (s) 32.3769 +time/total (s) 8934.08 +Epoch -567 +------------------------------ ---------------- +2022-05-15 20:31:52.524372 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -566 finished +------------------------------ ---------------- +epoch -566 +replay_buffer/size 999047 +trainer/num train calls 435000 +trainer/QF1 Loss 0.568056 +trainer/QF2 Loss 0.560829 +trainer/Policy Loss 10.7763 +trainer/Q1 Predictions Mean -73.8055 +trainer/Q1 Predictions Std 16.6779 +trainer/Q1 Predictions Max -2.01021 +trainer/Q1 Predictions Min -86.4057 +trainer/Q2 Predictions Mean -73.8188 +trainer/Q2 Predictions Std 16.6723 +trainer/Q2 Predictions Max -1.94709 +trainer/Q2 Predictions Min -86.6773 +trainer/Q Targets Mean -73.8124 +trainer/Q Targets Std 16.6659 +trainer/Q Targets Max -2.46503 +trainer/Q Targets Min -86.6985 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00641623 +trainer/policy/mean Std 0.70979 +trainer/policy/mean Max 0.999012 +trainer/policy/mean Min -0.99732 +trainer/policy/std Mean 0.420948 +trainer/policy/std Std 0.0204865 +trainer/policy/std Max 0.442535 +trainer/policy/std Min 0.388361 +trainer/Advantage Weights Mean 2.61529 +trainer/Advantage Weights Std 13.2202 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.26012e-15 +trainer/Advantage Score Mean -0.507469 +trainer/Advantage Score Std 0.549418 +trainer/Advantage Score Max 1.60289 +trainer/Advantage Score Min -3.24273 +trainer/V1 Predictions Mean -73.5285 +trainer/V1 Predictions Std 16.8589 +trainer/V1 Predictions Max -2.39762 +trainer/V1 Predictions Min -86.569 +trainer/VF Loss 0.0801961 +expl/num steps total 435000 +expl/num paths total 522 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.240063 +expl/Actions Std 0.895239 +expl/Actions Max 2.37863 +expl/Actions Min -2.3809 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 417421 +eval/num paths total 436 +eval/path length Mean 763 +eval/path length Std 0 +eval/path length Max 763 +eval/path length Min 763 +eval/Rewards Mean 0.00131062 +eval/Rewards Std 0.0361787 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0242139 +eval/Actions Std 0.738917 +eval/Actions Max 0.998693 +eval/Actions Min -0.999056 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0822e-05 +time/evaluation sampling (s) 5.04479 +time/exploration sampling (s) 6.85822 +time/logging (s) 0.00806658 +time/saving (s) 0.0163648 +time/training (s) 19.5157 +time/epoch (s) 31.4432 +time/total (s) 8965.53 +Epoch -566 +------------------------------ ---------------- +2022-05-15 20:32:24.030311 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -565 finished +------------------------------ ---------------- +epoch -565 +replay_buffer/size 999047 +trainer/num train calls 436000 +trainer/QF1 Loss 1.25748 +trainer/QF2 Loss 1.20701 +trainer/Policy Loss 40.1523 +trainer/Q1 Predictions Mean -72.8316 +trainer/Q1 Predictions Std 18.2942 +trainer/Q1 Predictions Max -0.11291 +trainer/Q1 Predictions Min -86.5006 +trainer/Q2 Predictions Mean -72.9152 +trainer/Q2 Predictions Std 18.3144 +trainer/Q2 Predictions Max -0.352739 +trainer/Q2 Predictions Min -86.5473 +trainer/Q Targets Mean -73.5283 +trainer/Q Targets Std 18.1286 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4922 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000208708 +trainer/policy/mean Std 0.710532 +trainer/policy/mean Max 0.998472 +trainer/policy/mean Min -0.996616 +trainer/policy/std Mean 0.421258 +trainer/policy/std Std 0.021211 +trainer/policy/std Max 0.446925 +trainer/policy/std Min 0.386885 +trainer/Advantage Weights Mean 10.483 +trainer/Advantage Weights Std 24.26 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.20114e-13 +trainer/Advantage Score Mean -0.136247 +trainer/Advantage Score Std 0.560793 +trainer/Advantage Score Max 2.35218 +trainer/Advantage Score Min -2.91446 +trainer/V1 Predictions Mean -73.2115 +trainer/V1 Predictions Std 18.2928 +trainer/V1 Predictions Max -0.172645 +trainer/V1 Predictions Min -86.3782 +trainer/VF Loss 0.0838858 +expl/num steps total 436000 +expl/num paths total 523 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0509582 +expl/Actions Std 0.822163 +expl/Actions Max 2.42044 +expl/Actions Min -2.31833 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 418421 +eval/num paths total 437 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0205901 +eval/Actions Std 0.726391 +eval/Actions Max 0.999772 +eval/Actions Min -0.999735 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.58373e-06 +time/evaluation sampling (s) 4.74917 +time/exploration sampling (s) 6.4133 +time/logging (s) 0.0126208 +time/saving (s) 0.0189009 +time/training (s) 20.3027 +time/epoch (s) 31.4967 +time/total (s) 8997.04 +Epoch -565 +------------------------------ ---------------- +2022-05-15 20:32:55.213063 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -564 finished +------------------------------ ---------------- +epoch -564 +replay_buffer/size 999047 +trainer/num train calls 437000 +trainer/QF1 Loss 3.20223 +trainer/QF2 Loss 3.02104 +trainer/Policy Loss 33.5815 +trainer/Q1 Predictions Mean -73.1712 +trainer/Q1 Predictions Std 19.1486 +trainer/Q1 Predictions Max -1.51252 +trainer/Q1 Predictions Min -87.9718 +trainer/Q2 Predictions Mean -73.2287 +trainer/Q2 Predictions Std 19.1572 +trainer/Q2 Predictions Max -1.54021 +trainer/Q2 Predictions Min -87.7279 +trainer/Q Targets Mean -73.3148 +trainer/Q Targets Std 19.515 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5208 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.022871 +trainer/policy/mean Std 0.724622 +trainer/policy/mean Max 0.99955 +trainer/policy/mean Min -0.999859 +trainer/policy/std Mean 0.422304 +trainer/policy/std Std 0.0208196 +trainer/policy/std Max 0.448092 +trainer/policy/std Min 0.387111 +trainer/Advantage Weights Mean 8.15941 +trainer/Advantage Weights Std 22.5018 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.51791e-20 +trainer/Advantage Score Mean -0.237142 +trainer/Advantage Score Std 0.720995 +trainer/Advantage Score Max 2.25051 +trainer/Advantage Score Min -4.39095 +trainer/V1 Predictions Mean -73.1128 +trainer/V1 Predictions Std 19.3878 +trainer/V1 Predictions Max 0.276688 +trainer/V1 Predictions Min -88.7532 +trainer/VF Loss 0.0993878 +expl/num steps total 437000 +expl/num paths total 525 +expl/path length Mean 500 +expl/path length Std 271 +expl/path length Max 771 +expl/path length Min 229 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0571523 +expl/Actions Std 0.829045 +expl/Actions Max 2.43124 +expl/Actions Min -2.26632 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 419421 +eval/num paths total 438 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00049085 +eval/Actions Std 0.723649 +eval/Actions Max 0.999479 +eval/Actions Min -0.999205 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.28462e-05 +time/evaluation sampling (s) 5.16214 +time/exploration sampling (s) 6.60975 +time/logging (s) 0.0115578 +time/saving (s) 0.0154773 +time/training (s) 19.3671 +time/epoch (s) 31.166 +time/total (s) 9028.21 +Epoch -564 +------------------------------ ---------------- +2022-05-15 20:33:26.572522 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -563 finished +------------------------------ ---------------- +epoch -563 +replay_buffer/size 999047 +trainer/num train calls 438000 +trainer/QF1 Loss 0.515011 +trainer/QF2 Loss 0.442855 +trainer/Policy Loss 18.5443 +trainer/Q1 Predictions Mean -75.9971 +trainer/Q1 Predictions Std 15.0248 +trainer/Q1 Predictions Max -2.13668 +trainer/Q1 Predictions Min -85.9431 +trainer/Q2 Predictions Mean -75.9844 +trainer/Q2 Predictions Std 14.9424 +trainer/Q2 Predictions Max -2.92665 +trainer/Q2 Predictions Min -85.9896 +trainer/Q Targets Mean -76.3162 +trainer/Q Targets Std 14.7772 +trainer/Q Targets Max -3.4103 +trainer/Q Targets Min -86.455 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.025306 +trainer/policy/mean Std 0.704172 +trainer/policy/mean Max 0.99985 +trainer/policy/mean Min -0.999495 +trainer/policy/std Mean 0.423149 +trainer/policy/std Std 0.0210149 +trainer/policy/std Max 0.447011 +trainer/policy/std Min 0.387149 +trainer/Advantage Weights Mean 3.92601 +trainer/Advantage Weights Std 14.5424 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.25979e-14 +trainer/Advantage Score Mean -0.221712 +trainer/Advantage Score Std 0.524904 +trainer/Advantage Score Max 2.40516 +trainer/Advantage Score Min -3.14209 +trainer/V1 Predictions Mean -76.073 +trainer/V1 Predictions Std 14.913 +trainer/V1 Predictions Max -3.33678 +trainer/V1 Predictions Min -86.2219 +trainer/VF Loss 0.0632999 +expl/num steps total 438000 +expl/num paths total 527 +expl/path length Mean 500 +expl/path length Std 219 +expl/path length Max 719 +expl/path length Min 281 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0337511 +expl/Actions Std 0.816155 +expl/Actions Max 2.29899 +expl/Actions Min -2.5278 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 420297 +eval/num paths total 439 +eval/path length Mean 876 +eval/path length Std 0 +eval/path length Max 876 +eval/path length Min 876 +eval/Rewards Mean 0.00114155 +eval/Rewards Std 0.0337676 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0136811 +eval/Actions Std 0.716897 +eval/Actions Max 0.999231 +eval/Actions Min -0.999467 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2802e-05 +time/evaluation sampling (s) 4.79477 +time/exploration sampling (s) 6.79151 +time/logging (s) 0.0118943 +time/saving (s) 0.0187409 +time/training (s) 19.7286 +time/epoch (s) 31.3455 +time/total (s) 9059.56 +Epoch -563 +------------------------------ ---------------- +2022-05-15 20:33:58.413452 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -562 finished +------------------------------ ---------------- +epoch -562 +replay_buffer/size 999047 +trainer/num train calls 439000 +trainer/QF1 Loss 0.715573 +trainer/QF2 Loss 0.708227 +trainer/Policy Loss 8.86702 +trainer/Q1 Predictions Mean -73.3698 +trainer/Q1 Predictions Std 17.3834 +trainer/Q1 Predictions Max -1.47889 +trainer/Q1 Predictions Min -87.3793 +trainer/Q2 Predictions Mean -73.3682 +trainer/Q2 Predictions Std 17.4561 +trainer/Q2 Predictions Max -1.02563 +trainer/Q2 Predictions Min -87.2018 +trainer/Q Targets Mean -73.6935 +trainer/Q Targets Std 17.4813 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9099 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0107655 +trainer/policy/mean Std 0.720061 +trainer/policy/mean Max 0.999245 +trainer/policy/mean Min -0.999971 +trainer/policy/std Mean 0.422751 +trainer/policy/std Std 0.0210131 +trainer/policy/std Max 0.445978 +trainer/policy/std Min 0.386518 +trainer/Advantage Weights Mean 2.31431 +trainer/Advantage Weights Std 10.7521 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.00463e-20 +trainer/Advantage Score Mean -0.317083 +trainer/Advantage Score Std 0.539928 +trainer/Advantage Score Max 0.743141 +trainer/Advantage Score Min -4.44413 +trainer/V1 Predictions Mean -73.4007 +trainer/V1 Predictions Std 17.681 +trainer/V1 Predictions Max -0.132908 +trainer/V1 Predictions Min -87.5807 +trainer/VF Loss 0.0457068 +expl/num steps total 439000 +expl/num paths total 529 +expl/path length Mean 500 +expl/path length Std 278 +expl/path length Max 778 +expl/path length Min 222 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00380143 +expl/Actions Std 0.826552 +expl/Actions Max 2.12671 +expl/Actions Min -2.39146 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 421297 +eval/num paths total 440 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.239844 +eval/Actions Std 0.730762 +eval/Actions Max 0.999977 +eval/Actions Min -0.999447 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.36159e-05 +time/evaluation sampling (s) 5.03905 +time/exploration sampling (s) 6.54019 +time/logging (s) 0.00979461 +time/saving (s) 0.0156953 +time/training (s) 20.2194 +time/epoch (s) 31.8241 +time/total (s) 9091.39 +Epoch -562 +------------------------------ ---------------- +2022-05-15 20:34:30.636656 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -561 finished +------------------------------ ---------------- +epoch -561 +replay_buffer/size 999047 +trainer/num train calls 440000 +trainer/QF1 Loss 0.989623 +trainer/QF2 Loss 1.02125 +trainer/Policy Loss 30.2553 +trainer/Q1 Predictions Mean -72.3348 +trainer/Q1 Predictions Std 19.3117 +trainer/Q1 Predictions Max 0.849114 +trainer/Q1 Predictions Min -87.6782 +trainer/Q2 Predictions Mean -72.4187 +trainer/Q2 Predictions Std 19.2503 +trainer/Q2 Predictions Max -0.322016 +trainer/Q2 Predictions Min -87.474 +trainer/Q Targets Mean -72.4463 +trainer/Q Targets Std 19.3004 +trainer/Q Targets Max 3.54353 +trainer/Q Targets Min -87.1917 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0332573 +trainer/policy/mean Std 0.71941 +trainer/policy/mean Max 0.998537 +trainer/policy/mean Min -0.997284 +trainer/policy/std Mean 0.422853 +trainer/policy/std Std 0.021232 +trainer/policy/std Max 0.445881 +trainer/policy/std Min 0.386972 +trainer/Advantage Weights Mean 7.07617 +trainer/Advantage Weights Std 22.1715 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19867e-18 +trainer/Advantage Score Mean -0.245637 +trainer/Advantage Score Std 0.600388 +trainer/Advantage Score Max 2.56432 +trainer/Advantage Score Min -4.12653 +trainer/V1 Predictions Mean -72.1757 +trainer/V1 Predictions Std 19.3133 +trainer/V1 Predictions Max 3.80694 +trainer/V1 Predictions Min -87.2667 +trainer/VF Loss 0.0956181 +expl/num steps total 440000 +expl/num paths total 530 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0353225 +expl/Actions Std 0.82583 +expl/Actions Max 2.38282 +expl/Actions Min -2.56472 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 422297 +eval/num paths total 441 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0310435 +eval/Actions Std 0.737543 +eval/Actions Max 0.999493 +eval/Actions Min -0.999689 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.17761e-05 +time/evaluation sampling (s) 4.9728 +time/exploration sampling (s) 6.79615 +time/logging (s) 0.0121299 +time/saving (s) 0.0156521 +time/training (s) 20.4179 +time/epoch (s) 32.2147 +time/total (s) 9123.61 +Epoch -561 +------------------------------ ---------------- +2022-05-15 20:35:02.632976 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -560 finished +------------------------------ ---------------- +epoch -560 +replay_buffer/size 999047 +trainer/num train calls 441000 +trainer/QF1 Loss 0.948739 +trainer/QF2 Loss 0.827819 +trainer/Policy Loss 54.6832 +trainer/Q1 Predictions Mean -73.3007 +trainer/Q1 Predictions Std 18.2408 +trainer/Q1 Predictions Max -0.717557 +trainer/Q1 Predictions Min -86.0828 +trainer/Q2 Predictions Mean -73.1817 +trainer/Q2 Predictions Std 18.3485 +trainer/Q2 Predictions Max -0.702239 +trainer/Q2 Predictions Min -86.2831 +trainer/Q Targets Mean -73.5763 +trainer/Q Targets Std 18.585 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6502 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00681645 +trainer/policy/mean Std 0.716842 +trainer/policy/mean Max 0.99973 +trainer/policy/mean Min -0.99872 +trainer/policy/std Mean 0.422958 +trainer/policy/std Std 0.0215295 +trainer/policy/std Max 0.446553 +trainer/policy/std Min 0.385393 +trainer/Advantage Weights Mean 12.059 +trainer/Advantage Weights Std 25.6 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.07734e-20 +trainer/Advantage Score Mean -0.174348 +trainer/Advantage Score Std 0.794913 +trainer/Advantage Score Max 3.36051 +trainer/Advantage Score Min -4.38459 +trainer/V1 Predictions Mean -73.2207 +trainer/V1 Predictions Std 18.8128 +trainer/V1 Predictions Max 3.30731 +trainer/V1 Predictions Min -86.6438 +trainer/VF Loss 0.137308 +expl/num steps total 441000 +expl/num paths total 531 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00821465 +expl/Actions Std 0.851929 +expl/Actions Max 2.67942 +expl/Actions Min -2.59011 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 423297 +eval/num paths total 442 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.469992 +eval/Actions Std 0.565298 +eval/Actions Max 0.99915 +eval/Actions Min -0.999657 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.42866e-06 +time/evaluation sampling (s) 5.16292 +time/exploration sampling (s) 7.65744 +time/logging (s) 0.0115916 +time/saving (s) 0.0168917 +time/training (s) 19.1352 +time/epoch (s) 31.984 +time/total (s) 9155.6 +Epoch -560 +------------------------------ ---------------- +2022-05-15 20:35:34.095308 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -559 finished +------------------------------ ---------------- +epoch -559 +replay_buffer/size 999047 +trainer/num train calls 442000 +trainer/QF1 Loss 0.968097 +trainer/QF2 Loss 0.876279 +trainer/Policy Loss 15.7023 +trainer/Q1 Predictions Mean -72.3887 +trainer/Q1 Predictions Std 19.2375 +trainer/Q1 Predictions Max -2.39802 +trainer/Q1 Predictions Min -87.4235 +trainer/Q2 Predictions Mean -72.3558 +trainer/Q2 Predictions Std 19.3373 +trainer/Q2 Predictions Max -2.53402 +trainer/Q2 Predictions Min -87.4404 +trainer/Q Targets Mean -72.1538 +trainer/Q Targets Std 19.4394 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0358 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.025512 +trainer/policy/mean Std 0.717804 +trainer/policy/mean Max 0.999785 +trainer/policy/mean Min -0.999236 +trainer/policy/std Mean 0.422829 +trainer/policy/std Std 0.0217828 +trainer/policy/std Max 0.44665 +trainer/policy/std Min 0.385703 +trainer/Advantage Weights Mean 3.60291 +trainer/Advantage Weights Std 17.2838 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.35223e-15 +trainer/Advantage Score Mean -0.443918 +trainer/Advantage Score Std 0.549517 +trainer/Advantage Score Max 1.37307 +trainer/Advantage Score Min -3.23032 +trainer/V1 Predictions Mean -71.8941 +trainer/V1 Predictions Std 19.4355 +trainer/V1 Predictions Max -2.61326 +trainer/V1 Predictions Min -86.7939 +trainer/VF Loss 0.0724362 +expl/num steps total 442000 +expl/num paths total 532 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0232193 +expl/Actions Std 0.834144 +expl/Actions Max 2.33225 +expl/Actions Min -2.56431 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 424297 +eval/num paths total 443 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.201877 +eval/Actions Std 0.767311 +eval/Actions Max 0.999288 +eval/Actions Min -0.999594 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.43099e-06 +time/evaluation sampling (s) 4.75847 +time/exploration sampling (s) 7.12795 +time/logging (s) 0.0123488 +time/saving (s) 0.0177657 +time/training (s) 19.5333 +time/epoch (s) 31.4499 +time/total (s) 9187.06 +Epoch -559 +------------------------------ ---------------- +2022-05-15 20:36:06.405180 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -558 finished +------------------------------ ---------------- +epoch -558 +replay_buffer/size 999047 +trainer/num train calls 443000 +trainer/QF1 Loss 0.522812 +trainer/QF2 Loss 0.472158 +trainer/Policy Loss 7.70269 +trainer/Q1 Predictions Mean -73.837 +trainer/Q1 Predictions Std 16.7733 +trainer/Q1 Predictions Max -0.603569 +trainer/Q1 Predictions Min -87.723 +trainer/Q2 Predictions Mean -73.8377 +trainer/Q2 Predictions Std 16.7713 +trainer/Q2 Predictions Max -0.0261441 +trainer/Q2 Predictions Min -87.5384 +trainer/Q Targets Mean -73.4624 +trainer/Q Targets Std 16.8404 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.151 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0163281 +trainer/policy/mean Std 0.721955 +trainer/policy/mean Max 0.997708 +trainer/policy/mean Min -0.995362 +trainer/policy/std Mean 0.422018 +trainer/policy/std Std 0.0203728 +trainer/policy/std Max 0.441654 +trainer/policy/std Min 0.389291 +trainer/Advantage Weights Mean 2.11689 +trainer/Advantage Weights Std 12.1009 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.26167e-25 +trainer/Advantage Score Mean -0.503866 +trainer/Advantage Score Std 0.63886 +trainer/Advantage Score Max 0.776226 +trainer/Advantage Score Min -5.59042 +trainer/V1 Predictions Mean -73.1432 +trainer/V1 Predictions Std 17.077 +trainer/V1 Predictions Max 1.34109 +trainer/V1 Predictions Min -87.026 +trainer/VF Loss 0.072538 +expl/num steps total 443000 +expl/num paths total 534 +expl/path length Mean 500 +expl/path length Std 246 +expl/path length Max 746 +expl/path length Min 254 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0218166 +expl/Actions Std 0.804966 +expl/Actions Max 2.37117 +expl/Actions Min -2.51825 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 425086 +eval/num paths total 444 +eval/path length Mean 789 +eval/path length Std 0 +eval/path length Max 789 +eval/path length Min 789 +eval/Rewards Mean 0.00126743 +eval/Rewards Std 0.0355784 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0214723 +eval/Actions Std 0.73278 +eval/Actions Max 0.997998 +eval/Actions Min -0.999283 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.26944e-05 +time/evaluation sampling (s) 5.20888 +time/exploration sampling (s) 7.10274 +time/logging (s) 0.0117728 +time/saving (s) 0.0186091 +time/training (s) 19.9517 +time/epoch (s) 32.2937 +time/total (s) 9219.36 +Epoch -558 +------------------------------ ---------------- +2022-05-15 20:36:38.589137 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -557 finished +------------------------------ ---------------- +epoch -557 +replay_buffer/size 999047 +trainer/num train calls 444000 +trainer/QF1 Loss 0.672612 +trainer/QF2 Loss 0.650485 +trainer/Policy Loss 51.8506 +trainer/Q1 Predictions Mean -72.8029 +trainer/Q1 Predictions Std 19.3651 +trainer/Q1 Predictions Max -0.718675 +trainer/Q1 Predictions Min -87.1567 +trainer/Q2 Predictions Mean -72.9022 +trainer/Q2 Predictions Std 19.5086 +trainer/Q2 Predictions Max -0.175441 +trainer/Q2 Predictions Min -87.3055 +trainer/Q Targets Mean -72.9688 +trainer/Q Targets Std 19.7834 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5452 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00254354 +trainer/policy/mean Std 0.715983 +trainer/policy/mean Max 0.999013 +trainer/policy/mean Min -0.998924 +trainer/policy/std Mean 0.421473 +trainer/policy/std Std 0.0211757 +trainer/policy/std Max 0.443492 +trainer/policy/std Min 0.386794 +trainer/Advantage Weights Mean 6.52545 +trainer/Advantage Weights Std 21.0758 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.6007e-15 +trainer/Advantage Score Mean -0.263208 +trainer/Advantage Score Std 0.580284 +trainer/Advantage Score Max 1.0421 +trainer/Advantage Score Min -3.40683 +trainer/V1 Predictions Mean -72.7671 +trainer/V1 Predictions Std 19.7551 +trainer/V1 Predictions Max 0.410158 +trainer/V1 Predictions Min -87.1117 +trainer/VF Loss 0.0614401 +expl/num steps total 444000 +expl/num paths total 536 +expl/path length Mean 500 +expl/path length Std 173 +expl/path length Max 673 +expl/path length Min 327 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0293751 +expl/Actions Std 0.831506 +expl/Actions Max 2.32753 +expl/Actions Min -2.16237 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 425594 +eval/num paths total 445 +eval/path length Mean 508 +eval/path length Std 0 +eval/path length Max 508 +eval/path length Min 508 +eval/Rewards Mean 0.0019685 +eval/Rewards Std 0.0443241 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.027828 +eval/Actions Std 0.729317 +eval/Actions Max 0.998723 +eval/Actions Min -0.999426 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.33011e-05 +time/evaluation sampling (s) 4.65019 +time/exploration sampling (s) 7.40415 +time/logging (s) 0.0104381 +time/saving (s) 0.0187726 +time/training (s) 20.0818 +time/epoch (s) 32.1653 +time/total (s) 9251.54 +Epoch -557 +------------------------------ ---------------- +2022-05-15 20:37:10.630139 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -556 finished +------------------------------ --------------- +epoch -556 +replay_buffer/size 999047 +trainer/num train calls 445000 +trainer/QF1 Loss 0.593246 +trainer/QF2 Loss 0.57017 +trainer/Policy Loss 7.71846 +trainer/Q1 Predictions Mean -73.9407 +trainer/Q1 Predictions Std 17.3707 +trainer/Q1 Predictions Max -0.175019 +trainer/Q1 Predictions Min -87.0337 +trainer/Q2 Predictions Mean -73.9718 +trainer/Q2 Predictions Std 17.3391 +trainer/Q2 Predictions Max -0.369182 +trainer/Q2 Predictions Min -87.052 +trainer/Q Targets Mean -73.7109 +trainer/Q Targets Std 17.3864 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9416 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0137762 +trainer/policy/mean Std 0.719025 +trainer/policy/mean Max 0.999071 +trainer/policy/mean Min -0.999187 +trainer/policy/std Mean 0.421842 +trainer/policy/std Std 0.0208843 +trainer/policy/std Max 0.444584 +trainer/policy/std Min 0.386831 +trainer/Advantage Weights Mean 0.890698 +trainer/Advantage Weights Std 8.10188 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.363e-16 +trainer/Advantage Score Mean -0.658037 +trainer/Advantage Score Std 0.586512 +trainer/Advantage Score Max 0.528214 +trainer/Advantage Score Min -3.48449 +trainer/V1 Predictions Mean -73.4545 +trainer/V1 Predictions Std 17.5618 +trainer/V1 Predictions Max 1.45987 +trainer/V1 Predictions Min -86.6522 +trainer/VF Loss 0.0796679 +expl/num steps total 445000 +expl/num paths total 537 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0287239 +expl/Actions Std 0.81637 +expl/Actions Max 2.30858 +expl/Actions Min -2.27971 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 426594 +eval/num paths total 446 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0304164 +eval/Actions Std 0.795033 +eval/Actions Max 0.999595 +eval/Actions Min -0.998277 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.1849e-06 +time/evaluation sampling (s) 5.00237 +time/exploration sampling (s) 7.32285 +time/logging (s) 0.00747534 +time/saving (s) 0.0113549 +time/training (s) 19.6785 +time/epoch (s) 32.0225 +time/total (s) 9283.56 +Epoch -556 +------------------------------ --------------- +2022-05-15 20:37:42.743936 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -555 finished +------------------------------ ---------------- +epoch -555 +replay_buffer/size 999047 +trainer/num train calls 446000 +trainer/QF1 Loss 1.38038 +trainer/QF2 Loss 1.17633 +trainer/Policy Loss 95.7974 +trainer/Q1 Predictions Mean -73.5798 +trainer/Q1 Predictions Std 17.3537 +trainer/Q1 Predictions Max -1.55726 +trainer/Q1 Predictions Min -86.2917 +trainer/Q2 Predictions Mean -73.6748 +trainer/Q2 Predictions Std 17.3605 +trainer/Q2 Predictions Max -0.586097 +trainer/Q2 Predictions Min -86.2476 +trainer/Q Targets Mean -74.4395 +trainer/Q Targets Std 17.5713 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.788 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00584477 +trainer/policy/mean Std 0.73037 +trainer/policy/mean Max 0.997774 +trainer/policy/mean Min -0.998527 +trainer/policy/std Mean 0.42412 +trainer/policy/std Std 0.0200236 +trainer/policy/std Max 0.449044 +trainer/policy/std Min 0.393187 +trainer/Advantage Weights Mean 20.0798 +trainer/Advantage Weights Std 32.8648 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.35162e-10 +trainer/Advantage Score Mean 0.0645494 +trainer/Advantage Score Std 0.458976 +trainer/Advantage Score Max 2.28408 +trainer/Advantage Score Min -2.18164 +trainer/V1 Predictions Mean -74.2559 +trainer/V1 Predictions Std 17.4435 +trainer/V1 Predictions Max -1.00962 +trainer/V1 Predictions Min -86.761 +trainer/VF Loss 0.109225 +expl/num steps total 446000 +expl/num paths total 538 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0508865 +expl/Actions Std 0.817693 +expl/Actions Max 2.63818 +expl/Actions Min -2.59549 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 427250 +eval/num paths total 447 +eval/path length Mean 656 +eval/path length Std 0 +eval/path length Max 656 +eval/path length Min 656 +eval/Rewards Mean 0.00152439 +eval/Rewards Std 0.0390137 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0417802 +eval/Actions Std 0.732198 +eval/Actions Max 0.999116 +eval/Actions Min -0.99916 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.20387e-05 +time/evaluation sampling (s) 4.85514 +time/exploration sampling (s) 7.52608 +time/logging (s) 0.0105902 +time/saving (s) 0.0220874 +time/training (s) 19.6934 +time/epoch (s) 32.1073 +time/total (s) 9315.68 +Epoch -555 +------------------------------ ---------------- +2022-05-15 20:38:15.714982 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -554 finished +------------------------------ ---------------- +epoch -554 +replay_buffer/size 999047 +trainer/num train calls 447000 +trainer/QF1 Loss 0.95487 +trainer/QF2 Loss 1.03915 +trainer/Policy Loss 15.1761 +trainer/Q1 Predictions Mean -71.8302 +trainer/Q1 Predictions Std 19.5287 +trainer/Q1 Predictions Max -0.881049 +trainer/Q1 Predictions Min -87.191 +trainer/Q2 Predictions Mean -71.821 +trainer/Q2 Predictions Std 19.5663 +trainer/Q2 Predictions Max -0.567332 +trainer/Q2 Predictions Min -87.1048 +trainer/Q Targets Mean -71.9423 +trainer/Q Targets Std 19.3265 +trainer/Q Targets Max -3.2979 +trainer/Q Targets Min -86.508 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0197687 +trainer/policy/mean Std 0.717164 +trainer/policy/mean Max 0.999113 +trainer/policy/mean Min -0.99976 +trainer/policy/std Mean 0.425102 +trainer/policy/std Std 0.0200368 +trainer/policy/std Max 0.448396 +trainer/policy/std Min 0.393777 +trainer/Advantage Weights Mean 3.81707 +trainer/Advantage Weights Std 15.629 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.23271e-19 +trainer/Advantage Score Mean -0.383708 +trainer/Advantage Score Std 0.593352 +trainer/Advantage Score Max 0.593728 +trainer/Advantage Score Min -4.35399 +trainer/V1 Predictions Mean -71.6664 +trainer/V1 Predictions Std 19.5384 +trainer/V1 Predictions Max -0.710489 +trainer/V1 Predictions Min -86.1977 +trainer/VF Loss 0.0595495 +expl/num steps total 447000 +expl/num paths total 539 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0916951 +expl/Actions Std 0.811485 +expl/Actions Max 2.3757 +expl/Actions Min -2.16598 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 428250 +eval/num paths total 448 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.239317 +eval/Actions Std 0.694902 +eval/Actions Max 0.99955 +eval/Actions Min -0.998677 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.28105e-06 +time/evaluation sampling (s) 5.41474 +time/exploration sampling (s) 7.65051 +time/logging (s) 0.00886288 +time/saving (s) 0.0150096 +time/training (s) 19.8655 +time/epoch (s) 32.9547 +time/total (s) 9348.64 +Epoch -554 +------------------------------ ---------------- +2022-05-15 20:38:47.465497 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -553 finished +------------------------------ ---------------- +epoch -553 +replay_buffer/size 999047 +trainer/num train calls 448000 +trainer/QF1 Loss 1.25152 +trainer/QF2 Loss 1.3744 +trainer/Policy Loss 2.45111 +trainer/Q1 Predictions Mean -72.1347 +trainer/Q1 Predictions Std 20.2989 +trainer/Q1 Predictions Max -0.733468 +trainer/Q1 Predictions Min -86.831 +trainer/Q2 Predictions Mean -72.1061 +trainer/Q2 Predictions Std 20.3755 +trainer/Q2 Predictions Max -0.466101 +trainer/Q2 Predictions Min -87.0769 +trainer/Q Targets Mean -71.3858 +trainer/Q Targets Std 20.0477 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4219 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00358361 +trainer/policy/mean Std 0.712896 +trainer/policy/mean Max 0.997537 +trainer/policy/mean Min -0.997607 +trainer/policy/std Mean 0.422645 +trainer/policy/std Std 0.0216219 +trainer/policy/std Max 0.444174 +trainer/policy/std Min 0.386758 +trainer/Advantage Weights Mean 0.407661 +trainer/Advantage Weights Std 6.23838 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.08599e-15 +trainer/Advantage Score Mean -0.905703 +trainer/Advantage Score Std 0.480927 +trainer/Advantage Score Max 0.674162 +trainer/Advantage Score Min -3.34119 +trainer/V1 Predictions Mean -71.016 +trainer/V1 Predictions Std 20.4217 +trainer/V1 Predictions Max 1.31617 +trainer/V1 Predictions Min -86.2276 +trainer/VF Loss 0.106597 +expl/num steps total 448000 +expl/num paths total 541 +expl/path length Mean 500 +expl/path length Std 471 +expl/path length Max 971 +expl/path length Min 29 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0573983 +expl/Actions Std 0.822065 +expl/Actions Max 2.32302 +expl/Actions Min -2.32182 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 429066 +eval/num paths total 449 +eval/path length Mean 816 +eval/path length Std 0 +eval/path length Max 816 +eval/path length Min 816 +eval/Rewards Mean 0.00122549 +eval/Rewards Std 0.0349855 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0671214 +eval/Actions Std 0.72924 +eval/Actions Max 0.999808 +eval/Actions Min -0.998547 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.9977e-06 +time/evaluation sampling (s) 5.05752 +time/exploration sampling (s) 6.36703 +time/logging (s) 0.0106808 +time/saving (s) 0.0199446 +time/training (s) 20.2875 +time/epoch (s) 31.7427 +time/total (s) 9380.39 +Epoch -553 +------------------------------ ---------------- +2022-05-15 20:39:19.367735 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -552 finished +------------------------------ ---------------- +epoch -552 +replay_buffer/size 999047 +trainer/num train calls 449000 +trainer/QF1 Loss 0.80145 +trainer/QF2 Loss 0.738078 +trainer/Policy Loss 30.4362 +trainer/Q1 Predictions Mean -72.1465 +trainer/Q1 Predictions Std 20.8858 +trainer/Q1 Predictions Max 0.369835 +trainer/Q1 Predictions Min -86.7015 +trainer/Q2 Predictions Mean -72.1953 +trainer/Q2 Predictions Std 20.8988 +trainer/Q2 Predictions Max -0.34584 +trainer/Q2 Predictions Min -86.6314 +trainer/Q Targets Mean -72.3397 +trainer/Q Targets Std 20.7359 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.56 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000901467 +trainer/policy/mean Std 0.727363 +trainer/policy/mean Max 0.999139 +trainer/policy/mean Min -0.999239 +trainer/policy/std Mean 0.421306 +trainer/policy/std Std 0.0225893 +trainer/policy/std Max 0.444778 +trainer/policy/std Min 0.385564 +trainer/Advantage Weights Mean 7.41491 +trainer/Advantage Weights Std 22.7553 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.50593e-14 +trainer/Advantage Score Mean -0.270769 +trainer/Advantage Score Std 0.544768 +trainer/Advantage Score Max 0.886749 +trainer/Advantage Score Min -3.13175 +trainer/V1 Predictions Mean -71.9987 +trainer/V1 Predictions Std 21.0723 +trainer/V1 Predictions Max 1.40441 +trainer/V1 Predictions Min -86.4807 +trainer/VF Loss 0.0588798 +expl/num steps total 449000 +expl/num paths total 543 +expl/path length Mean 500 +expl/path length Std 242 +expl/path length Max 742 +expl/path length Min 258 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.028918 +expl/Actions Std 0.81737 +expl/Actions Max 2.26064 +expl/Actions Min -2.32677 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 430066 +eval/num paths total 450 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0104683 +eval/Actions Std 0.622591 +eval/Actions Max 0.998325 +eval/Actions Min -0.999585 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.59154e-05 +time/evaluation sampling (s) 4.5035 +time/exploration sampling (s) 7.58876 +time/logging (s) 0.0110503 +time/saving (s) 0.0150135 +time/training (s) 19.7709 +time/epoch (s) 31.8893 +time/total (s) 9412.28 +Epoch -552 +------------------------------ ---------------- +2022-05-15 20:39:52.154033 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -551 finished +------------------------------ ---------------- +epoch -551 +replay_buffer/size 999047 +trainer/num train calls 450000 +trainer/QF1 Loss 0.727032 +trainer/QF2 Loss 0.565078 +trainer/Policy Loss 18.5149 +trainer/Q1 Predictions Mean -73.857 +trainer/Q1 Predictions Std 17.3946 +trainer/Q1 Predictions Max -2.48279 +trainer/Q1 Predictions Min -87.3649 +trainer/Q2 Predictions Mean -73.7369 +trainer/Q2 Predictions Std 17.4014 +trainer/Q2 Predictions Max -2.66368 +trainer/Q2 Predictions Min -87.3503 +trainer/Q Targets Mean -73.6543 +trainer/Q Targets Std 17.643 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2344 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0083332 +trainer/policy/mean Std 0.712797 +trainer/policy/mean Max 0.999881 +trainer/policy/mean Min -0.999225 +trainer/policy/std Mean 0.422276 +trainer/policy/std Std 0.0205763 +trainer/policy/std Max 0.445395 +trainer/policy/std Min 0.386694 +trainer/Advantage Weights Mean 4.30622 +trainer/Advantage Weights Std 17.9524 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.58666e-12 +trainer/Advantage Score Mean -0.347603 +trainer/Advantage Score Std 0.473903 +trainer/Advantage Score Max 1.19129 +trainer/Advantage Score Min -2.56046 +trainer/V1 Predictions Mean -73.4915 +trainer/V1 Predictions Std 17.5728 +trainer/V1 Predictions Max -1.37854 +trainer/V1 Predictions Min -87.1969 +trainer/VF Loss 0.0554103 +expl/num steps total 450000 +expl/num paths total 544 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.144221 +expl/Actions Std 0.813961 +expl/Actions Max 2.23379 +expl/Actions Min -2.45373 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 431066 +eval/num paths total 451 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0732408 +eval/Actions Std 0.68003 +eval/Actions Max 0.999761 +eval/Actions Min -0.998945 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2557e-05 +time/evaluation sampling (s) 5.28826 +time/exploration sampling (s) 7.3801 +time/logging (s) 0.00745144 +time/saving (s) 0.0120984 +time/training (s) 20.0793 +time/epoch (s) 32.7673 +time/total (s) 9445.06 +Epoch -551 +------------------------------ ---------------- +2022-05-15 20:40:24.583448 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -550 finished +------------------------------ ---------------- +epoch -550 +replay_buffer/size 999047 +trainer/num train calls 451000 +trainer/QF1 Loss 0.999009 +trainer/QF2 Loss 0.976098 +trainer/Policy Loss 28.9923 +trainer/Q1 Predictions Mean -74.3436 +trainer/Q1 Predictions Std 16.3426 +trainer/Q1 Predictions Max -0.152585 +trainer/Q1 Predictions Min -87.1023 +trainer/Q2 Predictions Mean -74.1754 +trainer/Q2 Predictions Std 16.2634 +trainer/Q2 Predictions Max -0.348629 +trainer/Q2 Predictions Min -87.2066 +trainer/Q Targets Mean -74.4029 +trainer/Q Targets Std 16.5446 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1098 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0166543 +trainer/policy/mean Std 0.722413 +trainer/policy/mean Max 0.999637 +trainer/policy/mean Min -0.999197 +trainer/policy/std Mean 0.42308 +trainer/policy/std Std 0.0208623 +trainer/policy/std Max 0.446164 +trainer/policy/std Min 0.388438 +trainer/Advantage Weights Mean 6.77999 +trainer/Advantage Weights Std 19.8333 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.98246e-22 +trainer/Advantage Score Mean -0.374972 +trainer/Advantage Score Std 0.758342 +trainer/Advantage Score Max 1.08515 +trainer/Advantage Score Min -4.99725 +trainer/V1 Predictions Mean -74.0962 +trainer/V1 Predictions Std 16.7298 +trainer/V1 Predictions Max 2.47738 +trainer/V1 Predictions Min -86.9778 +trainer/VF Loss 0.0958013 +expl/num steps total 451000 +expl/num paths total 546 +expl/path length Mean 500 +expl/path length Std 98 +expl/path length Max 598 +expl/path length Min 402 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.023198 +expl/Actions Std 0.821878 +expl/Actions Max 2.22947 +expl/Actions Min -2.37522 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 431849 +eval/num paths total 452 +eval/path length Mean 783 +eval/path length Std 0 +eval/path length Max 783 +eval/path length Min 783 +eval/Rewards Mean 0.00127714 +eval/Rewards Std 0.0357143 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0251307 +eval/Actions Std 0.733422 +eval/Actions Max 0.999402 +eval/Actions Min -0.999499 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.13891e-05 +time/evaluation sampling (s) 5.28907 +time/exploration sampling (s) 7.2479 +time/logging (s) 0.0107293 +time/saving (s) 0.0292365 +time/training (s) 19.8469 +time/epoch (s) 32.4238 +time/total (s) 9477.49 +Epoch -550 +------------------------------ ---------------- +2022-05-15 20:40:55.767398 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -549 finished +------------------------------ ---------------- +epoch -549 +replay_buffer/size 999047 +trainer/num train calls 452000 +trainer/QF1 Loss 1.01915 +trainer/QF2 Loss 1.04851 +trainer/Policy Loss 16.7119 +trainer/Q1 Predictions Mean -73.3698 +trainer/Q1 Predictions Std 18.1561 +trainer/Q1 Predictions Max -3.93411 +trainer/Q1 Predictions Min -87.1735 +trainer/Q2 Predictions Mean -73.2937 +trainer/Q2 Predictions Std 18.124 +trainer/Q2 Predictions Max -3.32651 +trainer/Q2 Predictions Min -86.8459 +trainer/Q Targets Mean -73.3764 +trainer/Q Targets Std 17.8693 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9421 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000266391 +trainer/policy/mean Std 0.716739 +trainer/policy/mean Max 0.997674 +trainer/policy/mean Min -0.998461 +trainer/policy/std Mean 0.421725 +trainer/policy/std Std 0.021022 +trainer/policy/std Max 0.446549 +trainer/policy/std Min 0.38667 +trainer/Advantage Weights Mean 2.50289 +trainer/Advantage Weights Std 12.9751 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.81311e-12 +trainer/Advantage Score Mean -0.422956 +trainer/Advantage Score Std 0.47345 +trainer/Advantage Score Max 1.1776 +trainer/Advantage Score Min -2.55752 +trainer/V1 Predictions Mean -73.1393 +trainer/V1 Predictions Std 18.0326 +trainer/V1 Predictions Max -5.24942 +trainer/V1 Predictions Min -86.8567 +trainer/VF Loss 0.0510014 +expl/num steps total 452000 +expl/num paths total 547 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.149807 +expl/Actions Std 0.829419 +expl/Actions Max 2.33014 +expl/Actions Min -2.32002 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 432665 +eval/num paths total 453 +eval/path length Mean 816 +eval/path length Std 0 +eval/path length Max 816 +eval/path length Min 816 +eval/Rewards Mean 0.00122549 +eval/Rewards Std 0.0349855 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0190271 +eval/Actions Std 0.727521 +eval/Actions Max 0.999659 +eval/Actions Min -0.998681 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.81381e-06 +time/evaluation sampling (s) 4.72029 +time/exploration sampling (s) 7.03991 +time/logging (s) 0.0114066 +time/saving (s) 0.0190569 +time/training (s) 19.3794 +time/epoch (s) 31.1701 +time/total (s) 9508.66 +Epoch -549 +------------------------------ ---------------- +2022-05-15 20:41:28.355775 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -548 finished +------------------------------ ---------------- +epoch -548 +replay_buffer/size 999047 +trainer/num train calls 453000 +trainer/QF1 Loss 0.8882 +trainer/QF2 Loss 0.746993 +trainer/Policy Loss 14.9295 +trainer/Q1 Predictions Mean -74.5547 +trainer/Q1 Predictions Std 17.2619 +trainer/Q1 Predictions Max -0.153334 +trainer/Q1 Predictions Min -87.0265 +trainer/Q2 Predictions Mean -74.4384 +trainer/Q2 Predictions Std 17.2205 +trainer/Q2 Predictions Max -0.783487 +trainer/Q2 Predictions Min -86.7196 +trainer/Q Targets Mean -73.9972 +trainer/Q Targets Std 17.3626 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5668 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0121816 +trainer/policy/mean Std 0.721814 +trainer/policy/mean Max 0.999292 +trainer/policy/mean Min -0.99515 +trainer/policy/std Mean 0.420889 +trainer/policy/std Std 0.0205148 +trainer/policy/std Max 0.443924 +trainer/policy/std Min 0.383802 +trainer/Advantage Weights Mean 3.40963 +trainer/Advantage Weights Std 16.4988 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.10519e-09 +trainer/Advantage Score Mean -0.372689 +trainer/Advantage Score Std 0.476304 +trainer/Advantage Score Max 2.51024 +trainer/Advantage Score Min -2.06232 +trainer/V1 Predictions Mean -73.8489 +trainer/V1 Predictions Std 17.3871 +trainer/V1 Predictions Max 0.742236 +trainer/V1 Predictions Min -86.1126 +trainer/VF Loss 0.0706693 +expl/num steps total 453000 +expl/num paths total 548 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0197234 +expl/Actions Std 0.831415 +expl/Actions Max 2.2433 +expl/Actions Min -2.33046 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 433665 +eval/num paths total 454 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0202129 +eval/Actions Std 0.725929 +eval/Actions Max 0.999565 +eval/Actions Min -0.999213 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27731e-05 +time/evaluation sampling (s) 4.99683 +time/exploration sampling (s) 7.7835 +time/logging (s) 0.0121881 +time/saving (s) 0.0173427 +time/training (s) 19.7634 +time/epoch (s) 32.5732 +time/total (s) 9541.24 +Epoch -548 +------------------------------ ---------------- +2022-05-15 20:42:00.375093 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -547 finished +------------------------------ ---------------- +epoch -547 +replay_buffer/size 999047 +trainer/num train calls 454000 +trainer/QF1 Loss 0.859942 +trainer/QF2 Loss 0.829873 +trainer/Policy Loss 4.4067 +trainer/Q1 Predictions Mean -74.4028 +trainer/Q1 Predictions Std 16.545 +trainer/Q1 Predictions Max -1.08778 +trainer/Q1 Predictions Min -86.9439 +trainer/Q2 Predictions Mean -74.3747 +trainer/Q2 Predictions Std 16.6176 +trainer/Q2 Predictions Max 0.109305 +trainer/Q2 Predictions Min -87.044 +trainer/Q Targets Mean -74.1891 +trainer/Q Targets Std 16.5172 +trainer/Q Targets Max 1.14186 +trainer/Q Targets Min -86.9296 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0141424 +trainer/policy/mean Std 0.72585 +trainer/policy/mean Max 0.999864 +trainer/policy/mean Min -0.99851 +trainer/policy/std Mean 0.419667 +trainer/policy/std Std 0.0214687 +trainer/policy/std Max 0.444112 +trainer/policy/std Min 0.383459 +trainer/Advantage Weights Mean 1.70026 +trainer/Advantage Weights Std 10.3334 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.45591e-20 +trainer/Advantage Score Mean -0.497314 +trainer/Advantage Score Std 0.570636 +trainer/Advantage Score Max 0.825707 +trainer/Advantage Score Min -4.41867 +trainer/V1 Predictions Mean -73.944 +trainer/V1 Predictions Std 16.6291 +trainer/V1 Predictions Max 1.8624 +trainer/V1 Predictions Min -86.7913 +trainer/VF Loss 0.0627636 +expl/num steps total 454000 +expl/num paths total 549 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0372774 +expl/Actions Std 0.84437 +expl/Actions Max 2.5741 +expl/Actions Min -2.22087 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 434461 +eval/num paths total 455 +eval/path length Mean 796 +eval/path length Std 0 +eval/path length Max 796 +eval/path length Min 796 +eval/Rewards Mean 0.00125628 +eval/Rewards Std 0.0354218 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0444318 +eval/Actions Std 0.749445 +eval/Actions Max 0.999273 +eval/Actions Min -0.999645 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.84408e-06 +time/evaluation sampling (s) 4.96831 +time/exploration sampling (s) 7.36373 +time/logging (s) 0.0105061 +time/saving (s) 0.0167541 +time/training (s) 19.6449 +time/epoch (s) 32.0042 +time/total (s) 9573.25 +Epoch -547 +------------------------------ ---------------- +2022-05-15 20:42:32.780838 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -546 finished +------------------------------ ---------------- +epoch -546 +replay_buffer/size 999047 +trainer/num train calls 455000 +trainer/QF1 Loss 0.667638 +trainer/QF2 Loss 0.716536 +trainer/Policy Loss 8.66151 +trainer/Q1 Predictions Mean -74.5331 +trainer/Q1 Predictions Std 16.2792 +trainer/Q1 Predictions Max -0.350691 +trainer/Q1 Predictions Min -86.5183 +trainer/Q2 Predictions Mean -74.6214 +trainer/Q2 Predictions Std 16.259 +trainer/Q2 Predictions Max -0.183479 +trainer/Q2 Predictions Min -86.5389 +trainer/Q Targets Mean -74.6993 +trainer/Q Targets Std 16.479 +trainer/Q Targets Max 0.743115 +trainer/Q Targets Min -86.7375 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0037216 +trainer/policy/mean Std 0.717665 +trainer/policy/mean Max 0.9979 +trainer/policy/mean Min -0.999626 +trainer/policy/std Mean 0.42046 +trainer/policy/std Std 0.0203165 +trainer/policy/std Max 0.441351 +trainer/policy/std Min 0.38479 +trainer/Advantage Weights Mean 2.0351 +trainer/Advantage Weights Std 12.4873 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.67195e-29 +trainer/Advantage Score Mean -0.552309 +trainer/Advantage Score Std 0.747157 +trainer/Advantage Score Max 1.51978 +trainer/Advantage Score Min -6.47374 +trainer/V1 Predictions Mean -74.446 +trainer/V1 Predictions Std 16.582 +trainer/V1 Predictions Max 1.1197 +trainer/V1 Predictions Min -86.5637 +trainer/VF Loss 0.0984856 +expl/num steps total 455000 +expl/num paths total 551 +expl/path length Mean 500 +expl/path length Std 420 +expl/path length Max 920 +expl/path length Min 80 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0287877 +expl/Actions Std 0.825612 +expl/Actions Max 2.30025 +expl/Actions Min -2.46379 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 435146 +eval/num paths total 456 +eval/path length Mean 685 +eval/path length Std 0 +eval/path length Max 685 +eval/path length Min 685 +eval/Rewards Mean 0.00145985 +eval/Rewards Std 0.0381801 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00313546 +eval/Actions Std 0.728839 +eval/Actions Max 0.998828 +eval/Actions Min -0.999631 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2923e-05 +time/evaluation sampling (s) 5.1295 +time/exploration sampling (s) 7.3241 +time/logging (s) 0.0124407 +time/saving (s) 0.0248928 +time/training (s) 19.9032 +time/epoch (s) 32.3941 +time/total (s) 9605.65 +Epoch -546 +------------------------------ ---------------- +2022-05-15 20:43:05.486850 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -545 finished +------------------------------ ---------------- +epoch -545 +replay_buffer/size 999047 +trainer/num train calls 456000 +trainer/QF1 Loss 0.572538 +trainer/QF2 Loss 0.53936 +trainer/Policy Loss 22.5927 +trainer/Q1 Predictions Mean -72.5329 +trainer/Q1 Predictions Std 19.1944 +trainer/Q1 Predictions Max 0.298048 +trainer/Q1 Predictions Min -86.7835 +trainer/Q2 Predictions Mean -72.4482 +trainer/Q2 Predictions Std 19.1902 +trainer/Q2 Predictions Max -0.32253 +trainer/Q2 Predictions Min -86.5466 +trainer/Q Targets Mean -72.6253 +trainer/Q Targets Std 19.3134 +trainer/Q Targets Max 0.12676 +trainer/Q Targets Min -86.7673 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0347564 +trainer/policy/mean Std 0.711324 +trainer/policy/mean Max 0.999746 +trainer/policy/mean Min -0.998482 +trainer/policy/std Mean 0.420883 +trainer/policy/std Std 0.0205011 +trainer/policy/std Max 0.442423 +trainer/policy/std Min 0.385596 +trainer/Advantage Weights Mean 6.73015 +trainer/Advantage Weights Std 19.2372 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.43401e-13 +trainer/Advantage Score Mean -0.277436 +trainer/Advantage Score Std 0.657116 +trainer/Advantage Score Max 0.80896 +trainer/Advantage Score Min -2.76893 +trainer/V1 Predictions Mean -72.3016 +trainer/V1 Predictions Std 19.4992 +trainer/V1 Predictions Max 1.69733 +trainer/V1 Predictions Min -86.8123 +trainer/VF Loss 0.0717777 +expl/num steps total 456000 +expl/num paths total 552 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0220019 +expl/Actions Std 0.86051 +expl/Actions Max 2.4723 +expl/Actions Min -2.43613 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 435820 +eval/num paths total 457 +eval/path length Mean 674 +eval/path length Std 0 +eval/path length Max 674 +eval/path length Min 674 +eval/Rewards Mean 0.00148368 +eval/Rewards Std 0.03849 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0221825 +eval/Actions Std 0.7153 +eval/Actions Max 0.999785 +eval/Actions Min -0.999799 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0971e-05 +time/evaluation sampling (s) 5.68474 +time/exploration sampling (s) 6.9785 +time/logging (s) 0.00898121 +time/saving (s) 0.0149833 +time/training (s) 19.9948 +time/epoch (s) 32.682 +time/total (s) 9638.35 +Epoch -545 +------------------------------ ---------------- +2022-05-15 20:43:37.551446 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -544 finished +------------------------------ ---------------- +epoch -544 +replay_buffer/size 999047 +trainer/num train calls 457000 +trainer/QF1 Loss 0.914422 +trainer/QF2 Loss 1.09636 +trainer/Policy Loss 27.1641 +trainer/Q1 Predictions Mean -72.7312 +trainer/Q1 Predictions Std 18.6155 +trainer/Q1 Predictions Max -0.96482 +trainer/Q1 Predictions Min -86.8763 +trainer/Q2 Predictions Mean -72.5783 +trainer/Q2 Predictions Std 18.62 +trainer/Q2 Predictions Max -0.947041 +trainer/Q2 Predictions Min -87.0243 +trainer/Q Targets Mean -72.8463 +trainer/Q Targets Std 18.4743 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8594 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00975939 +trainer/policy/mean Std 0.723862 +trainer/policy/mean Max 0.999243 +trainer/policy/mean Min -0.999228 +trainer/policy/std Mean 0.420768 +trainer/policy/std Std 0.019926 +trainer/policy/std Max 0.440396 +trainer/policy/std Min 0.383361 +trainer/Advantage Weights Mean 4.90819 +trainer/Advantage Weights Std 18.6096 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.80008e-15 +trainer/Advantage Score Mean -0.414395 +trainer/Advantage Score Std 0.55596 +trainer/Advantage Score Max 1.5869 +trainer/Advantage Score Min -3.26218 +trainer/V1 Predictions Mean -72.6449 +trainer/V1 Predictions Std 18.4307 +trainer/V1 Predictions Max 0.293578 +trainer/V1 Predictions Min -86.4937 +trainer/VF Loss 0.0738748 +expl/num steps total 457000 +expl/num paths total 554 +expl/path length Mean 500 +expl/path length Std 263 +expl/path length Max 763 +expl/path length Min 237 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0115844 +expl/Actions Std 0.817404 +expl/Actions Max 2.14516 +expl/Actions Min -2.38655 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 436486 +eval/num paths total 458 +eval/path length Mean 666 +eval/path length Std 0 +eval/path length Max 666 +eval/path length Min 666 +eval/Rewards Mean 0.0015015 +eval/Rewards Std 0.0387201 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0248255 +eval/Actions Std 0.725947 +eval/Actions Max 0.999851 +eval/Actions Min -0.999706 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.862e-06 +time/evaluation sampling (s) 4.87666 +time/exploration sampling (s) 7.09058 +time/logging (s) 0.0101212 +time/saving (s) 0.019813 +time/training (s) 20.0582 +time/epoch (s) 32.0554 +time/total (s) 9670.41 +Epoch -544 +------------------------------ ---------------- +2022-05-15 20:44:09.521795 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -543 finished +------------------------------ ---------------- +epoch -543 +replay_buffer/size 999047 +trainer/num train calls 458000 +trainer/QF1 Loss 1.20982 +trainer/QF2 Loss 1.05664 +trainer/Policy Loss 12.8139 +trainer/Q1 Predictions Mean -72.7974 +trainer/Q1 Predictions Std 18.7049 +trainer/Q1 Predictions Max -0.732957 +trainer/Q1 Predictions Min -86.2942 +trainer/Q2 Predictions Mean -72.723 +trainer/Q2 Predictions Std 18.6923 +trainer/Q2 Predictions Max -1.20956 +trainer/Q2 Predictions Min -86.1897 +trainer/Q Targets Mean -72.4577 +trainer/Q Targets Std 18.9919 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9394 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0353295 +trainer/policy/mean Std 0.711073 +trainer/policy/mean Max 0.999396 +trainer/policy/mean Min -0.999133 +trainer/policy/std Mean 0.422448 +trainer/policy/std Std 0.0199327 +trainer/policy/std Max 0.443708 +trainer/policy/std Min 0.387197 +trainer/Advantage Weights Mean 1.47445 +trainer/Advantage Weights Std 9.56724 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.21153e-15 +trainer/Advantage Score Mean -0.513973 +trainer/Advantage Score Std 0.537562 +trainer/Advantage Score Max 1.04065 +trainer/Advantage Score Min -3.43469 +trainer/V1 Predictions Mean -72.1899 +trainer/V1 Predictions Std 19.0573 +trainer/V1 Predictions Max -0.784075 +trainer/V1 Predictions Min -85.8735 +trainer/VF Loss 0.0615558 +expl/num steps total 458000 +expl/num paths total 555 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0714662 +expl/Actions Std 0.805918 +expl/Actions Max 2.41368 +expl/Actions Min -2.22818 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 437486 +eval/num paths total 459 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0259488 +eval/Actions Std 0.828627 +eval/Actions Max 0.999525 +eval/Actions Min -0.999125 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.85921e-06 +time/evaluation sampling (s) 5.20904 +time/exploration sampling (s) 7.0065 +time/logging (s) 0.0122238 +time/saving (s) 0.0175975 +time/training (s) 19.7135 +time/epoch (s) 31.9589 +time/total (s) 9702.37 +Epoch -543 +------------------------------ ---------------- +2022-05-15 20:44:41.106015 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -542 finished +------------------------------ ---------------- +epoch -542 +replay_buffer/size 999047 +trainer/num train calls 459000 +trainer/QF1 Loss 1.94849 +trainer/QF2 Loss 2.23028 +trainer/Policy Loss 12.5988 +trainer/Q1 Predictions Mean -72.6261 +trainer/Q1 Predictions Std 19.5926 +trainer/Q1 Predictions Max -1.56256 +trainer/Q1 Predictions Min -86.2775 +trainer/Q2 Predictions Mean -72.7225 +trainer/Q2 Predictions Std 19.6398 +trainer/Q2 Predictions Max -2.35175 +trainer/Q2 Predictions Min -86.6586 +trainer/Q Targets Mean -72.6042 +trainer/Q Targets Std 19.2277 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.156 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.030545 +trainer/policy/mean Std 0.724994 +trainer/policy/mean Max 0.998496 +trainer/policy/mean Min -0.996295 +trainer/policy/std Mean 0.423085 +trainer/policy/std Std 0.0205509 +trainer/policy/std Max 0.446028 +trainer/policy/std Min 0.387419 +trainer/Advantage Weights Mean 3.85658 +trainer/Advantage Weights Std 17.733 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.77162e-12 +trainer/Advantage Score Mean -0.412152 +trainer/Advantage Score Std 0.51833 +trainer/Advantage Score Max 1.56102 +trainer/Advantage Score Min -2.54595 +trainer/V1 Predictions Mean -72.234 +trainer/V1 Predictions Std 19.5479 +trainer/V1 Predictions Max -3.77339 +trainer/V1 Predictions Min -86.0805 +trainer/VF Loss 0.0709006 +expl/num steps total 459000 +expl/num paths total 556 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0279769 +expl/Actions Std 0.825218 +expl/Actions Max 2.33875 +expl/Actions Min -2.45962 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 438379 +eval/num paths total 460 +eval/path length Mean 893 +eval/path length Std 0 +eval/path length Max 893 +eval/path length Min 893 +eval/Rewards Mean 0.00111982 +eval/Rewards Std 0.033445 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00887402 +eval/Actions Std 0.717341 +eval/Actions Max 0.999335 +eval/Actions Min -0.999815 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0733e-05 +time/evaluation sampling (s) 5.21233 +time/exploration sampling (s) 6.69953 +time/logging (s) 0.0119023 +time/saving (s) 0.0143219 +time/training (s) 19.6302 +time/epoch (s) 31.5683 +time/total (s) 9733.95 +Epoch -542 +------------------------------ ---------------- +2022-05-15 20:45:12.884857 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -541 finished +------------------------------ ---------------- +epoch -541 +replay_buffer/size 999047 +trainer/num train calls 460000 +trainer/QF1 Loss 0.559665 +trainer/QF2 Loss 0.508687 +trainer/Policy Loss 19.4054 +trainer/Q1 Predictions Mean -74.8447 +trainer/Q1 Predictions Std 16.8656 +trainer/Q1 Predictions Max -6.07261 +trainer/Q1 Predictions Min -87.7512 +trainer/Q2 Predictions Mean -74.8506 +trainer/Q2 Predictions Std 16.972 +trainer/Q2 Predictions Max -4.83928 +trainer/Q2 Predictions Min -87.5988 +trainer/Q Targets Mean -74.8492 +trainer/Q Targets Std 17.1408 +trainer/Q Targets Max -2.1954 +trainer/Q Targets Min -86.8375 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0357646 +trainer/policy/mean Std 0.718811 +trainer/policy/mean Max 0.998277 +trainer/policy/mean Min -0.999432 +trainer/policy/std Mean 0.423095 +trainer/policy/std Std 0.0199736 +trainer/policy/std Max 0.446144 +trainer/policy/std Min 0.385758 +trainer/Advantage Weights Mean 4.29587 +trainer/Advantage Weights Std 15.8141 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.34676e-15 +trainer/Advantage Score Mean -0.3055 +trainer/Advantage Score Std 0.572369 +trainer/Advantage Score Max 2.3028 +trainer/Advantage Score Min -3.36857 +trainer/V1 Predictions Mean -74.5979 +trainer/V1 Predictions Std 17.2954 +trainer/V1 Predictions Max -1.45957 +trainer/V1 Predictions Min -87.0203 +trainer/VF Loss 0.0748471 +expl/num steps total 460000 +expl/num paths total 557 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0391809 +expl/Actions Std 0.833462 +expl/Actions Max 2.38186 +expl/Actions Min -2.43406 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 439027 +eval/num paths total 461 +eval/path length Mean 648 +eval/path length Std 0 +eval/path length Max 648 +eval/path length Min 648 +eval/Rewards Mean 0.00154321 +eval/Rewards Std 0.0392534 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0339064 +eval/Actions Std 0.732739 +eval/Actions Max 0.999241 +eval/Actions Min -0.999453 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.20727e-05 +time/evaluation sampling (s) 4.91934 +time/exploration sampling (s) 6.94691 +time/logging (s) 0.0105298 +time/saving (s) 0.0182799 +time/training (s) 19.8689 +time/epoch (s) 31.764 +time/total (s) 9765.72 +Epoch -541 +------------------------------ ---------------- +2022-05-15 20:45:45.255956 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -540 finished +------------------------------ ---------------- +epoch -540 +replay_buffer/size 999047 +trainer/num train calls 461000 +trainer/QF1 Loss 1.15412 +trainer/QF2 Loss 1.17243 +trainer/Policy Loss 49.8381 +trainer/Q1 Predictions Mean -73.4869 +trainer/Q1 Predictions Std 18.3064 +trainer/Q1 Predictions Max -0.844587 +trainer/Q1 Predictions Min -88.766 +trainer/Q2 Predictions Mean -73.5203 +trainer/Q2 Predictions Std 18.2991 +trainer/Q2 Predictions Max -1.5547 +trainer/Q2 Predictions Min -88.6779 +trainer/Q Targets Mean -73.7036 +trainer/Q Targets Std 18.6177 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4017 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0210028 +trainer/policy/mean Std 0.72895 +trainer/policy/mean Max 0.999372 +trainer/policy/mean Min -0.998927 +trainer/policy/std Mean 0.419917 +trainer/policy/std Std 0.021543 +trainer/policy/std Max 0.444329 +trainer/policy/std Min 0.381888 +trainer/Advantage Weights Mean 9.94456 +trainer/Advantage Weights Std 24.6292 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.27799e-17 +trainer/Advantage Score Mean -0.118033 +trainer/Advantage Score Std 0.52726 +trainer/Advantage Score Max 1.26827 +trainer/Advantage Score Min -3.88987 +trainer/V1 Predictions Mean -73.5914 +trainer/V1 Predictions Std 18.3717 +trainer/V1 Predictions Max -1.04319 +trainer/V1 Predictions Min -88.779 +trainer/VF Loss 0.0770177 +expl/num steps total 461000 +expl/num paths total 558 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0601222 +expl/Actions Std 0.833481 +expl/Actions Max 2.31177 +expl/Actions Min -2.25843 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 440027 +eval/num paths total 462 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0943495 +eval/Actions Std 0.753351 +eval/Actions Max 0.999564 +eval/Actions Min -0.999206 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.47616e-06 +time/evaluation sampling (s) 5.21298 +time/exploration sampling (s) 7.13641 +time/logging (s) 0.0114806 +time/saving (s) 0.0156343 +time/training (s) 19.9809 +time/epoch (s) 32.3574 +time/total (s) 9798.08 +Epoch -540 +------------------------------ ---------------- +2022-05-15 20:46:16.200373 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -539 finished +------------------------------ ---------------- +epoch -539 +replay_buffer/size 999047 +trainer/num train calls 462000 +trainer/QF1 Loss 0.623498 +trainer/QF2 Loss 0.597172 +trainer/Policy Loss 8.21021 +trainer/Q1 Predictions Mean -72.295 +trainer/Q1 Predictions Std 19.3469 +trainer/Q1 Predictions Max -1.13362 +trainer/Q1 Predictions Min -86.6958 +trainer/Q2 Predictions Mean -72.427 +trainer/Q2 Predictions Std 19.3543 +trainer/Q2 Predictions Max -1.8812 +trainer/Q2 Predictions Min -86.8707 +trainer/Q Targets Mean -72.2281 +trainer/Q Targets Std 19.3727 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8853 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.006602 +trainer/policy/mean Std 0.724216 +trainer/policy/mean Max 0.998867 +trainer/policy/mean Min -0.998224 +trainer/policy/std Mean 0.420775 +trainer/policy/std Std 0.0190625 +trainer/policy/std Max 0.440711 +trainer/policy/std Min 0.388976 +trainer/Advantage Weights Mean 2.18154 +trainer/Advantage Weights Std 12.5717 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.66679e-15 +trainer/Advantage Score Mean -0.491811 +trainer/Advantage Score Std 0.618147 +trainer/Advantage Score Max 1.88911 +trainer/Advantage Score Min -3.22701 +trainer/V1 Predictions Mean -71.9844 +trainer/V1 Predictions Std 19.3929 +trainer/V1 Predictions Max -1.97687 +trainer/V1 Predictions Min -86.5411 +trainer/VF Loss 0.0829696 +expl/num steps total 462000 +expl/num paths total 560 +expl/path length Mean 500 +expl/path length Std 271 +expl/path length Max 771 +expl/path length Min 229 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0288998 +expl/Actions Std 0.819291 +expl/Actions Max 2.30227 +expl/Actions Min -2.35789 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 441027 +eval/num paths total 463 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00796445 +eval/Actions Std 0.735325 +eval/Actions Max 0.999574 +eval/Actions Min -0.999769 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.49226e-06 +time/evaluation sampling (s) 4.7025 +time/exploration sampling (s) 6.88413 +time/logging (s) 0.0118778 +time/saving (s) 0.0161878 +time/training (s) 19.3169 +time/epoch (s) 30.9317 +time/total (s) 9829.02 +Epoch -539 +------------------------------ ---------------- +2022-05-15 20:46:48.084491 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -538 finished +------------------------------ ---------------- +epoch -538 +replay_buffer/size 999047 +trainer/num train calls 463000 +trainer/QF1 Loss 0.974145 +trainer/QF2 Loss 0.904897 +trainer/Policy Loss 27.3179 +trainer/Q1 Predictions Mean -73.519 +trainer/Q1 Predictions Std 18.9423 +trainer/Q1 Predictions Max -0.965096 +trainer/Q1 Predictions Min -87.0278 +trainer/Q2 Predictions Mean -73.4462 +trainer/Q2 Predictions Std 18.986 +trainer/Q2 Predictions Max -1.02288 +trainer/Q2 Predictions Min -86.8334 +trainer/Q Targets Mean -73.5278 +trainer/Q Targets Std 18.9551 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0487 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0132377 +trainer/policy/mean Std 0.728822 +trainer/policy/mean Max 0.998337 +trainer/policy/mean Min -0.99727 +trainer/policy/std Mean 0.420397 +trainer/policy/std Std 0.0195181 +trainer/policy/std Max 0.442082 +trainer/policy/std Min 0.389296 +trainer/Advantage Weights Mean 5.82457 +trainer/Advantage Weights Std 19.674 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.41292e-18 +trainer/Advantage Score Mean -0.272326 +trainer/Advantage Score Std 0.565232 +trainer/Advantage Score Max 0.99321 +trainer/Advantage Score Min -3.97577 +trainer/V1 Predictions Mean -73.2055 +trainer/V1 Predictions Std 19.0534 +trainer/V1 Predictions Max -0.0856537 +trainer/V1 Predictions Min -87.0549 +trainer/VF Loss 0.0585576 +expl/num steps total 463000 +expl/num paths total 562 +expl/path length Mean 500 +expl/path length Std 463 +expl/path length Max 963 +expl/path length Min 37 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0031783 +expl/Actions Std 0.814911 +expl/Actions Max 2.34149 +expl/Actions Min -2.32815 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 442027 +eval/num paths total 464 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0123212 +eval/Actions Std 0.702527 +eval/Actions Max 0.999586 +eval/Actions Min -0.999792 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.28848e-05 +time/evaluation sampling (s) 4.78687 +time/exploration sampling (s) 7.23066 +time/logging (s) 0.0111082 +time/saving (s) 0.0152875 +time/training (s) 19.8233 +time/epoch (s) 31.8673 +time/total (s) 9860.9 +Epoch -538 +------------------------------ ---------------- +2022-05-15 20:47:19.568631 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -537 finished +------------------------------ ---------------- +epoch -537 +replay_buffer/size 999047 +trainer/num train calls 464000 +trainer/QF1 Loss 0.898436 +trainer/QF2 Loss 0.816754 +trainer/Policy Loss 17.7242 +trainer/Q1 Predictions Mean -75.2873 +trainer/Q1 Predictions Std 16.6812 +trainer/Q1 Predictions Max -0.916649 +trainer/Q1 Predictions Min -87.1943 +trainer/Q2 Predictions Mean -75.2042 +trainer/Q2 Predictions Std 16.6512 +trainer/Q2 Predictions Max -1.2359 +trainer/Q2 Predictions Min -87.0356 +trainer/Q Targets Mean -75.0515 +trainer/Q Targets Std 16.6868 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0354 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0186044 +trainer/policy/mean Std 0.711104 +trainer/policy/mean Max 0.999777 +trainer/policy/mean Min -0.999706 +trainer/policy/std Mean 0.421662 +trainer/policy/std Std 0.0190936 +trainer/policy/std Max 0.442786 +trainer/policy/std Min 0.391273 +trainer/Advantage Weights Mean 5.07688 +trainer/Advantage Weights Std 19.5186 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.88447e-12 +trainer/Advantage Score Mean -0.281695 +trainer/Advantage Score Std 0.463442 +trainer/Advantage Score Max 1.40386 +trainer/Advantage Score Min -2.6045 +trainer/V1 Predictions Mean -74.856 +trainer/V1 Predictions Std 16.6488 +trainer/V1 Predictions Max -1.89482 +trainer/V1 Predictions Min -86.6343 +trainer/VF Loss 0.0551902 +expl/num steps total 464000 +expl/num paths total 563 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0565906 +expl/Actions Std 0.79954 +expl/Actions Max 2.35737 +expl/Actions Min -2.52645 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 443027 +eval/num paths total 465 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.141136 +eval/Actions Std 0.696551 +eval/Actions Max 0.999664 +eval/Actions Min -0.999367 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27316e-05 +time/evaluation sampling (s) 4.71366 +time/exploration sampling (s) 7.68195 +time/logging (s) 0.0106773 +time/saving (s) 0.0159646 +time/training (s) 19.0484 +time/epoch (s) 31.4707 +time/total (s) 9892.37 +Epoch -537 +------------------------------ ---------------- +2022-05-15 20:47:52.208207 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -536 finished +------------------------------ ---------------- +epoch -536 +replay_buffer/size 999047 +trainer/num train calls 465000 +trainer/QF1 Loss 1.03818 +trainer/QF2 Loss 1.20005 +trainer/Policy Loss 14.5356 +trainer/Q1 Predictions Mean -73.486 +trainer/Q1 Predictions Std 18.4069 +trainer/Q1 Predictions Max -1.97686 +trainer/Q1 Predictions Min -88.2317 +trainer/Q2 Predictions Mean -73.4791 +trainer/Q2 Predictions Std 18.3196 +trainer/Q2 Predictions Max -1.64859 +trainer/Q2 Predictions Min -87.8925 +trainer/Q Targets Mean -73.2109 +trainer/Q Targets Std 18.78 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.8175 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0179753 +trainer/policy/mean Std 0.725039 +trainer/policy/mean Max 0.999245 +trainer/policy/mean Min -0.998466 +trainer/policy/std Mean 0.419648 +trainer/policy/std Std 0.0203053 +trainer/policy/std Max 0.444394 +trainer/policy/std Min 0.388084 +trainer/Advantage Weights Mean 3.38975 +trainer/Advantage Weights Std 14.0504 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.7106e-17 +trainer/Advantage Score Mean -0.337682 +trainer/Advantage Score Std 0.658861 +trainer/Advantage Score Max 1.2366 +trainer/Advantage Score Min -3.86071 +trainer/V1 Predictions Mean -72.9918 +trainer/V1 Predictions Std 18.7505 +trainer/V1 Predictions Max -0.508829 +trainer/V1 Predictions Min -87.5953 +trainer/VF Loss 0.0688 +expl/num steps total 465000 +expl/num paths total 565 +expl/path length Mean 500 +expl/path length Std 310 +expl/path length Max 810 +expl/path length Min 190 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0245067 +expl/Actions Std 0.823997 +expl/Actions Max 2.36771 +expl/Actions Min -2.1893 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 443631 +eval/num paths total 466 +eval/path length Mean 604 +eval/path length Std 0 +eval/path length Max 604 +eval/path length Min 604 +eval/Rewards Mean 0.00165563 +eval/Rewards Std 0.0406557 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0211537 +eval/Actions Std 0.726087 +eval/Actions Max 0.999007 +eval/Actions Min -0.998782 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.11801e-05 +time/evaluation sampling (s) 4.82529 +time/exploration sampling (s) 7.41183 +time/logging (s) 0.0102964 +time/saving (s) 0.0188909 +time/training (s) 20.3594 +time/epoch (s) 32.6257 +time/total (s) 9925 +Epoch -536 +------------------------------ ---------------- +2022-05-15 20:48:24.502844 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -535 finished +------------------------------ ---------------- +epoch -535 +replay_buffer/size 999047 +trainer/num train calls 466000 +trainer/QF1 Loss 6.75161 +trainer/QF2 Loss 6.80872 +trainer/Policy Loss 16.9442 +trainer/Q1 Predictions Mean -74.1535 +trainer/Q1 Predictions Std 17.1081 +trainer/Q1 Predictions Max 0.600194 +trainer/Q1 Predictions Min -86.6593 +trainer/Q2 Predictions Mean -74.1272 +trainer/Q2 Predictions Std 17.1545 +trainer/Q2 Predictions Max -0.359252 +trainer/Q2 Predictions Min -86.5024 +trainer/Q Targets Mean -74.1641 +trainer/Q Targets Std 16.9254 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1207 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00259634 +trainer/policy/mean Std 0.719679 +trainer/policy/mean Max 0.999943 +trainer/policy/mean Min -0.998971 +trainer/policy/std Mean 0.420845 +trainer/policy/std Std 0.0204 +trainer/policy/std Max 0.443197 +trainer/policy/std Min 0.39094 +trainer/Advantage Weights Mean 2.9926 +trainer/Advantage Weights Std 14.2501 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.30427e-20 +trainer/Advantage Score Mean -0.412074 +trainer/Advantage Score Std 0.510426 +trainer/Advantage Score Max 0.934364 +trainer/Advantage Score Min -4.57861 +trainer/V1 Predictions Mean -73.8465 +trainer/V1 Predictions Std 17.1368 +trainer/V1 Predictions Max 0.268217 +trainer/V1 Predictions Min -86.6475 +trainer/VF Loss 0.0533937 +expl/num steps total 466000 +expl/num paths total 567 +expl/path length Mean 500 +expl/path length Std 114 +expl/path length Max 614 +expl/path length Min 386 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.036608 +expl/Actions Std 0.841634 +expl/Actions Max 2.24562 +expl/Actions Min -2.52078 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 444305 +eval/num paths total 467 +eval/path length Mean 674 +eval/path length Std 0 +eval/path length Max 674 +eval/path length Min 674 +eval/Rewards Mean 0.00148368 +eval/Rewards Std 0.03849 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0200539 +eval/Actions Std 0.726127 +eval/Actions Max 0.999483 +eval/Actions Min -0.999232 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.03689e-05 +time/evaluation sampling (s) 5.41537 +time/exploration sampling (s) 7.43408 +time/logging (s) 0.00810583 +time/saving (s) 0.0146828 +time/training (s) 19.4069 +time/epoch (s) 32.2792 +time/total (s) 9957.29 +Epoch -535 +------------------------------ ---------------- +2022-05-15 20:48:56.715857 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -534 finished +------------------------------ ---------------- +epoch -534 +replay_buffer/size 999047 +trainer/num train calls 467000 +trainer/QF1 Loss 15.7194 +trainer/QF2 Loss 15.3986 +trainer/Policy Loss 44.6046 +trainer/Q1 Predictions Mean -73.4693 +trainer/Q1 Predictions Std 18.7944 +trainer/Q1 Predictions Max 0.512379 +trainer/Q1 Predictions Min -87.2635 +trainer/Q2 Predictions Mean -73.4014 +trainer/Q2 Predictions Std 18.7994 +trainer/Q2 Predictions Max 0.187607 +trainer/Q2 Predictions Min -87.4039 +trainer/Q Targets Mean -73.4637 +trainer/Q Targets Std 18.9183 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2006 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00336662 +trainer/policy/mean Std 0.72736 +trainer/policy/mean Max 0.99855 +trainer/policy/mean Min -0.997845 +trainer/policy/std Mean 0.420812 +trainer/policy/std Std 0.021951 +trainer/policy/std Max 0.44574 +trainer/policy/std Min 0.388096 +trainer/Advantage Weights Mean 8.97643 +trainer/Advantage Weights Std 25.2853 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.01738e-14 +trainer/Advantage Score Mean -0.17849 +trainer/Advantage Score Std 0.507568 +trainer/Advantage Score Max 1.83373 +trainer/Advantage Score Min -3.06233 +trainer/V1 Predictions Mean -73.4681 +trainer/V1 Predictions Std 18.721 +trainer/V1 Predictions Max 0.341454 +trainer/V1 Predictions Min -87.1751 +trainer/VF Loss 0.0736694 +expl/num steps total 467000 +expl/num paths total 568 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0331953 +expl/Actions Std 0.818836 +expl/Actions Max 2.05591 +expl/Actions Min -2.18684 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 445305 +eval/num paths total 468 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0225111 +eval/Actions Std 0.755509 +eval/Actions Max 0.999787 +eval/Actions Min -0.999383 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.22939e-05 +time/evaluation sampling (s) 4.9507 +time/exploration sampling (s) 7.12548 +time/logging (s) 0.00944127 +time/saving (s) 0.0138184 +time/training (s) 20.1042 +time/epoch (s) 32.2037 +time/total (s) 9989.5 +Epoch -534 +------------------------------ ---------------- +2022-05-15 20:49:29.260735 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -533 finished +------------------------------ ---------------- +epoch -533 +replay_buffer/size 999047 +trainer/num train calls 468000 +trainer/QF1 Loss 0.713058 +trainer/QF2 Loss 0.679474 +trainer/Policy Loss 30.7306 +trainer/Q1 Predictions Mean -73.3885 +trainer/Q1 Predictions Std 16.7787 +trainer/Q1 Predictions Max -1.12588 +trainer/Q1 Predictions Min -86.8396 +trainer/Q2 Predictions Mean -73.4456 +trainer/Q2 Predictions Std 16.8604 +trainer/Q2 Predictions Max -0.852269 +trainer/Q2 Predictions Min -86.5726 +trainer/Q Targets Mean -73.5332 +trainer/Q Targets Std 16.7055 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0737 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00970183 +trainer/policy/mean Std 0.704184 +trainer/policy/mean Max 0.996628 +trainer/policy/mean Min -0.998842 +trainer/policy/std Mean 0.41994 +trainer/policy/std Std 0.0203848 +trainer/policy/std Max 0.44048 +trainer/policy/std Min 0.388678 +trainer/Advantage Weights Mean 5.19644 +trainer/Advantage Weights Std 19.4749 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.7171e-13 +trainer/Advantage Score Mean -0.436739 +trainer/Advantage Score Std 0.557787 +trainer/Advantage Score Max 0.863472 +trainer/Advantage Score Min -2.78902 +trainer/V1 Predictions Mean -73.2741 +trainer/V1 Predictions Std 16.8596 +trainer/V1 Predictions Max -1.77182 +trainer/V1 Predictions Min -86.9761 +trainer/VF Loss 0.0658445 +expl/num steps total 468000 +expl/num paths total 569 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0235244 +expl/Actions Std 0.82399 +expl/Actions Max 2.45556 +expl/Actions Min -2.36696 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 446305 +eval/num paths total 469 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.110566 +eval/Actions Std 0.699564 +eval/Actions Max 0.9997 +eval/Actions Min -0.999424 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.04314e-06 +time/evaluation sampling (s) 5.40721 +time/exploration sampling (s) 6.47229 +time/logging (s) 0.00868752 +time/saving (s) 0.014624 +time/training (s) 20.6269 +time/epoch (s) 32.5298 +time/total (s) 10022 +Epoch -533 +------------------------------ ---------------- +2022-05-15 20:50:01.024565 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -532 finished +------------------------------ ---------------- +epoch -532 +replay_buffer/size 999047 +trainer/num train calls 469000 +trainer/QF1 Loss 0.763374 +trainer/QF2 Loss 0.827145 +trainer/Policy Loss 6.5038 +trainer/Q1 Predictions Mean -72.7045 +trainer/Q1 Predictions Std 19.1357 +trainer/Q1 Predictions Max -1.78766 +trainer/Q1 Predictions Min -86.8329 +trainer/Q2 Predictions Mean -72.9136 +trainer/Q2 Predictions Std 19.0551 +trainer/Q2 Predictions Max -2.0577 +trainer/Q2 Predictions Min -86.7269 +trainer/Q Targets Mean -72.4645 +trainer/Q Targets Std 18.9823 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6448 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000934322 +trainer/policy/mean Std 0.70826 +trainer/policy/mean Max 0.999475 +trainer/policy/mean Min -0.999132 +trainer/policy/std Mean 0.421451 +trainer/policy/std Std 0.0186857 +trainer/policy/std Max 0.439623 +trainer/policy/std Min 0.394755 +trainer/Advantage Weights Mean 1.85621 +trainer/Advantage Weights Std 11.6586 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.17911e-15 +trainer/Advantage Score Mean -0.662704 +trainer/Advantage Score Std 0.589512 +trainer/Advantage Score Max 1.33464 +trainer/Advantage Score Min -3.37599 +trainer/V1 Predictions Mean -72.2754 +trainer/V1 Predictions Std 18.9993 +trainer/V1 Predictions Max -0.641103 +trainer/V1 Predictions Min -86.5135 +trainer/VF Loss 0.0895395 +expl/num steps total 469000 +expl/num paths total 570 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0983127 +expl/Actions Std 0.822023 +expl/Actions Max 2.21993 +expl/Actions Min -2.29879 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 447305 +eval/num paths total 470 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.029872 +eval/Actions Std 0.680103 +eval/Actions Max 0.998779 +eval/Actions Min -0.996574 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.32574e-05 +time/evaluation sampling (s) 4.68918 +time/exploration sampling (s) 7.45986 +time/logging (s) 0.0126923 +time/saving (s) 0.0180931 +time/training (s) 19.5758 +time/epoch (s) 31.7556 +time/total (s) 10053.8 +Epoch -532 +------------------------------ ---------------- +2022-05-15 20:50:33.424176 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -531 finished +------------------------------ ---------------- +epoch -531 +replay_buffer/size 999047 +trainer/num train calls 470000 +trainer/QF1 Loss 1.2794 +trainer/QF2 Loss 1.17089 +trainer/Policy Loss 32.3342 +trainer/Q1 Predictions Mean -74.734 +trainer/Q1 Predictions Std 17.3064 +trainer/Q1 Predictions Max -0.272517 +trainer/Q1 Predictions Min -86.6412 +trainer/Q2 Predictions Mean -74.8629 +trainer/Q2 Predictions Std 17.3402 +trainer/Q2 Predictions Max -0.731355 +trainer/Q2 Predictions Min -86.6589 +trainer/Q Targets Mean -75.2106 +trainer/Q Targets Std 17.1252 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6257 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000346679 +trainer/policy/mean Std 0.712056 +trainer/policy/mean Max 0.999747 +trainer/policy/mean Min -0.997328 +trainer/policy/std Mean 0.41962 +trainer/policy/std Std 0.0186617 +trainer/policy/std Max 0.437641 +trainer/policy/std Min 0.391011 +trainer/Advantage Weights Mean 5.93974 +trainer/Advantage Weights Std 20.5369 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.51574e-13 +trainer/Advantage Score Mean -0.257463 +trainer/Advantage Score Std 0.496914 +trainer/Advantage Score Max 1.11132 +trainer/Advantage Score Min -2.9011 +trainer/V1 Predictions Mean -74.8694 +trainer/V1 Predictions Std 17.4014 +trainer/V1 Predictions Max -0.0255603 +trainer/V1 Predictions Min -86.4424 +trainer/VF Loss 0.0553361 +expl/num steps total 470000 +expl/num paths total 572 +expl/path length Mean 500 +expl/path length Std 334 +expl/path length Max 834 +expl/path length Min 166 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0339014 +expl/Actions Std 0.811095 +expl/Actions Max 2.14246 +expl/Actions Min -2.68403 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 448305 +eval/num paths total 471 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.148762 +eval/Actions Std 0.702306 +eval/Actions Max 0.999759 +eval/Actions Min -0.999084 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.23139e-05 +time/evaluation sampling (s) 4.9037 +time/exploration sampling (s) 7.25088 +time/logging (s) 0.0125683 +time/saving (s) 0.0189572 +time/training (s) 20.1978 +time/epoch (s) 32.3839 +time/total (s) 10086.2 +Epoch -531 +------------------------------ ---------------- +2022-05-15 20:51:06.870968 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -530 finished +------------------------------ ---------------- +epoch -530 +replay_buffer/size 999047 +trainer/num train calls 471000 +trainer/QF1 Loss 13.2175 +trainer/QF2 Loss 13.3103 +trainer/Policy Loss 9.54909 +trainer/Q1 Predictions Mean -73.7423 +trainer/Q1 Predictions Std 16.6295 +trainer/Q1 Predictions Max -1.39208 +trainer/Q1 Predictions Min -86.3466 +trainer/Q2 Predictions Mean -73.7868 +trainer/Q2 Predictions Std 16.6425 +trainer/Q2 Predictions Max -1.40659 +trainer/Q2 Predictions Min -86.5349 +trainer/Q Targets Mean -73.5169 +trainer/Q Targets Std 16.8972 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7575 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0112454 +trainer/policy/mean Std 0.717597 +trainer/policy/mean Max 0.999077 +trainer/policy/mean Min -0.997763 +trainer/policy/std Mean 0.420768 +trainer/policy/std Std 0.0198761 +trainer/policy/std Max 0.441387 +trainer/policy/std Min 0.389346 +trainer/Advantage Weights Mean 1.97208 +trainer/Advantage Weights Std 11.3627 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.87139e-19 +trainer/Advantage Score Mean -0.499719 +trainer/Advantage Score Std 0.656427 +trainer/Advantage Score Max 1.07968 +trainer/Advantage Score Min -4.21657 +trainer/V1 Predictions Mean -73.4312 +trainer/V1 Predictions Std 16.8181 +trainer/V1 Predictions Max 0.454128 +trainer/V1 Predictions Min -86.6835 +trainer/VF Loss 0.0769498 +expl/num steps total 471000 +expl/num paths total 574 +expl/path length Mean 500 +expl/path length Std 147 +expl/path length Max 647 +expl/path length Min 353 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0177272 +expl/Actions Std 0.855981 +expl/Actions Max 2.50727 +expl/Actions Min -2.5297 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 449305 +eval/num paths total 472 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.263261 +eval/Actions Std 0.663931 +eval/Actions Max 0.999736 +eval/Actions Min -0.999736 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.26301e-05 +time/evaluation sampling (s) 5.1975 +time/exploration sampling (s) 7.84275 +time/logging (s) 0.0116302 +time/saving (s) 0.015668 +time/training (s) 20.362 +time/epoch (s) 33.4296 +time/total (s) 10119.6 +Epoch -530 +------------------------------ ---------------- +2022-05-15 20:51:39.060490 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -529 finished +------------------------------ ---------------- +epoch -529 +replay_buffer/size 999047 +trainer/num train calls 472000 +trainer/QF1 Loss 1.06675 +trainer/QF2 Loss 1.03981 +trainer/Policy Loss 4.6823 +trainer/Q1 Predictions Mean -73.3891 +trainer/Q1 Predictions Std 16.5925 +trainer/Q1 Predictions Max -0.649673 +trainer/Q1 Predictions Min -86.918 +trainer/Q2 Predictions Mean -73.3141 +trainer/Q2 Predictions Std 16.6155 +trainer/Q2 Predictions Max -0.915777 +trainer/Q2 Predictions Min -86.8565 +trainer/Q Targets Mean -73.0266 +trainer/Q Targets Std 16.8746 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0003 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.017604 +trainer/policy/mean Std 0.713413 +trainer/policy/mean Max 0.998358 +trainer/policy/mean Min -0.998362 +trainer/policy/std Mean 0.420598 +trainer/policy/std Std 0.020509 +trainer/policy/std Max 0.443763 +trainer/policy/std Min 0.387642 +trainer/Advantage Weights Mean 1.57336 +trainer/Advantage Weights Std 11.0496 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.33486e-17 +trainer/Advantage Score Mean -0.620508 +trainer/Advantage Score Std 0.570364 +trainer/Advantage Score Max 0.846403 +trainer/Advantage Score Min -3.69102 +trainer/V1 Predictions Mean -72.7852 +trainer/V1 Predictions Std 16.8923 +trainer/V1 Predictions Max -0.574249 +trainer/V1 Predictions Min -86.8153 +trainer/VF Loss 0.0767634 +expl/num steps total 472000 +expl/num paths total 575 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0514674 +expl/Actions Std 0.807435 +expl/Actions Max 2.29614 +expl/Actions Min -2.28031 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 450203 +eval/num paths total 474 +eval/path length Mean 449 +eval/path length Std 92 +eval/path length Max 541 +eval/path length Min 357 +eval/Rewards Mean 0.00222717 +eval/Rewards Std 0.0471403 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0312194 +eval/Actions Std 0.747921 +eval/Actions Max 0.999942 +eval/Actions Min -0.999875 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 1.30599e-05 +time/evaluation sampling (s) 5.22714 +time/exploration sampling (s) 7.31219 +time/logging (s) 0.0111257 +time/saving (s) 0.0152661 +time/training (s) 19.6081 +time/epoch (s) 32.1738 +time/total (s) 10151.8 +Epoch -529 +------------------------------ ---------------- +2022-05-15 20:52:09.973022 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -528 finished +------------------------------ ---------------- +epoch -528 +replay_buffer/size 999047 +trainer/num train calls 473000 +trainer/QF1 Loss 0.755476 +trainer/QF2 Loss 0.658735 +trainer/Policy Loss 25.1465 +trainer/Q1 Predictions Mean -72.4675 +trainer/Q1 Predictions Std 18.5784 +trainer/Q1 Predictions Max 0.29431 +trainer/Q1 Predictions Min -86.9709 +trainer/Q2 Predictions Mean -72.4118 +trainer/Q2 Predictions Std 18.6491 +trainer/Q2 Predictions Max -0.540249 +trainer/Q2 Predictions Min -86.9674 +trainer/Q Targets Mean -72.4029 +trainer/Q Targets Std 19.0081 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0529 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0348336 +trainer/policy/mean Std 0.717525 +trainer/policy/mean Max 0.999343 +trainer/policy/mean Min -0.999674 +trainer/policy/std Mean 0.420621 +trainer/policy/std Std 0.0196211 +trainer/policy/std Max 0.441127 +trainer/policy/std Min 0.389395 +trainer/Advantage Weights Mean 4.24435 +trainer/Advantage Weights Std 16.2075 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.24592e-18 +trainer/Advantage Score Mean -0.404325 +trainer/Advantage Score Std 0.711357 +trainer/Advantage Score Max 0.902241 +trainer/Advantage Score Min -4.00006 +trainer/V1 Predictions Mean -72.1357 +trainer/V1 Predictions Std 19.1488 +trainer/V1 Predictions Max 2.34069 +trainer/V1 Predictions Min -87.0185 +trainer/VF Loss 0.0801724 +expl/num steps total 473000 +expl/num paths total 577 +expl/path length Mean 500 +expl/path length Std 86 +expl/path length Max 586 +expl/path length Min 414 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0122487 +expl/Actions Std 0.825923 +expl/Actions Max 2.40755 +expl/Actions Min -2.2823 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 451173 +eval/num paths total 475 +eval/path length Mean 970 +eval/path length Std 0 +eval/path length Max 970 +eval/path length Min 970 +eval/Rewards Mean 0.00103093 +eval/Rewards Std 0.0320915 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0559593 +eval/Actions Std 0.726771 +eval/Actions Max 0.999094 +eval/Actions Min -0.999783 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.22196e-06 +time/evaluation sampling (s) 4.80262 +time/exploration sampling (s) 6.67865 +time/logging (s) 0.0124905 +time/saving (s) 0.0175526 +time/training (s) 19.3895 +time/epoch (s) 30.9008 +time/total (s) 10182.7 +Epoch -528 +------------------------------ ---------------- +2022-05-15 20:52:41.311556 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -527 finished +------------------------------ ---------------- +epoch -527 +replay_buffer/size 999047 +trainer/num train calls 474000 +trainer/QF1 Loss 0.751181 +trainer/QF2 Loss 0.76681 +trainer/Policy Loss 10.4044 +trainer/Q1 Predictions Mean -73.0989 +trainer/Q1 Predictions Std 18.5403 +trainer/Q1 Predictions Max -0.961102 +trainer/Q1 Predictions Min -86.2752 +trainer/Q2 Predictions Mean -73.0733 +trainer/Q2 Predictions Std 18.5267 +trainer/Q2 Predictions Max -1.58977 +trainer/Q2 Predictions Min -86.5775 +trainer/Q Targets Mean -73.2067 +trainer/Q Targets Std 18.5351 +trainer/Q Targets Max -1.09181 +trainer/Q Targets Min -86.5658 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000153054 +trainer/policy/mean Std 0.718561 +trainer/policy/mean Max 0.999515 +trainer/policy/mean Min -0.999742 +trainer/policy/std Mean 0.420635 +trainer/policy/std Std 0.0196618 +trainer/policy/std Max 0.440725 +trainer/policy/std Min 0.38839 +trainer/Advantage Weights Mean 2.88393 +trainer/Advantage Weights Std 12.2053 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.07958e-15 +trainer/Advantage Score Mean -0.343737 +trainer/Advantage Score Std 0.55221 +trainer/Advantage Score Max 1.00008 +trainer/Advantage Score Min -3.27338 +trainer/V1 Predictions Mean -72.9284 +trainer/V1 Predictions Std 18.7871 +trainer/V1 Predictions Max 0.911538 +trainer/V1 Predictions Min -86.4759 +trainer/VF Loss 0.0536619 +expl/num steps total 474000 +expl/num paths total 578 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0962499 +expl/Actions Std 0.812801 +expl/Actions Max 2.3559 +expl/Actions Min -2.21367 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 452132 +eval/num paths total 476 +eval/path length Mean 959 +eval/path length Std 0 +eval/path length Max 959 +eval/path length Min 959 +eval/Rewards Mean 0.00104275 +eval/Rewards Std 0.0322748 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.049553 +eval/Actions Std 0.707324 +eval/Actions Max 0.99964 +eval/Actions Min -0.999443 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2191e-05 +time/evaluation sampling (s) 5.00398 +time/exploration sampling (s) 6.82982 +time/logging (s) 0.00846242 +time/saving (s) 0.0141586 +time/training (s) 19.4625 +time/epoch (s) 31.319 +time/total (s) 10214 +Epoch -527 +------------------------------ ---------------- +2022-05-15 20:53:13.643817 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -526 finished +------------------------------ ---------------- +epoch -526 +replay_buffer/size 999047 +trainer/num train calls 475000 +trainer/QF1 Loss 0.706968 +trainer/QF2 Loss 0.762732 +trainer/Policy Loss 10.6187 +trainer/Q1 Predictions Mean -71.2541 +trainer/Q1 Predictions Std 21.3246 +trainer/Q1 Predictions Max -0.193353 +trainer/Q1 Predictions Min -86.1359 +trainer/Q2 Predictions Mean -71.2866 +trainer/Q2 Predictions Std 21.2942 +trainer/Q2 Predictions Max -0.355129 +trainer/Q2 Predictions Min -86.0689 +trainer/Q Targets Mean -71.4497 +trainer/Q Targets Std 21.6761 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3792 +trainer/rewards Mean -0.976562 +trainer/rewards Std 0.151288 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0234375 +trainer/terminals Std 0.151288 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0122835 +trainer/policy/mean Std 0.729274 +trainer/policy/mean Max 0.998597 +trainer/policy/mean Min -0.999679 +trainer/policy/std Mean 0.420509 +trainer/policy/std Std 0.0186526 +trainer/policy/std Max 0.44034 +trainer/policy/std Min 0.39128 +trainer/Advantage Weights Mean 2.55772 +trainer/Advantage Weights Std 11.567 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.48155e-24 +trainer/Advantage Score Mean -0.442725 +trainer/Advantage Score Std 0.704087 +trainer/Advantage Score Max 0.593872 +trainer/Advantage Score Min -5.37621 +trainer/V1 Predictions Mean -71.2203 +trainer/V1 Predictions Std 21.7303 +trainer/V1 Predictions Max 1.65762 +trainer/V1 Predictions Min -86.2843 +trainer/VF Loss 0.0762217 +expl/num steps total 475000 +expl/num paths total 580 +expl/path length Mean 500 +expl/path length Std 351 +expl/path length Max 851 +expl/path length Min 149 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0356055 +expl/Actions Std 0.820315 +expl/Actions Max 2.34688 +expl/Actions Min -2.27347 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 452738 +eval/num paths total 477 +eval/path length Mean 606 +eval/path length Std 0 +eval/path length Max 606 +eval/path length Min 606 +eval/Rewards Mean 0.00165017 +eval/Rewards Std 0.0405887 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 1.12901e-05 +eval/Actions Std 0.715663 +eval/Actions Max 0.999244 +eval/Actions Min -0.998663 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.97003e-06 +time/evaluation sampling (s) 5.07398 +time/exploration sampling (s) 7.09557 +time/logging (s) 0.0108193 +time/saving (s) 0.0171374 +time/training (s) 20.128 +time/epoch (s) 32.3255 +time/total (s) 10246.4 +Epoch -526 +------------------------------ ---------------- +2022-05-15 20:53:46.596364 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -525 finished +------------------------------ ---------------- +epoch -525 +replay_buffer/size 999047 +trainer/num train calls 476000 +trainer/QF1 Loss 0.75553 +trainer/QF2 Loss 0.6606 +trainer/Policy Loss 25.2564 +trainer/Q1 Predictions Mean -72.8463 +trainer/Q1 Predictions Std 18.729 +trainer/Q1 Predictions Max -2.4241 +trainer/Q1 Predictions Min -86.0391 +trainer/Q2 Predictions Mean -72.8581 +trainer/Q2 Predictions Std 18.8281 +trainer/Q2 Predictions Max -1.71142 +trainer/Q2 Predictions Min -86.0077 +trainer/Q Targets Mean -72.8601 +trainer/Q Targets Std 18.795 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9109 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.017735 +trainer/policy/mean Std 0.718549 +trainer/policy/mean Max 0.99898 +trainer/policy/mean Min -0.99789 +trainer/policy/std Mean 0.419937 +trainer/policy/std Std 0.0197707 +trainer/policy/std Max 0.438575 +trainer/policy/std Min 0.389725 +trainer/Advantage Weights Mean 4.86723 +trainer/Advantage Weights Std 20.2507 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.03384e-15 +trainer/Advantage Score Mean -0.420052 +trainer/Advantage Score Std 0.474521 +trainer/Advantage Score Max 0.884087 +trainer/Advantage Score Min -3.2588 +trainer/V1 Predictions Mean -72.6277 +trainer/V1 Predictions Std 18.7696 +trainer/V1 Predictions Max -1.55104 +trainer/V1 Predictions Min -85.7804 +trainer/VF Loss 0.0552393 +expl/num steps total 476000 +expl/num paths total 582 +expl/path length Mean 500 +expl/path length Std 11 +expl/path length Max 511 +expl/path length Min 489 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0280732 +expl/Actions Std 0.815887 +expl/Actions Max 2.14642 +expl/Actions Min -2.35983 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 453738 +eval/num paths total 478 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0413664 +eval/Actions Std 0.769816 +eval/Actions Max 0.999405 +eval/Actions Min -0.999914 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.21823e-06 +time/evaluation sampling (s) 5.18606 +time/exploration sampling (s) 7.19178 +time/logging (s) 0.0121602 +time/saving (s) 0.017249 +time/training (s) 20.5313 +time/epoch (s) 32.9385 +time/total (s) 10279.3 +Epoch -525 +------------------------------ ---------------- +2022-05-15 20:54:18.766539 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -524 finished +------------------------------ ---------------- +epoch -524 +replay_buffer/size 999047 +trainer/num train calls 477000 +trainer/QF1 Loss 0.8971 +trainer/QF2 Loss 0.911859 +trainer/Policy Loss 52.5262 +trainer/Q1 Predictions Mean -73.0555 +trainer/Q1 Predictions Std 18.4994 +trainer/Q1 Predictions Max -0.938913 +trainer/Q1 Predictions Min -86.4503 +trainer/Q2 Predictions Mean -73.0564 +trainer/Q2 Predictions Std 18.5312 +trainer/Q2 Predictions Max -0.479866 +trainer/Q2 Predictions Min -86.3898 +trainer/Q Targets Mean -73.5038 +trainer/Q Targets Std 18.3576 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0867 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0199377 +trainer/policy/mean Std 0.727233 +trainer/policy/mean Max 0.998412 +trainer/policy/mean Min -0.998938 +trainer/policy/std Mean 0.420193 +trainer/policy/std Std 0.0203783 +trainer/policy/std Max 0.439113 +trainer/policy/std Min 0.386824 +trainer/Advantage Weights Mean 8.16682 +trainer/Advantage Weights Std 21.9844 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.81445e-11 +trainer/Advantage Score Mean -0.161602 +trainer/Advantage Score Std 0.499888 +trainer/Advantage Score Max 1.63427 +trainer/Advantage Score Min -2.39896 +trainer/V1 Predictions Mean -73.2611 +trainer/V1 Predictions Std 18.4752 +trainer/V1 Predictions Max -0.33883 +trainer/V1 Predictions Min -86.9477 +trainer/VF Loss 0.0732886 +expl/num steps total 477000 +expl/num paths total 583 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00440418 +expl/Actions Std 0.832664 +expl/Actions Max 2.24125 +expl/Actions Min -2.25972 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 454738 +eval/num paths total 479 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0821456 +eval/Actions Std 0.75086 +eval/Actions Max 0.999694 +eval/Actions Min -0.999712 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.23349e-05 +time/evaluation sampling (s) 4.82321 +time/exploration sampling (s) 7.04311 +time/logging (s) 0.0125043 +time/saving (s) 0.0183013 +time/training (s) 20.2556 +time/epoch (s) 32.1528 +time/total (s) 10311.5 +Epoch -524 +------------------------------ ---------------- +2022-05-15 20:54:50.274595 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -523 finished +------------------------------ ---------------- +epoch -523 +replay_buffer/size 999047 +trainer/num train calls 478000 +trainer/QF1 Loss 0.778966 +trainer/QF2 Loss 0.769416 +trainer/Policy Loss 19.6391 +trainer/Q1 Predictions Mean -72.4268 +trainer/Q1 Predictions Std 19.4054 +trainer/Q1 Predictions Max -0.816403 +trainer/Q1 Predictions Min -86.8652 +trainer/Q2 Predictions Mean -72.3374 +trainer/Q2 Predictions Std 19.3859 +trainer/Q2 Predictions Max -0.767219 +trainer/Q2 Predictions Min -86.8838 +trainer/Q Targets Mean -72.5445 +trainer/Q Targets Std 19.3147 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1301 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0159743 +trainer/policy/mean Std 0.712761 +trainer/policy/mean Max 0.997853 +trainer/policy/mean Min -0.999724 +trainer/policy/std Mean 0.421118 +trainer/policy/std Std 0.020502 +trainer/policy/std Max 0.441529 +trainer/policy/std Min 0.388586 +trainer/Advantage Weights Mean 4.98977 +trainer/Advantage Weights Std 19.2853 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.10169e-15 +trainer/Advantage Score Mean -0.361767 +trainer/Advantage Score Std 0.541367 +trainer/Advantage Score Max 1.25341 +trainer/Advantage Score Min -3.34068 +trainer/V1 Predictions Mean -72.2714 +trainer/V1 Predictions Std 19.4999 +trainer/V1 Predictions Max 0.136287 +trainer/V1 Predictions Min -86.9959 +trainer/VF Loss 0.0609314 +expl/num steps total 478000 +expl/num paths total 584 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.139925 +expl/Actions Std 0.804311 +expl/Actions Max 2.50502 +expl/Actions Min -2.24031 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 455738 +eval/num paths total 480 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0630913 +eval/Actions Std 0.695568 +eval/Actions Max 0.999569 +eval/Actions Min -0.999556 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.07419e-05 +time/evaluation sampling (s) 4.72083 +time/exploration sampling (s) 7.17883 +time/logging (s) 0.0102708 +time/saving (s) 0.0159205 +time/training (s) 19.5638 +time/epoch (s) 31.4897 +time/total (s) 10343 +Epoch -523 +------------------------------ ---------------- +2022-05-15 20:55:22.392044 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -522 finished +------------------------------ ---------------- +epoch -522 +replay_buffer/size 999047 +trainer/num train calls 479000 +trainer/QF1 Loss 1.28102 +trainer/QF2 Loss 1.14713 +trainer/Policy Loss 7.78076 +trainer/Q1 Predictions Mean -73.2891 +trainer/Q1 Predictions Std 18.5436 +trainer/Q1 Predictions Max -1.31932 +trainer/Q1 Predictions Min -87.465 +trainer/Q2 Predictions Mean -73.3005 +trainer/Q2 Predictions Std 18.5163 +trainer/Q2 Predictions Max -1.62721 +trainer/Q2 Predictions Min -87.8488 +trainer/Q Targets Mean -73.1994 +trainer/Q Targets Std 18.555 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2992 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000907781 +trainer/policy/mean Std 0.720355 +trainer/policy/mean Max 0.999664 +trainer/policy/mean Min -0.998932 +trainer/policy/std Mean 0.421967 +trainer/policy/std Std 0.0208781 +trainer/policy/std Max 0.442857 +trainer/policy/std Min 0.387811 +trainer/Advantage Weights Mean 1.94985 +trainer/Advantage Weights Std 11.614 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.63994e-21 +trainer/Advantage Score Mean -0.445484 +trainer/Advantage Score Std 0.5938 +trainer/Advantage Score Max 0.910419 +trainer/Advantage Score Min -4.61979 +trainer/V1 Predictions Mean -72.9064 +trainer/V1 Predictions Std 18.7061 +trainer/V1 Predictions Max -1.51591 +trainer/V1 Predictions Min -86.9101 +trainer/VF Loss 0.0610956 +expl/num steps total 479000 +expl/num paths total 586 +expl/path length Mean 500 +expl/path length Std 153 +expl/path length Max 653 +expl/path length Min 347 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0111853 +expl/Actions Std 0.832721 +expl/Actions Max 2.38885 +expl/Actions Min -2.40687 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 456738 +eval/num paths total 481 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.310975 +eval/Actions Std 0.669851 +eval/Actions Max 0.99793 +eval/Actions Min -0.997766 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.07661e-05 +time/evaluation sampling (s) 5.00393 +time/exploration sampling (s) 7.14615 +time/logging (s) 0.0110977 +time/saving (s) 0.0163177 +time/training (s) 19.9306 +time/epoch (s) 32.1081 +time/total (s) 10375.1 +Epoch -522 +------------------------------ ---------------- +2022-05-15 20:55:55.420847 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -521 finished +------------------------------ ---------------- +epoch -521 +replay_buffer/size 999047 +trainer/num train calls 480000 +trainer/QF1 Loss 0.79268 +trainer/QF2 Loss 0.662001 +trainer/Policy Loss 9.90639 +trainer/Q1 Predictions Mean -71.6781 +trainer/Q1 Predictions Std 20.4327 +trainer/Q1 Predictions Max -0.47135 +trainer/Q1 Predictions Min -86.9035 +trainer/Q2 Predictions Mean -71.6236 +trainer/Q2 Predictions Std 20.4122 +trainer/Q2 Predictions Max -0.543998 +trainer/Q2 Predictions Min -86.7843 +trainer/Q Targets Mean -71.5383 +trainer/Q Targets Std 20.4675 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0275 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0186508 +trainer/policy/mean Std 0.717619 +trainer/policy/mean Max 0.999498 +trainer/policy/mean Min -0.997826 +trainer/policy/std Mean 0.422995 +trainer/policy/std Std 0.0213567 +trainer/policy/std Max 0.443546 +trainer/policy/std Min 0.387388 +trainer/Advantage Weights Mean 2.93155 +trainer/Advantage Weights Std 13.5262 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.53889e-18 +trainer/Advantage Score Mean -0.501396 +trainer/Advantage Score Std 0.667547 +trainer/Advantage Score Max 1.50471 +trainer/Advantage Score Min -4.10155 +trainer/V1 Predictions Mean -71.2298 +trainer/V1 Predictions Std 20.5013 +trainer/V1 Predictions Max 1.59841 +trainer/V1 Predictions Min -86.8197 +trainer/VF Loss 0.08699 +expl/num steps total 480000 +expl/num paths total 588 +expl/path length Mean 500 +expl/path length Std 213 +expl/path length Max 713 +expl/path length Min 287 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.029089 +expl/Actions Std 0.804867 +expl/Actions Max 2.25201 +expl/Actions Min -2.1931 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 457738 +eval/num paths total 482 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0760133 +eval/Actions Std 0.676796 +eval/Actions Max 0.99984 +eval/Actions Min -0.999656 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.19163e-05 +time/evaluation sampling (s) 5.20051 +time/exploration sampling (s) 7.85519 +time/logging (s) 0.00804918 +time/saving (s) 0.0120726 +time/training (s) 19.9341 +time/epoch (s) 33.0099 +time/total (s) 10408.1 +Epoch -521 +------------------------------ ---------------- +2022-05-15 20:56:27.345029 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -520 finished +------------------------------ ---------------- +epoch -520 +replay_buffer/size 999047 +trainer/num train calls 481000 +trainer/QF1 Loss 0.888847 +trainer/QF2 Loss 0.883256 +trainer/Policy Loss 22.5633 +trainer/Q1 Predictions Mean -72.3448 +trainer/Q1 Predictions Std 18.6572 +trainer/Q1 Predictions Max -1.01784 +trainer/Q1 Predictions Min -86.6043 +trainer/Q2 Predictions Mean -72.2973 +trainer/Q2 Predictions Std 18.7462 +trainer/Q2 Predictions Max -1.01533 +trainer/Q2 Predictions Min -86.4681 +trainer/Q Targets Mean -72.4696 +trainer/Q Targets Std 18.584 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.367 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.038635 +trainer/policy/mean Std 0.731621 +trainer/policy/mean Max 0.999009 +trainer/policy/mean Min -0.999135 +trainer/policy/std Mean 0.420292 +trainer/policy/std Std 0.0206799 +trainer/policy/std Max 0.442217 +trainer/policy/std Min 0.390427 +trainer/Advantage Weights Mean 3.81233 +trainer/Advantage Weights Std 17.0585 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.42077e-13 +trainer/Advantage Score Mean -0.379934 +trainer/Advantage Score Std 0.530242 +trainer/Advantage Score Max 1.54697 +trainer/Advantage Score Min -2.95824 +trainer/V1 Predictions Mean -72.1707 +trainer/V1 Predictions Std 18.8575 +trainer/V1 Predictions Max -1.07365 +trainer/V1 Predictions Min -86.3208 +trainer/VF Loss 0.058688 +expl/num steps total 481000 +expl/num paths total 590 +expl/path length Mean 500 +expl/path length Std 241 +expl/path length Max 741 +expl/path length Min 259 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0417799 +expl/Actions Std 0.85417 +expl/Actions Max 2.35262 +expl/Actions Min -2.23293 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 458738 +eval/num paths total 483 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0588716 +eval/Actions Std 0.743878 +eval/Actions Max 0.999536 +eval/Actions Min -0.999764 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.03819e-05 +time/evaluation sampling (s) 5.16342 +time/exploration sampling (s) 7.31552 +time/logging (s) 0.010399 +time/saving (s) 0.0149267 +time/training (s) 19.4114 +time/epoch (s) 31.9157 +time/total (s) 10440 +Epoch -520 +------------------------------ ---------------- +2022-05-15 20:56:59.452659 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -519 finished +------------------------------ ---------------- +epoch -519 +replay_buffer/size 999047 +trainer/num train calls 482000 +trainer/QF1 Loss 1.0326 +trainer/QF2 Loss 0.911273 +trainer/Policy Loss 21.8902 +trainer/Q1 Predictions Mean -73.4381 +trainer/Q1 Predictions Std 18.3582 +trainer/Q1 Predictions Max -0.303193 +trainer/Q1 Predictions Min -88.6865 +trainer/Q2 Predictions Mean -73.4062 +trainer/Q2 Predictions Std 18.3643 +trainer/Q2 Predictions Max -1.06003 +trainer/Q2 Predictions Min -88.6323 +trainer/Q Targets Mean -73.3252 +trainer/Q Targets Std 18.3109 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7671 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0214787 +trainer/policy/mean Std 0.727577 +trainer/policy/mean Max 0.99955 +trainer/policy/mean Min -0.999034 +trainer/policy/std Mean 0.422175 +trainer/policy/std Std 0.0208312 +trainer/policy/std Max 0.446931 +trainer/policy/std Min 0.388973 +trainer/Advantage Weights Mean 4.60165 +trainer/Advantage Weights Std 19.0476 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.76008e-13 +trainer/Advantage Score Mean -0.415317 +trainer/Advantage Score Std 0.575809 +trainer/Advantage Score Max 2.83698 +trainer/Advantage Score Min -2.93682 +trainer/V1 Predictions Mean -73.0001 +trainer/V1 Predictions Std 18.4502 +trainer/V1 Predictions Max 0.0658609 +trainer/V1 Predictions Min -87.5851 +trainer/VF Loss 0.0955798 +expl/num steps total 482000 +expl/num paths total 591 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0860287 +expl/Actions Std 0.894581 +expl/Actions Max 2.41049 +expl/Actions Min -2.48944 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 459560 +eval/num paths total 484 +eval/path length Mean 822 +eval/path length Std 0 +eval/path length Max 822 +eval/path length Min 822 +eval/Rewards Mean 0.00121655 +eval/Rewards Std 0.0348578 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0208623 +eval/Actions Std 0.72473 +eval/Actions Max 0.999492 +eval/Actions Min -0.999839 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.05742e-05 +time/evaluation sampling (s) 5.03872 +time/exploration sampling (s) 7.1723 +time/logging (s) 0.0104846 +time/saving (s) 0.0158717 +time/training (s) 19.8558 +time/epoch (s) 32.0932 +time/total (s) 10472.1 +Epoch -519 +------------------------------ ---------------- +2022-05-15 20:57:31.285042 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -518 finished +------------------------------ ---------------- +epoch -518 +replay_buffer/size 999047 +trainer/num train calls 483000 +trainer/QF1 Loss 1.21226 +trainer/QF2 Loss 1.11131 +trainer/Policy Loss 20.3896 +trainer/Q1 Predictions Mean -73.2313 +trainer/Q1 Predictions Std 18.808 +trainer/Q1 Predictions Max -0.514761 +trainer/Q1 Predictions Min -86.3853 +trainer/Q2 Predictions Mean -73.2882 +trainer/Q2 Predictions Std 18.776 +trainer/Q2 Predictions Max -0.365161 +trainer/Q2 Predictions Min -86.0314 +trainer/Q Targets Mean -73.3587 +trainer/Q Targets Std 18.4781 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8224 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0064761 +trainer/policy/mean Std 0.714472 +trainer/policy/mean Max 0.999572 +trainer/policy/mean Min -0.999872 +trainer/policy/std Mean 0.420457 +trainer/policy/std Std 0.0205351 +trainer/policy/std Max 0.444983 +trainer/policy/std Min 0.387358 +trainer/Advantage Weights Mean 3.18343 +trainer/Advantage Weights Std 16.371 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.59317e-12 +trainer/Advantage Score Mean -0.450999 +trainer/Advantage Score Std 0.485692 +trainer/Advantage Score Max 0.940935 +trainer/Advantage Score Min -2.71653 +trainer/V1 Predictions Mean -73.0748 +trainer/V1 Predictions Std 18.6823 +trainer/V1 Predictions Max 0.141305 +trainer/V1 Predictions Min -85.7381 +trainer/VF Loss 0.0559376 +expl/num steps total 483000 +expl/num paths total 592 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.267379 +expl/Actions Std 0.786132 +expl/Actions Max 2.30109 +expl/Actions Min -2.14571 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 460560 +eval/num paths total 485 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.136107 +eval/Actions Std 0.718002 +eval/Actions Max 0.999668 +eval/Actions Min -0.999923 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.20439e-05 +time/evaluation sampling (s) 5.16259 +time/exploration sampling (s) 7.1389 +time/logging (s) 0.0121508 +time/saving (s) 0.0182603 +time/training (s) 19.4885 +time/epoch (s) 31.8205 +time/total (s) 10503.9 +Epoch -518 +------------------------------ ---------------- +2022-05-15 20:58:02.879643 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -517 finished +------------------------------ ---------------- +epoch -517 +replay_buffer/size 999047 +trainer/num train calls 484000 +trainer/QF1 Loss 1.25085 +trainer/QF2 Loss 1.05445 +trainer/Policy Loss 4.28799 +trainer/Q1 Predictions Mean -73.34 +trainer/Q1 Predictions Std 18.2908 +trainer/Q1 Predictions Max -1.8564 +trainer/Q1 Predictions Min -87.4258 +trainer/Q2 Predictions Mean -73.1907 +trainer/Q2 Predictions Std 18.3053 +trainer/Q2 Predictions Max -1.58459 +trainer/Q2 Predictions Min -87.31 +trainer/Q Targets Mean -72.5437 +trainer/Q Targets Std 18.4211 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4803 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0012798 +trainer/policy/mean Std 0.715501 +trainer/policy/mean Max 0.998946 +trainer/policy/mean Min -0.999451 +trainer/policy/std Mean 0.420432 +trainer/policy/std Std 0.0207773 +trainer/policy/std Max 0.446252 +trainer/policy/std Min 0.385372 +trainer/Advantage Weights Mean 1.20571 +trainer/Advantage Weights Std 10.7589 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.71528e-22 +trainer/Advantage Score Mean -0.806552 +trainer/Advantage Score Std 0.676866 +trainer/Advantage Score Max 0.819271 +trainer/Advantage Score Min -4.91061 +trainer/V1 Predictions Mean -72.2343 +trainer/V1 Predictions Std 18.4976 +trainer/V1 Predictions Max -0.0386044 +trainer/V1 Predictions Min -86.5135 +trainer/VF Loss 0.115689 +expl/num steps total 484000 +expl/num paths total 594 +expl/path length Mean 500 +expl/path length Std 130 +expl/path length Max 630 +expl/path length Min 370 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.016321 +expl/Actions Std 0.841061 +expl/Actions Max 2.40087 +expl/Actions Min -2.40548 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 461560 +eval/num paths total 486 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.141789 +eval/Actions Std 0.722839 +eval/Actions Max 0.999467 +eval/Actions Min -0.999312 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.52366e-06 +time/evaluation sampling (s) 5.24539 +time/exploration sampling (s) 6.58603 +time/logging (s) 0.0113095 +time/saving (s) 0.0131527 +time/training (s) 19.7228 +time/epoch (s) 31.5787 +time/total (s) 10535.5 +Epoch -517 +------------------------------ ---------------- +2022-05-15 20:58:34.012988 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -516 finished +------------------------------ ---------------- +epoch -516 +replay_buffer/size 999047 +trainer/num train calls 485000 +trainer/QF1 Loss 5.10136 +trainer/QF2 Loss 5.05226 +trainer/Policy Loss 22.1866 +trainer/Q1 Predictions Mean -73.6079 +trainer/Q1 Predictions Std 18.1704 +trainer/Q1 Predictions Max -1.69578 +trainer/Q1 Predictions Min -86.4814 +trainer/Q2 Predictions Mean -73.5166 +trainer/Q2 Predictions Std 18.2016 +trainer/Q2 Predictions Max -0.905384 +trainer/Q2 Predictions Min -86.5721 +trainer/Q Targets Mean -73.3761 +trainer/Q Targets Std 18.5304 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3022 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0110567 +trainer/policy/mean Std 0.719571 +trainer/policy/mean Max 0.998352 +trainer/policy/mean Min -0.999142 +trainer/policy/std Mean 0.419465 +trainer/policy/std Std 0.0204527 +trainer/policy/std Max 0.444767 +trainer/policy/std Min 0.385385 +trainer/Advantage Weights Mean 5.45327 +trainer/Advantage Weights Std 19.7569 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.27167e-16 +trainer/Advantage Score Mean -0.284584 +trainer/Advantage Score Std 0.556547 +trainer/Advantage Score Max 1.66311 +trainer/Advantage Score Min -3.6601 +trainer/V1 Predictions Mean -73.2957 +trainer/V1 Predictions Std 18.475 +trainer/V1 Predictions Max 0.167567 +trainer/V1 Predictions Min -86.2932 +trainer/VF Loss 0.0630892 +expl/num steps total 485000 +expl/num paths total 596 +expl/path length Mean 500 +expl/path length Std 465 +expl/path length Max 965 +expl/path length Min 35 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0242941 +expl/Actions Std 0.824091 +expl/Actions Max 2.36262 +expl/Actions Min -2.82536 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 462560 +eval/num paths total 487 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0415299 +eval/Actions Std 0.703649 +eval/Actions Max 0.999203 +eval/Actions Min -0.999367 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.94932e-06 +time/evaluation sampling (s) 5.29542 +time/exploration sampling (s) 6.52659 +time/logging (s) 0.0114004 +time/saving (s) 0.0329996 +time/training (s) 19.2521 +time/epoch (s) 31.1186 +time/total (s) 10566.7 +Epoch -516 +------------------------------ ---------------- +2022-05-15 20:59:05.655935 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -515 finished +------------------------------ ---------------- +epoch -515 +replay_buffer/size 999047 +trainer/num train calls 486000 +trainer/QF1 Loss 0.534775 +trainer/QF2 Loss 0.588836 +trainer/Policy Loss 8.64183 +trainer/Q1 Predictions Mean -73.4657 +trainer/Q1 Predictions Std 18.4326 +trainer/Q1 Predictions Max -0.953476 +trainer/Q1 Predictions Min -87.315 +trainer/Q2 Predictions Mean -73.4953 +trainer/Q2 Predictions Std 18.4899 +trainer/Q2 Predictions Max -0.0367264 +trainer/Q2 Predictions Min -87.4532 +trainer/Q Targets Mean -73.1007 +trainer/Q Targets Std 18.4811 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1119 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0142189 +trainer/policy/mean Std 0.724897 +trainer/policy/mean Max 0.998938 +trainer/policy/mean Min -0.999452 +trainer/policy/std Mean 0.420558 +trainer/policy/std Std 0.0202356 +trainer/policy/std Max 0.443526 +trainer/policy/std Min 0.386966 +trainer/Advantage Weights Mean 1.4335 +trainer/Advantage Weights Std 10.8192 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.23084e-15 +trainer/Advantage Score Mean -0.549471 +trainer/Advantage Score Std 0.537499 +trainer/Advantage Score Max 1.4172 +trainer/Advantage Score Min -3.23162 +trainer/V1 Predictions Mean -72.9309 +trainer/V1 Predictions Std 18.4728 +trainer/V1 Predictions Max 0.717207 +trainer/V1 Predictions Min -86.9124 +trainer/VF Loss 0.0693248 +expl/num steps total 486000 +expl/num paths total 597 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0184368 +expl/Actions Std 0.839571 +expl/Actions Max 2.75901 +expl/Actions Min -2.31619 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 463560 +eval/num paths total 488 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0317915 +eval/Actions Std 0.736433 +eval/Actions Max 0.99944 +eval/Actions Min -0.999926 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.74392e-06 +time/evaluation sampling (s) 4.75331 +time/exploration sampling (s) 6.76239 +time/logging (s) 0.0128167 +time/saving (s) 0.0182005 +time/training (s) 20.0829 +time/epoch (s) 31.6297 +time/total (s) 10598.3 +Epoch -515 +------------------------------ ---------------- +2022-05-15 20:59:39.334542 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -514 finished +------------------------------ ---------------- +epoch -514 +replay_buffer/size 999047 +trainer/num train calls 487000 +trainer/QF1 Loss 0.817734 +trainer/QF2 Loss 0.831653 +trainer/Policy Loss 22.5776 +trainer/Q1 Predictions Mean -71.6188 +trainer/Q1 Predictions Std 20.5598 +trainer/Q1 Predictions Max -0.384067 +trainer/Q1 Predictions Min -87.4815 +trainer/Q2 Predictions Mean -71.5775 +trainer/Q2 Predictions Std 20.5631 +trainer/Q2 Predictions Max -0.418097 +trainer/Q2 Predictions Min -87.1517 +trainer/Q Targets Mean -71.8697 +trainer/Q Targets Std 20.438 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.59 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0045544 +trainer/policy/mean Std 0.725309 +trainer/policy/mean Max 0.999574 +trainer/policy/mean Min -0.999738 +trainer/policy/std Mean 0.420595 +trainer/policy/std Std 0.0215994 +trainer/policy/std Max 0.445283 +trainer/policy/std Min 0.386186 +trainer/Advantage Weights Mean 7.24395 +trainer/Advantage Weights Std 21.9708 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.11952e-17 +trainer/Advantage Score Mean -0.247945 +trainer/Advantage Score Std 0.587813 +trainer/Advantage Score Max 1.69561 +trainer/Advantage Score Min -3.70497 +trainer/V1 Predictions Mean -71.6267 +trainer/V1 Predictions Std 20.4695 +trainer/V1 Predictions Max -0.118678 +trainer/V1 Predictions Min -87.3665 +trainer/VF Loss 0.0789982 +expl/num steps total 487000 +expl/num paths total 599 +expl/path length Mean 500 +expl/path length Std 131 +expl/path length Max 631 +expl/path length Min 369 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0410475 +expl/Actions Std 0.818502 +expl/Actions Max 2.48069 +expl/Actions Min -2.15168 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 464514 +eval/num paths total 489 +eval/path length Mean 954 +eval/path length Std 0 +eval/path length Max 954 +eval/path length Min 954 +eval/Rewards Mean 0.00104822 +eval/Rewards Std 0.0323592 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0553905 +eval/Actions Std 0.723775 +eval/Actions Max 0.99982 +eval/Actions Min -0.999881 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.24681e-05 +time/evaluation sampling (s) 5.17099 +time/exploration sampling (s) 7.85134 +time/logging (s) 0.00903832 +time/saving (s) 0.0146353 +time/training (s) 20.613 +time/epoch (s) 33.659 +time/total (s) 10632 +Epoch -514 +------------------------------ ---------------- +2022-05-15 21:00:11.499641 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -513 finished +------------------------------ ---------------- +epoch -513 +replay_buffer/size 999047 +trainer/num train calls 488000 +trainer/QF1 Loss 0.470853 +trainer/QF2 Loss 0.390664 +trainer/Policy Loss 18.1574 +trainer/Q1 Predictions Mean -75.3727 +trainer/Q1 Predictions Std 16.8447 +trainer/Q1 Predictions Max 0.0642677 +trainer/Q1 Predictions Min -89.0851 +trainer/Q2 Predictions Mean -75.296 +trainer/Q2 Predictions Std 16.8792 +trainer/Q2 Predictions Max -0.788151 +trainer/Q2 Predictions Min -88.7799 +trainer/Q Targets Mean -75.2464 +trainer/Q Targets Std 16.9942 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4619 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0131874 +trainer/policy/mean Std 0.730571 +trainer/policy/mean Max 0.999638 +trainer/policy/mean Min -0.998487 +trainer/policy/std Mean 0.419368 +trainer/policy/std Std 0.0222971 +trainer/policy/std Max 0.442311 +trainer/policy/std Min 0.383525 +trainer/Advantage Weights Mean 3.91944 +trainer/Advantage Weights Std 16.7598 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.06943e-11 +trainer/Advantage Score Mean -0.298878 +trainer/Advantage Score Std 0.453761 +trainer/Advantage Score Max 1.15477 +trainer/Advantage Score Min -2.52613 +trainer/V1 Predictions Mean -75.0547 +trainer/V1 Predictions Std 16.9419 +trainer/V1 Predictions Max 0.886269 +trainer/V1 Predictions Min -88.3079 +trainer/VF Loss 0.0446643 +expl/num steps total 488000 +expl/num paths total 601 +expl/path length Mean 500 +expl/path length Std 274 +expl/path length Max 774 +expl/path length Min 226 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.021451 +expl/Actions Std 0.820078 +expl/Actions Max 2.35359 +expl/Actions Min -2.39981 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 465514 +eval/num paths total 490 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00166957 +eval/Actions Std 0.723348 +eval/Actions Max 0.999713 +eval/Actions Min -0.999723 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.31032e-05 +time/evaluation sampling (s) 5.06811 +time/exploration sampling (s) 7.55771 +time/logging (s) 0.00882097 +time/saving (s) 0.0126822 +time/training (s) 19.5067 +time/epoch (s) 32.154 +time/total (s) 10664.1 +Epoch -513 +------------------------------ ---------------- +2022-05-15 21:00:43.231561 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -512 finished +------------------------------ ---------------- +epoch -512 +replay_buffer/size 999047 +trainer/num train calls 489000 +trainer/QF1 Loss 1.15992 +trainer/QF2 Loss 1.22591 +trainer/Policy Loss 7.6171 +trainer/Q1 Predictions Mean -74.1642 +trainer/Q1 Predictions Std 17.9395 +trainer/Q1 Predictions Max -1.444 +trainer/Q1 Predictions Min -87.0802 +trainer/Q2 Predictions Mean -74.0982 +trainer/Q2 Predictions Std 17.9487 +trainer/Q2 Predictions Max -1.11348 +trainer/Q2 Predictions Min -87.3143 +trainer/Q Targets Mean -73.8764 +trainer/Q Targets Std 18.0013 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2929 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00115756 +trainer/policy/mean Std 0.725384 +trainer/policy/mean Max 0.999558 +trainer/policy/mean Min -0.999628 +trainer/policy/std Mean 0.421446 +trainer/policy/std Std 0.0207378 +trainer/policy/std Max 0.44274 +trainer/policy/std Min 0.389227 +trainer/Advantage Weights Mean 1.76093 +trainer/Advantage Weights Std 9.382 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.15479e-25 +trainer/Advantage Score Mean -0.44799 +trainer/Advantage Score Std 0.571963 +trainer/Advantage Score Max 1.19066 +trainer/Advantage Score Min -5.61404 +trainer/V1 Predictions Mean -73.6463 +trainer/V1 Predictions Std 17.9791 +trainer/V1 Predictions Max 0.309454 +trainer/V1 Predictions Min -87.1003 +trainer/VF Loss 0.0626557 +expl/num steps total 489000 +expl/num paths total 603 +expl/path length Mean 500 +expl/path length Std 98 +expl/path length Max 598 +expl/path length Min 402 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0374642 +expl/Actions Std 0.824862 +expl/Actions Max 2.75494 +expl/Actions Min -2.46558 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 466514 +eval/num paths total 491 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0186643 +eval/Actions Std 0.647711 +eval/Actions Max 0.999757 +eval/Actions Min -0.999866 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.10758e-05 +time/evaluation sampling (s) 5.57274 +time/exploration sampling (s) 7.18744 +time/logging (s) 0.00959888 +time/saving (s) 0.0165116 +time/training (s) 18.9362 +time/epoch (s) 31.7225 +time/total (s) 10695.9 +Epoch -512 +------------------------------ ---------------- +2022-05-15 21:01:15.165957 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -511 finished +------------------------------ ---------------- +epoch -511 +replay_buffer/size 999047 +trainer/num train calls 490000 +trainer/QF1 Loss 1.11099 +trainer/QF2 Loss 1.08224 +trainer/Policy Loss 18.0551 +trainer/Q1 Predictions Mean -73.0988 +trainer/Q1 Predictions Std 18.1473 +trainer/Q1 Predictions Max -2.95703 +trainer/Q1 Predictions Min -87.0554 +trainer/Q2 Predictions Mean -72.9998 +trainer/Q2 Predictions Std 18.1202 +trainer/Q2 Predictions Max -2.61941 +trainer/Q2 Predictions Min -86.7451 +trainer/Q Targets Mean -73.3641 +trainer/Q Targets Std 18.2351 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2836 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0130653 +trainer/policy/mean Std 0.734631 +trainer/policy/mean Max 0.998486 +trainer/policy/mean Min -0.999206 +trainer/policy/std Mean 0.421282 +trainer/policy/std Std 0.0212092 +trainer/policy/std Max 0.446484 +trainer/policy/std Min 0.386737 +trainer/Advantage Weights Mean 5.22295 +trainer/Advantage Weights Std 18.5783 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.28537e-18 +trainer/Advantage Score Mean -0.287878 +trainer/Advantage Score Std 0.578707 +trainer/Advantage Score Max 1.84795 +trainer/Advantage Score Min -4.11955 +trainer/V1 Predictions Mean -73.1541 +trainer/V1 Predictions Std 18.2109 +trainer/V1 Predictions Max -2.28836 +trainer/V1 Predictions Min -87.2489 +trainer/VF Loss 0.0795534 +expl/num steps total 490000 +expl/num paths total 604 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0373681 +expl/Actions Std 0.827504 +expl/Actions Max 2.35812 +expl/Actions Min -2.26693 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 467514 +eval/num paths total 492 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.10885 +eval/Actions Std 0.691982 +eval/Actions Max 0.998662 +eval/Actions Min -0.99926 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.08361e-06 +time/evaluation sampling (s) 4.9996 +time/exploration sampling (s) 6.72589 +time/logging (s) 0.0124664 +time/saving (s) 0.0214403 +time/training (s) 20.1603 +time/epoch (s) 31.9197 +time/total (s) 10727.8 +Epoch -511 +------------------------------ ---------------- +2022-05-15 21:01:47.134507 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -510 finished +------------------------------ ---------------- +epoch -510 +replay_buffer/size 999047 +trainer/num train calls 491000 +trainer/QF1 Loss 0.905401 +trainer/QF2 Loss 0.83763 +trainer/Policy Loss 44.3392 +trainer/Q1 Predictions Mean -73.6006 +trainer/Q1 Predictions Std 17.9145 +trainer/Q1 Predictions Max -2.76314 +trainer/Q1 Predictions Min -88.6001 +trainer/Q2 Predictions Mean -73.6276 +trainer/Q2 Predictions Std 17.8632 +trainer/Q2 Predictions Max -3.00728 +trainer/Q2 Predictions Min -88.8552 +trainer/Q Targets Mean -73.8035 +trainer/Q Targets Std 17.4886 +trainer/Q Targets Max -4.26591 +trainer/Q Targets Min -88.1807 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.011738 +trainer/policy/mean Std 0.712465 +trainer/policy/mean Max 0.997574 +trainer/policy/mean Min -0.998298 +trainer/policy/std Mean 0.420009 +trainer/policy/std Std 0.0208786 +trainer/policy/std Max 0.442354 +trainer/policy/std Min 0.383446 +trainer/Advantage Weights Mean 6.97056 +trainer/Advantage Weights Std 22.6163 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.63388e-14 +trainer/Advantage Score Mean -0.240102 +trainer/Advantage Score Std 0.520539 +trainer/Advantage Score Max 1.98746 +trainer/Advantage Score Min -3.05074 +trainer/V1 Predictions Mean -73.5359 +trainer/V1 Predictions Std 17.7555 +trainer/V1 Predictions Max -2.38438 +trainer/V1 Predictions Min -87.8807 +trainer/VF Loss 0.0831971 +expl/num steps total 491000 +expl/num paths total 606 +expl/path length Mean 500 +expl/path length Std 437 +expl/path length Max 937 +expl/path length Min 63 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0520504 +expl/Actions Std 0.833181 +expl/Actions Max 2.65884 +expl/Actions Min -2.34959 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 468514 +eval/num paths total 493 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0236877 +eval/Actions Std 0.704792 +eval/Actions Max 0.999699 +eval/Actions Min -0.998935 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.52569e-05 +time/evaluation sampling (s) 5.13116 +time/exploration sampling (s) 6.79019 +time/logging (s) 0.0105033 +time/saving (s) 0.0149456 +time/training (s) 20.0062 +time/epoch (s) 31.953 +time/total (s) 10759.7 +Epoch -510 +------------------------------ ---------------- +2022-05-15 21:02:19.235711 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -509 finished +------------------------------ ---------------- +epoch -509 +replay_buffer/size 999047 +trainer/num train calls 492000 +trainer/QF1 Loss 0.550218 +trainer/QF2 Loss 0.646899 +trainer/Policy Loss 24.8256 +trainer/Q1 Predictions Mean -73.4473 +trainer/Q1 Predictions Std 18.6312 +trainer/Q1 Predictions Max -0.901083 +trainer/Q1 Predictions Min -86.9515 +trainer/Q2 Predictions Mean -73.4101 +trainer/Q2 Predictions Std 18.619 +trainer/Q2 Predictions Max -0.602722 +trainer/Q2 Predictions Min -87.0263 +trainer/Q Targets Mean -73.5971 +trainer/Q Targets Std 18.8409 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4109 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0118605 +trainer/policy/mean Std 0.724039 +trainer/policy/mean Max 0.999106 +trainer/policy/mean Min -0.999665 +trainer/policy/std Mean 0.419995 +trainer/policy/std Std 0.0194206 +trainer/policy/std Max 0.440001 +trainer/policy/std Min 0.388481 +trainer/Advantage Weights Mean 5.44173 +trainer/Advantage Weights Std 18.3016 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.17591e-16 +trainer/Advantage Score Mean -0.269604 +trainer/Advantage Score Std 0.578112 +trainer/Advantage Score Max 0.949533 +trainer/Advantage Score Min -3.56858 +trainer/V1 Predictions Mean -73.4155 +trainer/V1 Predictions Std 18.7368 +trainer/V1 Predictions Max 0.200498 +trainer/V1 Predictions Min -87.1511 +trainer/VF Loss 0.0609092 +expl/num steps total 492000 +expl/num paths total 607 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.104132 +expl/Actions Std 0.92805 +expl/Actions Max 2.28003 +expl/Actions Min -2.44366 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 469514 +eval/num paths total 494 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.179301 +eval/Actions Std 0.727984 +eval/Actions Max 0.999027 +eval/Actions Min -0.999412 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2402e-05 +time/evaluation sampling (s) 5.04453 +time/exploration sampling (s) 6.62024 +time/logging (s) 0.011407 +time/saving (s) 0.0129846 +time/training (s) 20.3997 +time/epoch (s) 32.0888 +time/total (s) 10791.8 +Epoch -509 +------------------------------ ---------------- +2022-05-15 21:02:51.583403 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -508 finished +------------------------------ ---------------- +epoch -508 +replay_buffer/size 999047 +trainer/num train calls 493000 +trainer/QF1 Loss 0.702208 +trainer/QF2 Loss 0.708433 +trainer/Policy Loss 26.4634 +trainer/Q1 Predictions Mean -72.475 +trainer/Q1 Predictions Std 18.2018 +trainer/Q1 Predictions Max 0.0375808 +trainer/Q1 Predictions Min -86.4624 +trainer/Q2 Predictions Mean -72.4768 +trainer/Q2 Predictions Std 18.1588 +trainer/Q2 Predictions Max -0.343216 +trainer/Q2 Predictions Min -86.6475 +trainer/Q Targets Mean -72.8688 +trainer/Q Targets Std 18.4043 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4322 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.02103 +trainer/policy/mean Std 0.726104 +trainer/policy/mean Max 0.999431 +trainer/policy/mean Min -0.997739 +trainer/policy/std Mean 0.420725 +trainer/policy/std Std 0.0209549 +trainer/policy/std Max 0.441769 +trainer/policy/std Min 0.388377 +trainer/Advantage Weights Mean 5.78682 +trainer/Advantage Weights Std 17.3578 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.40192e-15 +trainer/Advantage Score Mean -0.21767 +trainer/Advantage Score Std 0.552841 +trainer/Advantage Score Max 1.01131 +trainer/Advantage Score Min -3.36625 +trainer/V1 Predictions Mean -72.6188 +trainer/V1 Predictions Std 18.5151 +trainer/V1 Predictions Max 0.402471 +trainer/V1 Predictions Min -87.319 +trainer/VF Loss 0.0567959 +expl/num steps total 493000 +expl/num paths total 609 +expl/path length Mean 500 +expl/path length Std 73 +expl/path length Max 573 +expl/path length Min 427 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0122626 +expl/Actions Std 0.833158 +expl/Actions Max 2.20295 +expl/Actions Min -2.51264 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 469994 +eval/num paths total 495 +eval/path length Mean 480 +eval/path length Std 0 +eval/path length Max 480 +eval/path length Min 480 +eval/Rewards Mean 0.00208333 +eval/Rewards Std 0.045596 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0362737 +eval/Actions Std 0.716347 +eval/Actions Max 0.999753 +eval/Actions Min -0.999025 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.03437e-05 +time/evaluation sampling (s) 4.93526 +time/exploration sampling (s) 7.30475 +time/logging (s) 0.0102201 +time/saving (s) 0.017635 +time/training (s) 20.0648 +time/epoch (s) 32.3327 +time/total (s) 10824.2 +Epoch -508 +------------------------------ ---------------- +2022-05-15 21:03:23.565730 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -507 finished +------------------------------ ---------------- +epoch -507 +replay_buffer/size 999047 +trainer/num train calls 494000 +trainer/QF1 Loss 0.397281 +trainer/QF2 Loss 0.372727 +trainer/Policy Loss 8.18317 +trainer/Q1 Predictions Mean -76.0134 +trainer/Q1 Predictions Std 14.672 +trainer/Q1 Predictions Max 0.0766341 +trainer/Q1 Predictions Min -87.044 +trainer/Q2 Predictions Mean -76.0268 +trainer/Q2 Predictions Std 14.6561 +trainer/Q2 Predictions Max -0.624136 +trainer/Q2 Predictions Min -86.8608 +trainer/Q Targets Mean -76.0507 +trainer/Q Targets Std 14.7303 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.123 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00395123 +trainer/policy/mean Std 0.724409 +trainer/policy/mean Max 0.99908 +trainer/policy/mean Min -0.999622 +trainer/policy/std Mean 0.420577 +trainer/policy/std Std 0.0208702 +trainer/policy/std Max 0.442191 +trainer/policy/std Min 0.3897 +trainer/Advantage Weights Mean 1.46664 +trainer/Advantage Weights Std 10.8623 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.55478e-15 +trainer/Advantage Score Mean -0.552923 +trainer/Advantage Score Std 0.440828 +trainer/Advantage Score Max 1.80836 +trainer/Advantage Score Min -3.40974 +trainer/V1 Predictions Mean -75.8619 +trainer/V1 Predictions Std 14.5124 +trainer/V1 Predictions Max -2.45622 +trainer/V1 Predictions Min -86.975 +trainer/VF Loss 0.0653339 +expl/num steps total 494000 +expl/num paths total 610 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.188654 +expl/Actions Std 0.966419 +expl/Actions Max 2.79325 +expl/Actions Min -2.6786 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 470994 +eval/num paths total 496 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0416934 +eval/Actions Std 0.743629 +eval/Actions Max 0.999599 +eval/Actions Min -0.999452 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.05524e-05 +time/evaluation sampling (s) 5.05696 +time/exploration sampling (s) 7.18543 +time/logging (s) 0.0114695 +time/saving (s) 0.0154235 +time/training (s) 19.6988 +time/epoch (s) 31.9681 +time/total (s) 10856.1 +Epoch -507 +------------------------------ ---------------- +2022-05-15 21:03:54.665693 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -506 finished +------------------------------ ---------------- +epoch -506 +replay_buffer/size 999047 +trainer/num train calls 495000 +trainer/QF1 Loss 0.646361 +trainer/QF2 Loss 0.624815 +trainer/Policy Loss 22.9679 +trainer/Q1 Predictions Mean -75.3971 +trainer/Q1 Predictions Std 16.6263 +trainer/Q1 Predictions Max -1.92203 +trainer/Q1 Predictions Min -89.171 +trainer/Q2 Predictions Mean -75.3657 +trainer/Q2 Predictions Std 16.6571 +trainer/Q2 Predictions Max -0.848667 +trainer/Q2 Predictions Min -89.0858 +trainer/Q Targets Mean -75.2252 +trainer/Q Targets Std 16.6057 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3701 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00608017 +trainer/policy/mean Std 0.723757 +trainer/policy/mean Max 0.999065 +trainer/policy/mean Min -0.996665 +trainer/policy/std Mean 0.420455 +trainer/policy/std Std 0.0213456 +trainer/policy/std Max 0.44197 +trainer/policy/std Min 0.388305 +trainer/Advantage Weights Mean 5.2923 +trainer/Advantage Weights Std 19.9689 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.70881e-13 +trainer/Advantage Score Mean -0.398379 +trainer/Advantage Score Std 0.502734 +trainer/Advantage Score Max 1.19517 +trainer/Advantage Score Min -2.93978 +trainer/V1 Predictions Mean -75.0606 +trainer/V1 Predictions Std 16.5809 +trainer/V1 Predictions Max -1.58037 +trainer/V1 Predictions Min -87.7061 +trainer/VF Loss 0.0603695 +expl/num steps total 495000 +expl/num paths total 612 +expl/path length Mean 500 +expl/path length Std 211 +expl/path length Max 711 +expl/path length Min 289 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00999084 +expl/Actions Std 0.823132 +expl/Actions Max 2.28065 +expl/Actions Min -2.47044 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 471882 +eval/num paths total 497 +eval/path length Mean 888 +eval/path length Std 0 +eval/path length Max 888 +eval/path length Min 888 +eval/Rewards Mean 0.00112613 +eval/Rewards Std 0.0335389 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0344797 +eval/Actions Std 0.698922 +eval/Actions Max 0.999322 +eval/Actions Min -0.998722 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.01819e-06 +time/evaluation sampling (s) 4.82056 +time/exploration sampling (s) 6.63147 +time/logging (s) 0.0121072 +time/saving (s) 0.0199293 +time/training (s) 19.6025 +time/epoch (s) 31.0865 +time/total (s) 10887.2 +Epoch -506 +------------------------------ ---------------- +2022-05-15 21:04:26.505027 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -505 finished +------------------------------ ---------------- +epoch -505 +replay_buffer/size 999047 +trainer/num train calls 496000 +trainer/QF1 Loss 2.40934 +trainer/QF2 Loss 2.44567 +trainer/Policy Loss 30.6509 +trainer/Q1 Predictions Mean -74.012 +trainer/Q1 Predictions Std 17.8179 +trainer/Q1 Predictions Max -0.273362 +trainer/Q1 Predictions Min -86.8889 +trainer/Q2 Predictions Mean -73.9803 +trainer/Q2 Predictions Std 17.8768 +trainer/Q2 Predictions Max -0.894786 +trainer/Q2 Predictions Min -87.0767 +trainer/Q Targets Mean -74.3069 +trainer/Q Targets Std 17.9306 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2214 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000977194 +trainer/policy/mean Std 0.723393 +trainer/policy/mean Max 0.999708 +trainer/policy/mean Min -0.999832 +trainer/policy/std Mean 0.419248 +trainer/policy/std Std 0.0209027 +trainer/policy/std Max 0.441937 +trainer/policy/std Min 0.388682 +trainer/Advantage Weights Mean 5.87738 +trainer/Advantage Weights Std 20.0153 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.5155e-20 +trainer/Advantage Score Mean -0.2609 +trainer/Advantage Score Std 0.591918 +trainer/Advantage Score Max 2.74586 +trainer/Advantage Score Min -4.43441 +trainer/V1 Predictions Mean -74.1516 +trainer/V1 Predictions Std 17.9787 +trainer/V1 Predictions Max 0.0825974 +trainer/V1 Predictions Min -87.1886 +trainer/VF Loss 0.105863 +expl/num steps total 496000 +expl/num paths total 613 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0209899 +expl/Actions Std 0.815946 +expl/Actions Max 2.51911 +expl/Actions Min -2.33144 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 472882 +eval/num paths total 498 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.189419 +eval/Actions Std 0.762771 +eval/Actions Max 0.999866 +eval/Actions Min -0.998477 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.69316e-06 +time/evaluation sampling (s) 5.39541 +time/exploration sampling (s) 6.73788 +time/logging (s) 0.0108766 +time/saving (s) 0.0170022 +time/training (s) 19.6602 +time/epoch (s) 31.8214 +time/total (s) 10919.1 +Epoch -505 +------------------------------ ---------------- +2022-05-15 21:04:57.882403 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -504 finished +------------------------------ ---------------- +epoch -504 +replay_buffer/size 999047 +trainer/num train calls 497000 +trainer/QF1 Loss 0.520445 +trainer/QF2 Loss 0.510002 +trainer/Policy Loss 15.1866 +trainer/Q1 Predictions Mean -73.8373 +trainer/Q1 Predictions Std 16.7288 +trainer/Q1 Predictions Max -1.50995 +trainer/Q1 Predictions Min -86.9023 +trainer/Q2 Predictions Mean -73.8444 +trainer/Q2 Predictions Std 16.7696 +trainer/Q2 Predictions Max -2.10897 +trainer/Q2 Predictions Min -86.7195 +trainer/Q Targets Mean -73.8396 +trainer/Q Targets Std 16.7519 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5433 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0315419 +trainer/policy/mean Std 0.714094 +trainer/policy/mean Max 0.999348 +trainer/policy/mean Min -0.999019 +trainer/policy/std Mean 0.4197 +trainer/policy/std Std 0.0208954 +trainer/policy/std Max 0.441385 +trainer/policy/std Min 0.387987 +trainer/Advantage Weights Mean 4.35929 +trainer/Advantage Weights Std 18.3176 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.17858e-20 +trainer/Advantage Score Mean -0.412173 +trainer/Advantage Score Std 0.534742 +trainer/Advantage Score Max 1.56188 +trainer/Advantage Score Min -4.40806 +trainer/V1 Predictions Mean -73.5645 +trainer/V1 Predictions Std 16.9177 +trainer/V1 Predictions Max -1.32381 +trainer/V1 Predictions Min -86.4359 +trainer/VF Loss 0.0760426 +expl/num steps total 497000 +expl/num paths total 615 +expl/path length Mean 500 +expl/path length Std 17 +expl/path length Max 517 +expl/path length Min 483 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0400597 +expl/Actions Std 0.825218 +expl/Actions Max 2.51019 +expl/Actions Min -2.46318 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 473826 +eval/num paths total 500 +eval/path length Mean 472 +eval/path length Std 54 +eval/path length Max 526 +eval/path length Min 418 +eval/Rewards Mean 0.00211864 +eval/Rewards Std 0.0459799 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0355212 +eval/Actions Std 0.733199 +eval/Actions Max 0.99999 +eval/Actions Min -0.999651 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 9.18424e-06 +time/evaluation sampling (s) 5.02008 +time/exploration sampling (s) 7.09347 +time/logging (s) 0.0120703 +time/saving (s) 0.0148587 +time/training (s) 19.2239 +time/epoch (s) 31.3644 +time/total (s) 10950.4 +Epoch -504 +------------------------------ ---------------- +2022-05-15 21:05:29.687988 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -503 finished +------------------------------ --------------- +epoch -503 +replay_buffer/size 999047 +trainer/num train calls 498000 +trainer/QF1 Loss 1.0003 +trainer/QF2 Loss 1.03 +trainer/Policy Loss 31.9046 +trainer/Q1 Predictions Mean -73.3407 +trainer/Q1 Predictions Std 18.0103 +trainer/Q1 Predictions Max -0.737794 +trainer/Q1 Predictions Min -85.9376 +trainer/Q2 Predictions Mean -73.3001 +trainer/Q2 Predictions Std 17.9714 +trainer/Q2 Predictions Max -1.01025 +trainer/Q2 Predictions Min -85.8592 +trainer/Q Targets Mean -73.9312 +trainer/Q Targets Std 18.0736 +trainer/Q Targets Max -1.65641 +trainer/Q Targets Min -86.5803 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.019526 +trainer/policy/mean Std 0.719443 +trainer/policy/mean Max 0.999088 +trainer/policy/mean Min -0.997982 +trainer/policy/std Mean 0.421709 +trainer/policy/std Std 0.0208235 +trainer/policy/std Max 0.442158 +trainer/policy/std Min 0.387246 +trainer/Advantage Weights Mean 5.33253 +trainer/Advantage Weights Std 17.2375 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.1139e-15 +trainer/Advantage Score Mean -0.262283 +trainer/Advantage Score Std 0.606952 +trainer/Advantage Score Max 1.26318 +trainer/Advantage Score Min -3.34029 +trainer/V1 Predictions Mean -73.7007 +trainer/V1 Predictions Std 18.1327 +trainer/V1 Predictions Max -2.31138 +trainer/V1 Predictions Min -86.4054 +trainer/VF Loss 0.0677746 +expl/num steps total 498000 +expl/num paths total 616 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0361719 +expl/Actions Std 0.83095 +expl/Actions Max 2.35607 +expl/Actions Min -2.40452 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 474826 +eval/num paths total 501 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0422392 +eval/Actions Std 0.72414 +eval/Actions Max 0.99981 +eval/Actions Min -0.999661 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2489e-05 +time/evaluation sampling (s) 4.96198 +time/exploration sampling (s) 7.22892 +time/logging (s) 0.0108898 +time/saving (s) 0.0156013 +time/training (s) 19.5741 +time/epoch (s) 31.7915 +time/total (s) 10982.2 +Epoch -503 +------------------------------ --------------- +2022-05-15 21:06:01.072258 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -502 finished +------------------------------ ---------------- +epoch -502 +replay_buffer/size 999047 +trainer/num train calls 499000 +trainer/QF1 Loss 5.43532 +trainer/QF2 Loss 5.47215 +trainer/Policy Loss 7.40781 +trainer/Q1 Predictions Mean -74.8799 +trainer/Q1 Predictions Std 15.6912 +trainer/Q1 Predictions Max -0.249924 +trainer/Q1 Predictions Min -87.2725 +trainer/Q2 Predictions Mean -74.9238 +trainer/Q2 Predictions Std 15.7074 +trainer/Q2 Predictions Max -0.45166 +trainer/Q2 Predictions Min -87.1424 +trainer/Q Targets Mean -74.8145 +trainer/Q Targets Std 15.6699 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9894 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0436926 +trainer/policy/mean Std 0.728376 +trainer/policy/mean Max 0.999114 +trainer/policy/mean Min -0.999186 +trainer/policy/std Mean 0.421675 +trainer/policy/std Std 0.0203128 +trainer/policy/std Max 0.444319 +trainer/policy/std Min 0.387568 +trainer/Advantage Weights Mean 2.26412 +trainer/Advantage Weights Std 11.5576 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.75706e-13 +trainer/Advantage Score Mean -0.393169 +trainer/Advantage Score Std 0.500694 +trainer/Advantage Score Max 0.648203 +trainer/Advantage Score Min -2.937 +trainer/V1 Predictions Mean -74.6584 +trainer/V1 Predictions Std 15.691 +trainer/V1 Predictions Max -1.00417 +trainer/V1 Predictions Min -86.8602 +trainer/VF Loss 0.0473647 +expl/num steps total 499000 +expl/num paths total 618 +expl/path length Mean 500 +expl/path length Std 297 +expl/path length Max 797 +expl/path length Min 203 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0334102 +expl/Actions Std 0.831598 +expl/Actions Max 2.33248 +expl/Actions Min -2.77401 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 475457 +eval/num paths total 502 +eval/path length Mean 631 +eval/path length Std 0 +eval/path length Max 631 +eval/path length Min 631 +eval/Rewards Mean 0.00158479 +eval/Rewards Std 0.0397778 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.036752 +eval/Actions Std 0.727102 +eval/Actions Max 0.999591 +eval/Actions Min -0.998818 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.92785e-06 +time/evaluation sampling (s) 5.43083 +time/exploration sampling (s) 6.51016 +time/logging (s) 0.00725867 +time/saving (s) 0.0124817 +time/training (s) 19.4055 +time/epoch (s) 31.3663 +time/total (s) 11013.6 +Epoch -502 +------------------------------ ---------------- +2022-05-15 21:06:32.169775 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -501 finished +------------------------------ ---------------- +epoch -501 +replay_buffer/size 999047 +trainer/num train calls 500000 +trainer/QF1 Loss 0.685773 +trainer/QF2 Loss 0.663304 +trainer/Policy Loss 10.9335 +trainer/Q1 Predictions Mean -72.9158 +trainer/Q1 Predictions Std 18.732 +trainer/Q1 Predictions Max -0.826133 +trainer/Q1 Predictions Min -85.8839 +trainer/Q2 Predictions Mean -72.9564 +trainer/Q2 Predictions Std 18.7157 +trainer/Q2 Predictions Max -1.86859 +trainer/Q2 Predictions Min -86.0941 +trainer/Q Targets Mean -72.8762 +trainer/Q Targets Std 18.9243 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1709 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0282414 +trainer/policy/mean Std 0.709902 +trainer/policy/mean Max 0.998575 +trainer/policy/mean Min -0.998252 +trainer/policy/std Mean 0.420225 +trainer/policy/std Std 0.019564 +trainer/policy/std Max 0.439337 +trainer/policy/std Min 0.390141 +trainer/Advantage Weights Mean 3.04214 +trainer/Advantage Weights Std 13.9959 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.74027e-17 +trainer/Advantage Score Mean -0.369871 +trainer/Advantage Score Std 0.55102 +trainer/Advantage Score Max 1.75127 +trainer/Advantage Score Min -3.81359 +trainer/V1 Predictions Mean -72.6415 +trainer/V1 Predictions Std 18.9988 +trainer/V1 Predictions Max -1.08704 +trainer/V1 Predictions Min -86.0601 +trainer/VF Loss 0.0617858 +expl/num steps total 500000 +expl/num paths total 620 +expl/path length Mean 500 +expl/path length Std 283 +expl/path length Max 783 +expl/path length Min 217 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0387315 +expl/Actions Std 0.832616 +expl/Actions Max 2.50741 +expl/Actions Min -2.53825 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 476457 +eval/num paths total 503 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.218894 +eval/Actions Std 0.720492 +eval/Actions Max 0.999843 +eval/Actions Min -0.999153 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2456e-05 +time/evaluation sampling (s) 5.41117 +time/exploration sampling (s) 6.54985 +time/logging (s) 0.0106064 +time/saving (s) 0.0156583 +time/training (s) 19.1033 +time/epoch (s) 31.0906 +time/total (s) 11044.7 +Epoch -501 +------------------------------ ---------------- +2022-05-15 21:07:03.659880 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -500 finished +------------------------------ --------------- +epoch -500 +replay_buffer/size 999047 +trainer/num train calls 501000 +trainer/QF1 Loss 0.683519 +trainer/QF2 Loss 0.649768 +trainer/Policy Loss 13.6543 +trainer/Q1 Predictions Mean -72.4555 +trainer/Q1 Predictions Std 19.0999 +trainer/Q1 Predictions Max -0.660453 +trainer/Q1 Predictions Min -87.2854 +trainer/Q2 Predictions Mean -72.5507 +trainer/Q2 Predictions Std 19.0635 +trainer/Q2 Predictions Max -0.84303 +trainer/Q2 Predictions Min -87.5914 +trainer/Q Targets Mean -72.8667 +trainer/Q Targets Std 19.1722 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.46 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0122669 +trainer/policy/mean Std 0.731199 +trainer/policy/mean Max 0.997228 +trainer/policy/mean Min -0.997534 +trainer/policy/std Mean 0.4203 +trainer/policy/std Std 0.0197932 +trainer/policy/std Max 0.442348 +trainer/policy/std Min 0.38986 +trainer/Advantage Weights Mean 3.91001 +trainer/Advantage Weights Std 15.3866 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.127e-18 +trainer/Advantage Score Mean -0.384017 +trainer/Advantage Score Std 0.636678 +trainer/Advantage Score Max 2.36131 +trainer/Advantage Score Min -3.9812 +trainer/V1 Predictions Mean -72.5677 +trainer/V1 Predictions Std 19.313 +trainer/V1 Predictions Max 1.29329 +trainer/V1 Predictions Min -87.2583 +trainer/VF Loss 0.0815804 +expl/num steps total 501000 +expl/num paths total 621 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.15568 +expl/Actions Std 0.876223 +expl/Actions Max 2.50515 +expl/Actions Min -2.70078 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 477457 +eval/num paths total 504 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0983569 +eval/Actions Std 0.774573 +eval/Actions Max 0.999862 +eval/Actions Min -0.999887 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.8708e-06 +time/evaluation sampling (s) 5.23153 +time/exploration sampling (s) 6.18937 +time/logging (s) 0.0114609 +time/saving (s) 0.0299092 +time/training (s) 20.0151 +time/epoch (s) 31.4774 +time/total (s) 11076.2 +Epoch -500 +------------------------------ --------------- +2022-05-15 21:07:34.750529 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -499 finished +------------------------------ ---------------- +epoch -499 +replay_buffer/size 999047 +trainer/num train calls 502000 +trainer/QF1 Loss 1.04612 +trainer/QF2 Loss 1.03666 +trainer/Policy Loss 27.3667 +trainer/Q1 Predictions Mean -74.1038 +trainer/Q1 Predictions Std 17.5849 +trainer/Q1 Predictions Max -0.642059 +trainer/Q1 Predictions Min -88.1421 +trainer/Q2 Predictions Mean -74.1043 +trainer/Q2 Predictions Std 17.5089 +trainer/Q2 Predictions Max -0.284556 +trainer/Q2 Predictions Min -87.9212 +trainer/Q Targets Mean -74.2589 +trainer/Q Targets Std 17.9202 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6856 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0146363 +trainer/policy/mean Std 0.729207 +trainer/policy/mean Max 0.999179 +trainer/policy/mean Min -0.998809 +trainer/policy/std Mean 0.420576 +trainer/policy/std Std 0.0206753 +trainer/policy/std Max 0.441418 +trainer/policy/std Min 0.386861 +trainer/Advantage Weights Mean 8.26371 +trainer/Advantage Weights Std 22.7143 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.30245e-21 +trainer/Advantage Score Mean -0.257718 +trainer/Advantage Score Std 0.650185 +trainer/Advantage Score Max 0.932205 +trainer/Advantage Score Min -4.75203 +trainer/V1 Predictions Mean -74.062 +trainer/V1 Predictions Std 17.7699 +trainer/V1 Predictions Max -1.04217 +trainer/V1 Predictions Min -87.5702 +trainer/VF Loss 0.0762853 +expl/num steps total 502000 +expl/num paths total 623 +expl/path length Mean 500 +expl/path length Std 430 +expl/path length Max 930 +expl/path length Min 70 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0249058 +expl/Actions Std 0.819689 +expl/Actions Max 2.70379 +expl/Actions Min -2.05239 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 478457 +eval/num paths total 505 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.280274 +eval/Actions Std 0.802637 +eval/Actions Max 0.999527 +eval/Actions Min -0.999449 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2673e-05 +time/evaluation sampling (s) 4.85714 +time/exploration sampling (s) 6.18036 +time/logging (s) 0.00960823 +time/saving (s) 0.0161808 +time/training (s) 20.0106 +time/epoch (s) 31.0739 +time/total (s) 11107.3 +Epoch -499 +------------------------------ ---------------- +2022-05-15 21:08:06.251874 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -498 finished +------------------------------ ---------------- +epoch -498 +replay_buffer/size 999047 +trainer/num train calls 503000 +trainer/QF1 Loss 14.0076 +trainer/QF2 Loss 14.1324 +trainer/Policy Loss 4.79929 +trainer/Q1 Predictions Mean -73.534 +trainer/Q1 Predictions Std 18.8673 +trainer/Q1 Predictions Max -0.679554 +trainer/Q1 Predictions Min -87.3941 +trainer/Q2 Predictions Mean -73.4692 +trainer/Q2 Predictions Std 18.9118 +trainer/Q2 Predictions Max -0.781971 +trainer/Q2 Predictions Min -87.4473 +trainer/Q Targets Mean -73.355 +trainer/Q Targets Std 18.5328 +trainer/Q Targets Max -1.00354 +trainer/Q Targets Min -86.5251 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0219774 +trainer/policy/mean Std 0.729736 +trainer/policy/mean Max 0.99993 +trainer/policy/mean Min -0.999675 +trainer/policy/std Mean 0.419777 +trainer/policy/std Std 0.0197675 +trainer/policy/std Max 0.438713 +trainer/policy/std Min 0.388803 +trainer/Advantage Weights Mean 1.37599 +trainer/Advantage Weights Std 7.56419 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.93151e-12 +trainer/Advantage Score Mean -0.45115 +trainer/Advantage Score Std 0.537832 +trainer/Advantage Score Max 0.53096 +trainer/Advantage Score Min -2.6262 +trainer/V1 Predictions Mean -72.9455 +trainer/V1 Predictions Std 18.9779 +trainer/V1 Predictions Max 0.200904 +trainer/V1 Predictions Min -86.3916 +trainer/VF Loss 0.0530089 +expl/num steps total 503000 +expl/num paths total 624 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0798878 +expl/Actions Std 0.83214 +expl/Actions Max 2.44201 +expl/Actions Min -2.38247 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 479457 +eval/num paths total 506 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00774006 +eval/Actions Std 0.733332 +eval/Actions Max 0.999382 +eval/Actions Min -0.999562 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.37698e-06 +time/evaluation sampling (s) 4.51667 +time/exploration sampling (s) 6.75087 +time/logging (s) 0.00931586 +time/saving (s) 0.0153858 +time/training (s) 20.1938 +time/epoch (s) 31.4861 +time/total (s) 11138.8 +Epoch -498 +------------------------------ ---------------- +2022-05-15 21:08:37.995562 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -497 finished +------------------------------ ---------------- +epoch -497 +replay_buffer/size 999047 +trainer/num train calls 504000 +trainer/QF1 Loss 1.71285 +trainer/QF2 Loss 1.78547 +trainer/Policy Loss 8.18566 +trainer/Q1 Predictions Mean -73.4326 +trainer/Q1 Predictions Std 18.8064 +trainer/Q1 Predictions Max -0.932571 +trainer/Q1 Predictions Min -86.8327 +trainer/Q2 Predictions Mean -73.3671 +trainer/Q2 Predictions Std 18.8219 +trainer/Q2 Predictions Max -1.10538 +trainer/Q2 Predictions Min -86.6912 +trainer/Q Targets Mean -72.9694 +trainer/Q Targets Std 18.6802 +trainer/Q Targets Max -1.28936 +trainer/Q Targets Min -86.6524 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0323793 +trainer/policy/mean Std 0.721943 +trainer/policy/mean Max 0.998132 +trainer/policy/mean Min -0.998358 +trainer/policy/std Mean 0.418499 +trainer/policy/std Std 0.0213721 +trainer/policy/std Max 0.440743 +trainer/policy/std Min 0.384965 +trainer/Advantage Weights Mean 2.69108 +trainer/Advantage Weights Std 13.6749 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.15684e-20 +trainer/Advantage Score Mean -0.465849 +trainer/Advantage Score Std 0.654072 +trainer/Advantage Score Max 1.22603 +trainer/Advantage Score Min -4.40836 +trainer/V1 Predictions Mean -72.7186 +trainer/V1 Predictions Std 19.0368 +trainer/V1 Predictions Max -0.266942 +trainer/V1 Predictions Min -86.4746 +trainer/VF Loss 0.0762926 +expl/num steps total 504000 +expl/num paths total 625 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0279086 +expl/Actions Std 0.876431 +expl/Actions Max 2.37683 +expl/Actions Min -2.52191 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 480457 +eval/num paths total 507 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0281479 +eval/Actions Std 0.704836 +eval/Actions Max 0.99901 +eval/Actions Min -0.999986 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02911e-05 +time/evaluation sampling (s) 4.88081 +time/exploration sampling (s) 7.8479 +time/logging (s) 0.0117321 +time/saving (s) 0.018356 +time/training (s) 18.977 +time/epoch (s) 31.7358 +time/total (s) 11170.5 +Epoch -497 +------------------------------ ---------------- +2022-05-15 21:09:10.226361 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -496 finished +------------------------------ ---------------- +epoch -496 +replay_buffer/size 999047 +trainer/num train calls 505000 +trainer/QF1 Loss 0.671408 +trainer/QF2 Loss 0.758859 +trainer/Policy Loss 14.7855 +trainer/Q1 Predictions Mean -74.4364 +trainer/Q1 Predictions Std 15.8509 +trainer/Q1 Predictions Max -1.00768 +trainer/Q1 Predictions Min -87.0734 +trainer/Q2 Predictions Mean -74.4438 +trainer/Q2 Predictions Std 15.8044 +trainer/Q2 Predictions Max -1.42205 +trainer/Q2 Predictions Min -87.4779 +trainer/Q Targets Mean -74.583 +trainer/Q Targets Std 15.8644 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4969 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.001993 +trainer/policy/mean Std 0.728964 +trainer/policy/mean Max 0.998809 +trainer/policy/mean Min -0.999435 +trainer/policy/std Mean 0.417776 +trainer/policy/std Std 0.02032 +trainer/policy/std Max 0.439176 +trainer/policy/std Min 0.38538 +trainer/Advantage Weights Mean 4.28496 +trainer/Advantage Weights Std 17.4542 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.56531e-18 +trainer/Advantage Score Mean -0.29237 +trainer/Advantage Score Std 0.487165 +trainer/Advantage Score Max 1.70193 +trainer/Advantage Score Min -4.01753 +trainer/V1 Predictions Mean -74.4127 +trainer/V1 Predictions Std 15.7868 +trainer/V1 Predictions Max -0.713109 +trainer/V1 Predictions Min -87.3718 +trainer/VF Loss 0.0608161 +expl/num steps total 505000 +expl/num paths total 627 +expl/path length Mean 500 +expl/path length Std 255 +expl/path length Max 755 +expl/path length Min 245 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0314378 +expl/Actions Std 0.83274 +expl/Actions Max 2.81721 +expl/Actions Min -2.46328 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 481044 +eval/num paths total 508 +eval/path length Mean 587 +eval/path length Std 0 +eval/path length Max 587 +eval/path length Min 587 +eval/Rewards Mean 0.00170358 +eval/Rewards Std 0.0412392 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0348895 +eval/Actions Std 0.744363 +eval/Actions Max 0.999479 +eval/Actions Min -0.999201 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.28266e-05 +time/evaluation sampling (s) 5.63983 +time/exploration sampling (s) 7.43469 +time/logging (s) 0.00974278 +time/saving (s) 0.0152003 +time/training (s) 19.1144 +time/epoch (s) 32.2139 +time/total (s) 11202.7 +Epoch -496 +------------------------------ ---------------- +2022-05-15 21:09:42.379880 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -495 finished +------------------------------ ---------------- +epoch -495 +replay_buffer/size 999047 +trainer/num train calls 506000 +trainer/QF1 Loss 6.30571 +trainer/QF2 Loss 6.17769 +trainer/Policy Loss 39.6198 +trainer/Q1 Predictions Mean -74.8692 +trainer/Q1 Predictions Std 15.8497 +trainer/Q1 Predictions Max -0.750393 +trainer/Q1 Predictions Min -86.7543 +trainer/Q2 Predictions Mean -74.8363 +trainer/Q2 Predictions Std 15.8918 +trainer/Q2 Predictions Max -0.182141 +trainer/Q2 Predictions Min -86.8692 +trainer/Q Targets Mean -74.7883 +trainer/Q Targets Std 15.9656 +trainer/Q Targets Max 0.3289 +trainer/Q Targets Min -86.7555 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0313284 +trainer/policy/mean Std 0.732783 +trainer/policy/mean Max 0.999222 +trainer/policy/mean Min -0.998561 +trainer/policy/std Mean 0.419829 +trainer/policy/std Std 0.0206954 +trainer/policy/std Max 0.443645 +trainer/policy/std Min 0.386685 +trainer/Advantage Weights Mean 8.31916 +trainer/Advantage Weights Std 21.923 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.38013e-14 +trainer/Advantage Score Mean -0.167022 +trainer/Advantage Score Std 0.633998 +trainer/Advantage Score Max 1.27885 +trainer/Advantage Score Min -3.1369 +trainer/V1 Predictions Mean -74.6327 +trainer/V1 Predictions Std 16.1509 +trainer/V1 Predictions Max 0.102084 +trainer/V1 Predictions Min -86.6341 +trainer/VF Loss 0.0830153 +expl/num steps total 506000 +expl/num paths total 628 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.144783 +expl/Actions Std 0.827601 +expl/Actions Max 2.62132 +expl/Actions Min -2.16447 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 481512 +eval/num paths total 509 +eval/path length Mean 468 +eval/path length Std 0 +eval/path length Max 468 +eval/path length Min 468 +eval/Rewards Mean 0.00213675 +eval/Rewards Std 0.0461756 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0221035 +eval/Actions Std 0.741093 +eval/Actions Max 0.999506 +eval/Actions Min -0.999605 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.39979e-06 +time/evaluation sampling (s) 5.52994 +time/exploration sampling (s) 7.02224 +time/logging (s) 0.00909791 +time/saving (s) 0.0139465 +time/training (s) 19.5628 +time/epoch (s) 32.138 +time/total (s) 11234.9 +Epoch -495 +------------------------------ ---------------- +2022-05-15 21:10:13.365549 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -494 finished +------------------------------ ---------------- +epoch -494 +replay_buffer/size 999047 +trainer/num train calls 507000 +trainer/QF1 Loss 1.05202 +trainer/QF2 Loss 1.09071 +trainer/Policy Loss 2.97598 +trainer/Q1 Predictions Mean -73.36 +trainer/Q1 Predictions Std 17.4213 +trainer/Q1 Predictions Max -1.09167 +trainer/Q1 Predictions Min -87.2747 +trainer/Q2 Predictions Mean -73.394 +trainer/Q2 Predictions Std 17.3654 +trainer/Q2 Predictions Max -0.870044 +trainer/Q2 Predictions Min -87.179 +trainer/Q Targets Mean -72.9761 +trainer/Q Targets Std 17.3541 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.2353 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.017377 +trainer/policy/mean Std 0.718143 +trainer/policy/mean Max 0.998998 +trainer/policy/mean Min -0.998117 +trainer/policy/std Mean 0.42053 +trainer/policy/std Std 0.0213257 +trainer/policy/std Max 0.445192 +trainer/policy/std Min 0.385374 +trainer/Advantage Weights Mean 0.840711 +trainer/Advantage Weights Std 7.29112 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.93199e-15 +trainer/Advantage Score Mean -0.717629 +trainer/Advantage Score Std 0.547387 +trainer/Advantage Score Max 0.848473 +trainer/Advantage Score Min -3.34631 +trainer/V1 Predictions Mean -72.7102 +trainer/V1 Predictions Std 17.5337 +trainer/V1 Predictions Max -0.80275 +trainer/V1 Predictions Min -87.498 +trainer/VF Loss 0.0847812 +expl/num steps total 507000 +expl/num paths total 630 +expl/path length Mean 500 +expl/path length Std 218 +expl/path length Max 718 +expl/path length Min 282 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0155737 +expl/Actions Std 0.8299 +expl/Actions Max 2.26576 +expl/Actions Min -2.25333 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 482512 +eval/num paths total 510 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.102813 +eval/Actions Std 0.716176 +eval/Actions Max 0.999563 +eval/Actions Min -0.999597 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.28262e-05 +time/evaluation sampling (s) 5.18901 +time/exploration sampling (s) 6.45257 +time/logging (s) 0.010835 +time/saving (s) 0.015666 +time/training (s) 19.3048 +time/epoch (s) 30.9729 +time/total (s) 11265.8 +Epoch -494 +------------------------------ ---------------- +2022-05-15 21:10:45.496137 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -493 finished +------------------------------ ---------------- +epoch -493 +replay_buffer/size 999047 +trainer/num train calls 508000 +trainer/QF1 Loss 0.795524 +trainer/QF2 Loss 0.917759 +trainer/Policy Loss 16.069 +trainer/Q1 Predictions Mean -72.7932 +trainer/Q1 Predictions Std 19.2424 +trainer/Q1 Predictions Max -1.04527 +trainer/Q1 Predictions Min -88.2918 +trainer/Q2 Predictions Mean -72.8912 +trainer/Q2 Predictions Std 19.1753 +trainer/Q2 Predictions Max -0.72777 +trainer/Q2 Predictions Min -87.942 +trainer/Q Targets Mean -72.33 +trainer/Q Targets Std 19.3067 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9749 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00499601 +trainer/policy/mean Std 0.725942 +trainer/policy/mean Max 0.99921 +trainer/policy/mean Min -0.998433 +trainer/policy/std Mean 0.419387 +trainer/policy/std Std 0.0182962 +trainer/policy/std Max 0.440236 +trainer/policy/std Min 0.389241 +trainer/Advantage Weights Mean 2.34548 +trainer/Advantage Weights Std 13.0783 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.01419e-11 +trainer/Advantage Score Mean -0.483308 +trainer/Advantage Score Std 0.491661 +trainer/Advantage Score Max 1.88031 +trainer/Advantage Score Min -2.46282 +trainer/V1 Predictions Mean -72.1416 +trainer/V1 Predictions Std 19.2467 +trainer/V1 Predictions Max -0.0524109 +trainer/V1 Predictions Min -86.759 +trainer/VF Loss 0.067343 +expl/num steps total 508000 +expl/num paths total 632 +expl/path length Mean 500 +expl/path length Std 459 +expl/path length Max 959 +expl/path length Min 41 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.058677 +expl/Actions Std 0.831832 +expl/Actions Max 2.33181 +expl/Actions Min -2.23659 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 483512 +eval/num paths total 511 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00889218 +eval/Actions Std 0.713725 +eval/Actions Max 0.999533 +eval/Actions Min -0.999185 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.1977e-06 +time/evaluation sampling (s) 4.70619 +time/exploration sampling (s) 7.10581 +time/logging (s) 0.0125196 +time/saving (s) 0.0169267 +time/training (s) 20.277 +time/epoch (s) 32.1185 +time/total (s) 11298 +Epoch -493 +------------------------------ ---------------- +2022-05-15 21:11:16.250064 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -492 finished +------------------------------ ---------------- +epoch -492 +replay_buffer/size 999047 +trainer/num train calls 509000 +trainer/QF1 Loss 0.880234 +trainer/QF2 Loss 0.92499 +trainer/Policy Loss 14.5838 +trainer/Q1 Predictions Mean -72.1967 +trainer/Q1 Predictions Std 19.1963 +trainer/Q1 Predictions Max -0.486027 +trainer/Q1 Predictions Min -87.0255 +trainer/Q2 Predictions Mean -72.2536 +trainer/Q2 Predictions Std 19.2143 +trainer/Q2 Predictions Max 0.429654 +trainer/Q2 Predictions Min -87.119 +trainer/Q Targets Mean -72.3458 +trainer/Q Targets Std 19.421 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4553 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00133867 +trainer/policy/mean Std 0.72295 +trainer/policy/mean Max 0.999241 +trainer/policy/mean Min -0.999767 +trainer/policy/std Mean 0.419037 +trainer/policy/std Std 0.0190567 +trainer/policy/std Max 0.440125 +trainer/policy/std Min 0.388623 +trainer/Advantage Weights Mean 3.88842 +trainer/Advantage Weights Std 17.5776 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.96291e-20 +trainer/Advantage Score Mean -0.437362 +trainer/Advantage Score Std 0.648048 +trainer/Advantage Score Max 1.46657 +trainer/Advantage Score Min -4.53773 +trainer/V1 Predictions Mean -72.1035 +trainer/V1 Predictions Std 19.3669 +trainer/V1 Predictions Max -0.0398288 +trainer/V1 Predictions Min -87.367 +trainer/VF Loss 0.0799316 +expl/num steps total 509000 +expl/num paths total 633 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0586494 +expl/Actions Std 0.818159 +expl/Actions Max 2.28129 +expl/Actions Min -2.22709 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 484512 +eval/num paths total 512 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.214388 +eval/Actions Std 0.612461 +eval/Actions Max 0.999556 +eval/Actions Min -0.999603 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.35001e-06 +time/evaluation sampling (s) 4.80986 +time/exploration sampling (s) 6.41775 +time/logging (s) 0.0119113 +time/saving (s) 0.0169216 +time/training (s) 19.484 +time/epoch (s) 30.7404 +time/total (s) 11328.7 +Epoch -492 +------------------------------ ---------------- +2022-05-15 21:11:48.924044 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -491 finished +------------------------------ ---------------- +epoch -491 +replay_buffer/size 999047 +trainer/num train calls 510000 +trainer/QF1 Loss 0.6129 +trainer/QF2 Loss 0.618768 +trainer/Policy Loss 24.1998 +trainer/Q1 Predictions Mean -73.4594 +trainer/Q1 Predictions Std 18.5401 +trainer/Q1 Predictions Max -1.81844 +trainer/Q1 Predictions Min -87.4093 +trainer/Q2 Predictions Mean -73.4983 +trainer/Q2 Predictions Std 18.5887 +trainer/Q2 Predictions Max -1.15827 +trainer/Q2 Predictions Min -87.9232 +trainer/Q Targets Mean -73.1952 +trainer/Q Targets Std 18.5235 +trainer/Q Targets Max -2.63757 +trainer/Q Targets Min -87.2539 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0280316 +trainer/policy/mean Std 0.7222 +trainer/policy/mean Max 0.999571 +trainer/policy/mean Min -0.998036 +trainer/policy/std Mean 0.421041 +trainer/policy/std Std 0.0191982 +trainer/policy/std Max 0.442146 +trainer/policy/std Min 0.391026 +trainer/Advantage Weights Mean 3.35835 +trainer/Advantage Weights Std 15.3282 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.74631e-23 +trainer/Advantage Score Mean -0.463206 +trainer/Advantage Score Std 0.642552 +trainer/Advantage Score Max 0.927902 +trainer/Advantage Score Min -5.2402 +trainer/V1 Predictions Mean -72.8927 +trainer/V1 Predictions Std 18.7663 +trainer/V1 Predictions Max -1.03104 +trainer/V1 Predictions Min -87.1799 +trainer/VF Loss 0.0720518 +expl/num steps total 510000 +expl/num paths total 635 +expl/path length Mean 500 +expl/path length Std 145 +expl/path length Max 645 +expl/path length Min 355 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0103607 +expl/Actions Std 0.840665 +expl/Actions Max 2.28239 +expl/Actions Min -2.09309 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 485512 +eval/num paths total 513 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.132713 +eval/Actions Std 0.645316 +eval/Actions Max 0.999142 +eval/Actions Min -0.998715 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.61086e-05 +time/evaluation sampling (s) 5.31661 +time/exploration sampling (s) 7.39234 +time/logging (s) 0.0124597 +time/saving (s) 0.0177791 +time/training (s) 19.9226 +time/epoch (s) 32.6618 +time/total (s) 11361.4 +Epoch -491 +------------------------------ ---------------- +2022-05-15 21:12:21.595539 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -490 finished +------------------------------ ---------------- +epoch -490 +replay_buffer/size 999047 +trainer/num train calls 511000 +trainer/QF1 Loss 0.808503 +trainer/QF2 Loss 0.711335 +trainer/Policy Loss 21.7558 +trainer/Q1 Predictions Mean -74.1946 +trainer/Q1 Predictions Std 16.9641 +trainer/Q1 Predictions Max -2.06394 +trainer/Q1 Predictions Min -87.8611 +trainer/Q2 Predictions Mean -74.1941 +trainer/Q2 Predictions Std 16.9389 +trainer/Q2 Predictions Max -2.82189 +trainer/Q2 Predictions Min -88.0734 +trainer/Q Targets Mean -74.3252 +trainer/Q Targets Std 16.6848 +trainer/Q Targets Max -1.80366 +trainer/Q Targets Min -87.7317 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00353413 +trainer/policy/mean Std 0.714299 +trainer/policy/mean Max 0.999564 +trainer/policy/mean Min -0.999536 +trainer/policy/std Mean 0.41899 +trainer/policy/std Std 0.019771 +trainer/policy/std Max 0.441456 +trainer/policy/std Min 0.388942 +trainer/Advantage Weights Mean 6.05786 +trainer/Advantage Weights Std 21.1558 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.02293e-13 +trainer/Advantage Score Mean -0.251889 +trainer/Advantage Score Std 0.534736 +trainer/Advantage Score Max 2.26987 +trainer/Advantage Score Min -2.92291 +trainer/V1 Predictions Mean -74.0475 +trainer/V1 Predictions Std 17.0519 +trainer/V1 Predictions Max -2.99879 +trainer/V1 Predictions Min -88.1555 +trainer/VF Loss 0.0789462 +expl/num steps total 511000 +expl/num paths total 636 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.136847 +expl/Actions Std 0.840522 +expl/Actions Max 2.57443 +expl/Actions Min -2.35102 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 486512 +eval/num paths total 514 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.177123 +eval/Actions Std 0.661348 +eval/Actions Max 0.999491 +eval/Actions Min -0.998773 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.64897e-06 +time/evaluation sampling (s) 5.42742 +time/exploration sampling (s) 7.31188 +time/logging (s) 0.0111304 +time/saving (s) 0.0150931 +time/training (s) 19.8884 +time/epoch (s) 32.6539 +time/total (s) 11394 +Epoch -490 +------------------------------ ---------------- +2022-05-15 21:12:52.845477 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -489 finished +------------------------------ ---------------- +epoch -489 +replay_buffer/size 999047 +trainer/num train calls 512000 +trainer/QF1 Loss 1.52168 +trainer/QF2 Loss 1.49508 +trainer/Policy Loss 2.87829 +trainer/Q1 Predictions Mean -72.7043 +trainer/Q1 Predictions Std 17.2963 +trainer/Q1 Predictions Max -1.11582 +trainer/Q1 Predictions Min -87.6773 +trainer/Q2 Predictions Mean -72.7079 +trainer/Q2 Predictions Std 17.158 +trainer/Q2 Predictions Max -2.02802 +trainer/Q2 Predictions Min -86.9682 +trainer/Q Targets Mean -72.2573 +trainer/Q Targets Std 17.4734 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3621 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00530312 +trainer/policy/mean Std 0.717779 +trainer/policy/mean Max 0.998997 +trainer/policy/mean Min -0.998639 +trainer/policy/std Mean 0.419502 +trainer/policy/std Std 0.0204945 +trainer/policy/std Max 0.439431 +trainer/policy/std Min 0.386557 +trainer/Advantage Weights Mean 1.27033 +trainer/Advantage Weights Std 10.3191 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.38661e-19 +trainer/Advantage Score Mean -0.810619 +trainer/Advantage Score Std 0.678409 +trainer/Advantage Score Max 0.80719 +trainer/Advantage Score Min -4.18949 +trainer/V1 Predictions Mean -71.9793 +trainer/V1 Predictions Std 17.6094 +trainer/V1 Predictions Max -1.03002 +trainer/V1 Predictions Min -86.8857 +trainer/VF Loss 0.116354 +expl/num steps total 512000 +expl/num paths total 638 +expl/path length Mean 500 +expl/path length Std 125 +expl/path length Max 625 +expl/path length Min 375 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0230196 +expl/Actions Std 0.838987 +expl/Actions Max 2.34008 +expl/Actions Min -2.25754 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 487512 +eval/num paths total 515 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.146919 +eval/Actions Std 0.798058 +eval/Actions Max 0.999666 +eval/Actions Min -0.999329 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.01586e-06 +time/evaluation sampling (s) 4.93253 +time/exploration sampling (s) 6.74883 +time/logging (s) 0.0125164 +time/saving (s) 0.0184656 +time/training (s) 19.5236 +time/epoch (s) 31.236 +time/total (s) 11425.3 +Epoch -489 +------------------------------ ---------------- +2022-05-15 21:13:25.812565 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -488 finished +------------------------------ ---------------- +epoch -488 +replay_buffer/size 999047 +trainer/num train calls 513000 +trainer/QF1 Loss 0.59674 +trainer/QF2 Loss 0.535102 +trainer/Policy Loss 17.0511 +trainer/Q1 Predictions Mean -75.3158 +trainer/Q1 Predictions Std 16.027 +trainer/Q1 Predictions Max -0.438541 +trainer/Q1 Predictions Min -88.5645 +trainer/Q2 Predictions Mean -75.2769 +trainer/Q2 Predictions Std 16.0309 +trainer/Q2 Predictions Max 0.0551396 +trainer/Q2 Predictions Min -88.0631 +trainer/Q Targets Mean -75.0186 +trainer/Q Targets Std 16.0847 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4525 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00900484 +trainer/policy/mean Std 0.721073 +trainer/policy/mean Max 0.999355 +trainer/policy/mean Min -0.999339 +trainer/policy/std Mean 0.419777 +trainer/policy/std Std 0.0195568 +trainer/policy/std Max 0.438128 +trainer/policy/std Min 0.389338 +trainer/Advantage Weights Mean 3.03373 +trainer/Advantage Weights Std 16.329 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.70649e-18 +trainer/Advantage Score Mean -0.515486 +trainer/Advantage Score Std 0.604022 +trainer/Advantage Score Max 1.33719 +trainer/Advantage Score Min -3.94045 +trainer/V1 Predictions Mean -74.7895 +trainer/V1 Predictions Std 16.2032 +trainer/V1 Predictions Max -1.31376 +trainer/V1 Predictions Min -87.7789 +trainer/VF Loss 0.0827569 +expl/num steps total 513000 +expl/num paths total 639 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0427728 +expl/Actions Std 0.829172 +expl/Actions Max 2.32324 +expl/Actions Min -2.18658 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 488458 +eval/num paths total 516 +eval/path length Mean 946 +eval/path length Std 0 +eval/path length Max 946 +eval/path length Min 946 +eval/Rewards Mean 0.00105708 +eval/Rewards Std 0.0324956 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0526876 +eval/Actions Std 0.734672 +eval/Actions Max 0.999618 +eval/Actions Min -0.999739 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.01938e-05 +time/evaluation sampling (s) 5.65635 +time/exploration sampling (s) 7.67774 +time/logging (s) 0.00916956 +time/saving (s) 0.0167595 +time/training (s) 19.5873 +time/epoch (s) 32.9473 +time/total (s) 11458.2 +Epoch -488 +------------------------------ ---------------- +2022-05-15 21:13:57.349501 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -487 finished +------------------------------ ---------------- +epoch -487 +replay_buffer/size 999047 +trainer/num train calls 514000 +trainer/QF1 Loss 0.496338 +trainer/QF2 Loss 0.537629 +trainer/Policy Loss 15.3294 +trainer/Q1 Predictions Mean -73.9872 +trainer/Q1 Predictions Std 18.0234 +trainer/Q1 Predictions Max -1.83003 +trainer/Q1 Predictions Min -87.7127 +trainer/Q2 Predictions Mean -73.9556 +trainer/Q2 Predictions Std 18.04 +trainer/Q2 Predictions Max -1.57586 +trainer/Q2 Predictions Min -87.5291 +trainer/Q Targets Mean -74.0543 +trainer/Q Targets Std 17.8442 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3998 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0107445 +trainer/policy/mean Std 0.727999 +trainer/policy/mean Max 0.997281 +trainer/policy/mean Min -0.998269 +trainer/policy/std Mean 0.421252 +trainer/policy/std Std 0.0198935 +trainer/policy/std Max 0.440802 +trainer/policy/std Min 0.390496 +trainer/Advantage Weights Mean 4.83005 +trainer/Advantage Weights Std 19.3318 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.43578e-09 +trainer/Advantage Score Mean -0.308962 +trainer/Advantage Score Std 0.480881 +trainer/Advantage Score Max 1.92167 +trainer/Advantage Score Min -2.03616 +trainer/V1 Predictions Mean -73.8787 +trainer/V1 Predictions Std 17.8536 +trainer/V1 Predictions Max -1.2635 +trainer/V1 Predictions Min -87.2762 +trainer/VF Loss 0.0797516 +expl/num steps total 514000 +expl/num paths total 641 +expl/path length Mean 500 +expl/path length Std 393 +expl/path length Max 893 +expl/path length Min 107 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0717852 +expl/Actions Std 0.815061 +expl/Actions Max 2.43436 +expl/Actions Min -2.10052 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 489377 +eval/num paths total 517 +eval/path length Mean 919 +eval/path length Std 0 +eval/path length Max 919 +eval/path length Min 919 +eval/Rewards Mean 0.00108814 +eval/Rewards Std 0.032969 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0158047 +eval/Actions Std 0.742522 +eval/Actions Max 0.999127 +eval/Actions Min -0.999702 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.45917e-06 +time/evaluation sampling (s) 5.21819 +time/exploration sampling (s) 7.09675 +time/logging (s) 0.0106775 +time/saving (s) 0.0156291 +time/training (s) 19.1833 +time/epoch (s) 31.5246 +time/total (s) 11489.8 +Epoch -487 +------------------------------ ---------------- +2022-05-15 21:14:29.547355 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -486 finished +------------------------------ ---------------- +epoch -486 +replay_buffer/size 999047 +trainer/num train calls 515000 +trainer/QF1 Loss 0.882966 +trainer/QF2 Loss 0.91799 +trainer/Policy Loss 16.6169 +trainer/Q1 Predictions Mean -72.8186 +trainer/Q1 Predictions Std 19.0133 +trainer/Q1 Predictions Max -2.80938 +trainer/Q1 Predictions Min -86.9858 +trainer/Q2 Predictions Mean -72.859 +trainer/Q2 Predictions Std 18.9779 +trainer/Q2 Predictions Max -2.49211 +trainer/Q2 Predictions Min -87.3841 +trainer/Q Targets Mean -73.0583 +trainer/Q Targets Std 19.3616 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7336 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000335676 +trainer/policy/mean Std 0.726531 +trainer/policy/mean Max 0.999309 +trainer/policy/mean Min -0.999188 +trainer/policy/std Mean 0.422031 +trainer/policy/std Std 0.0205465 +trainer/policy/std Max 0.440968 +trainer/policy/std Min 0.391716 +trainer/Advantage Weights Mean 4.77227 +trainer/Advantage Weights Std 17.7303 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.54348e-16 +trainer/Advantage Score Mean -0.377977 +trainer/Advantage Score Std 0.648159 +trainer/Advantage Score Max 0.980255 +trainer/Advantage Score Min -3.51287 +trainer/V1 Predictions Mean -72.8437 +trainer/V1 Predictions Std 19.2577 +trainer/V1 Predictions Max -1.8763 +trainer/V1 Predictions Min -87.3577 +trainer/VF Loss 0.0704536 +expl/num steps total 515000 +expl/num paths total 643 +expl/path length Mean 500 +expl/path length Std 232 +expl/path length Max 732 +expl/path length Min 268 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0220714 +expl/Actions Std 0.825615 +expl/Actions Max 2.55984 +expl/Actions Min -2.17497 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 490353 +eval/num paths total 518 +eval/path length Mean 976 +eval/path length Std 0 +eval/path length Max 976 +eval/path length Min 976 +eval/Rewards Mean 0.00102459 +eval/Rewards Std 0.0319928 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0561985 +eval/Actions Std 0.72221 +eval/Actions Max 0.999625 +eval/Actions Min -0.999801 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.43242e-05 +time/evaluation sampling (s) 5.44353 +time/exploration sampling (s) 7.10696 +time/logging (s) 0.0136449 +time/saving (s) 0.0178263 +time/training (s) 19.6045 +time/epoch (s) 32.1865 +time/total (s) 11522 +Epoch -486 +------------------------------ ---------------- +2022-05-15 21:15:01.189767 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -485 finished +------------------------------ ---------------- +epoch -485 +replay_buffer/size 999047 +trainer/num train calls 516000 +trainer/QF1 Loss 1.01632 +trainer/QF2 Loss 0.926411 +trainer/Policy Loss 15.7242 +trainer/Q1 Predictions Mean -74.1415 +trainer/Q1 Predictions Std 17.2597 +trainer/Q1 Predictions Max -2.36815 +trainer/Q1 Predictions Min -87.139 +trainer/Q2 Predictions Mean -74.1723 +trainer/Q2 Predictions Std 17.3074 +trainer/Q2 Predictions Max -1.36218 +trainer/Q2 Predictions Min -87.123 +trainer/Q Targets Mean -74.2279 +trainer/Q Targets Std 17.1755 +trainer/Q Targets Max -1.12716 +trainer/Q Targets Min -86.9694 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0231262 +trainer/policy/mean Std 0.718813 +trainer/policy/mean Max 0.998171 +trainer/policy/mean Min -0.999752 +trainer/policy/std Mean 0.420927 +trainer/policy/std Std 0.0204324 +trainer/policy/std Max 0.442061 +trainer/policy/std Min 0.389502 +trainer/Advantage Weights Mean 3.94965 +trainer/Advantage Weights Std 16.5598 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.39258e-24 +trainer/Advantage Score Mean -0.363214 +trainer/Advantage Score Std 0.593622 +trainer/Advantage Score Max 0.896347 +trainer/Advantage Score Min -5.40404 +trainer/V1 Predictions Mean -73.8679 +trainer/V1 Predictions Std 17.567 +trainer/V1 Predictions Max -1.85654 +trainer/V1 Predictions Min -86.8387 +trainer/VF Loss 0.0621368 +expl/num steps total 516000 +expl/num paths total 644 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0453971 +expl/Actions Std 0.835506 +expl/Actions Max 2.2438 +expl/Actions Min -2.43109 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 491353 +eval/num paths total 519 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0239618 +eval/Actions Std 0.704276 +eval/Actions Max 0.998817 +eval/Actions Min -0.999164 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.13863e-05 +time/evaluation sampling (s) 5.37648 +time/exploration sampling (s) 6.8183 +time/logging (s) 0.0120876 +time/saving (s) 0.0180841 +time/training (s) 19.4022 +time/epoch (s) 31.6272 +time/total (s) 11553.6 +Epoch -485 +------------------------------ ---------------- +2022-05-15 21:15:33.531164 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -484 finished +------------------------------ ---------------- +epoch -484 +replay_buffer/size 999047 +trainer/num train calls 517000 +trainer/QF1 Loss 0.549246 +trainer/QF2 Loss 0.643831 +trainer/Policy Loss 40.6044 +trainer/Q1 Predictions Mean -73.1198 +trainer/Q1 Predictions Std 17.9922 +trainer/Q1 Predictions Max -0.70018 +trainer/Q1 Predictions Min -87.1543 +trainer/Q2 Predictions Mean -73.2176 +trainer/Q2 Predictions Std 17.8952 +trainer/Q2 Predictions Max 0.0611053 +trainer/Q2 Predictions Min -87.0665 +trainer/Q Targets Mean -73.256 +trainer/Q Targets Std 17.959 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.599 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0215022 +trainer/policy/mean Std 0.722556 +trainer/policy/mean Max 0.998899 +trainer/policy/mean Min -0.998237 +trainer/policy/std Mean 0.419819 +trainer/policy/std Std 0.0201933 +trainer/policy/std Max 0.440633 +trainer/policy/std Min 0.38644 +trainer/Advantage Weights Mean 8.28341 +trainer/Advantage Weights Std 24.408 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.82003e-29 +trainer/Advantage Score Mean -0.27796 +trainer/Advantage Score Std 0.746738 +trainer/Advantage Score Max 2.02828 +trainer/Advantage Score Min -6.57382 +trainer/V1 Predictions Mean -72.9976 +trainer/V1 Predictions Std 18.2306 +trainer/V1 Predictions Max 0.0673746 +trainer/V1 Predictions Min -87.3739 +trainer/VF Loss 0.11011 +expl/num steps total 517000 +expl/num paths total 645 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.322369 +expl/Actions Std 0.802337 +expl/Actions Max 2.33207 +expl/Actions Min -2.2864 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 492353 +eval/num paths total 520 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.168222 +eval/Actions Std 0.804458 +eval/Actions Max 0.999528 +eval/Actions Min -0.99946 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.21682e-05 +time/evaluation sampling (s) 5.36858 +time/exploration sampling (s) 7.03481 +time/logging (s) 0.00761216 +time/saving (s) 0.0114583 +time/training (s) 19.8985 +time/epoch (s) 32.3209 +time/total (s) 11585.9 +Epoch -484 +------------------------------ ---------------- +2022-05-15 21:16:06.187348 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -483 finished +------------------------------ ---------------- +epoch -483 +replay_buffer/size 999047 +trainer/num train calls 518000 +trainer/QF1 Loss 0.614534 +trainer/QF2 Loss 0.697743 +trainer/Policy Loss 12.1237 +trainer/Q1 Predictions Mean -74.6256 +trainer/Q1 Predictions Std 16.9622 +trainer/Q1 Predictions Max 0.294431 +trainer/Q1 Predictions Min -86.642 +trainer/Q2 Predictions Mean -74.5514 +trainer/Q2 Predictions Std 17.0033 +trainer/Q2 Predictions Max -0.530591 +trainer/Q2 Predictions Min -86.8009 +trainer/Q Targets Mean -74.5981 +trainer/Q Targets Std 16.9162 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9831 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0059973 +trainer/policy/mean Std 0.717887 +trainer/policy/mean Max 0.998572 +trainer/policy/mean Min -0.998321 +trainer/policy/std Mean 0.420442 +trainer/policy/std Std 0.0216482 +trainer/policy/std Max 0.443254 +trainer/policy/std Min 0.384735 +trainer/Advantage Weights Mean 2.85185 +trainer/Advantage Weights Std 13.4604 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.51238e-14 +trainer/Advantage Score Mean -0.461452 +trainer/Advantage Score Std 0.541834 +trainer/Advantage Score Max 1.67785 +trainer/Advantage Score Min -2.99836 +trainer/V1 Predictions Mean -74.3724 +trainer/V1 Predictions Std 16.8876 +trainer/V1 Predictions Max -0.0542937 +trainer/V1 Predictions Min -86.7568 +trainer/VF Loss 0.0722104 +expl/num steps total 518000 +expl/num paths total 646 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0218661 +expl/Actions Std 0.82693 +expl/Actions Max 2.46711 +expl/Actions Min -2.43601 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 493353 +eval/num paths total 521 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0423295 +eval/Actions Std 0.750149 +eval/Actions Max 0.999661 +eval/Actions Min -0.999818 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.11503e-05 +time/evaluation sampling (s) 5.88142 +time/exploration sampling (s) 7.28071 +time/logging (s) 0.0101213 +time/saving (s) 0.0167923 +time/training (s) 19.459 +time/epoch (s) 32.6481 +time/total (s) 11618.6 +Epoch -483 +------------------------------ ---------------- +2022-05-15 21:16:37.616213 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -482 finished +------------------------------ ---------------- +epoch -482 +replay_buffer/size 999047 +trainer/num train calls 519000 +trainer/QF1 Loss 0.736919 +trainer/QF2 Loss 0.669886 +trainer/Policy Loss 6.2917 +trainer/Q1 Predictions Mean -75.4352 +trainer/Q1 Predictions Std 15.5608 +trainer/Q1 Predictions Max -2.1304 +trainer/Q1 Predictions Min -85.9898 +trainer/Q2 Predictions Mean -75.4132 +trainer/Q2 Predictions Std 15.5674 +trainer/Q2 Predictions Max -2.01633 +trainer/Q2 Predictions Min -86.0139 +trainer/Q Targets Mean -75.5186 +trainer/Q Targets Std 15.8275 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1491 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0151376 +trainer/policy/mean Std 0.72605 +trainer/policy/mean Max 0.999429 +trainer/policy/mean Min -0.997094 +trainer/policy/std Mean 0.420577 +trainer/policy/std Std 0.0216681 +trainer/policy/std Max 0.442011 +trainer/policy/std Min 0.388199 +trainer/Advantage Weights Mean 1.87547 +trainer/Advantage Weights Std 8.9781 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.42191e-17 +trainer/Advantage Score Mean -0.343301 +trainer/Advantage Score Std 0.512442 +trainer/Advantage Score Max 1.08022 +trainer/Advantage Score Min -3.74535 +trainer/V1 Predictions Mean -75.2969 +trainer/V1 Predictions Std 15.8176 +trainer/V1 Predictions Max -1.37947 +trainer/V1 Predictions Min -86.0041 +trainer/VF Loss 0.0456265 +expl/num steps total 519000 +expl/num paths total 648 +expl/path length Mean 500 +expl/path length Std 392 +expl/path length Max 892 +expl/path length Min 108 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0460052 +expl/Actions Std 0.825644 +expl/Actions Max 2.30814 +expl/Actions Min -2.23764 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 494266 +eval/num paths total 522 +eval/path length Mean 913 +eval/path length Std 0 +eval/path length Max 913 +eval/path length Min 913 +eval/Rewards Mean 0.00109529 +eval/Rewards Std 0.033077 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0215052 +eval/Actions Std 0.725467 +eval/Actions Max 0.999729 +eval/Actions Min -0.999809 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.26618e-05 +time/evaluation sampling (s) 5.35006 +time/exploration sampling (s) 6.67976 +time/logging (s) 0.0117745 +time/saving (s) 0.0276136 +time/training (s) 19.3485 +time/epoch (s) 31.4178 +time/total (s) 11650 +Epoch -482 +------------------------------ ---------------- +2022-05-15 21:17:09.390201 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -481 finished +------------------------------ ---------------- +epoch -481 +replay_buffer/size 999047 +trainer/num train calls 520000 +trainer/QF1 Loss 12.8164 +trainer/QF2 Loss 12.9588 +trainer/Policy Loss 0.307132 +trainer/Q1 Predictions Mean -74.2552 +trainer/Q1 Predictions Std 17.3775 +trainer/Q1 Predictions Max -2.53205 +trainer/Q1 Predictions Min -86.5651 +trainer/Q2 Predictions Mean -74.2981 +trainer/Q2 Predictions Std 17.4158 +trainer/Q2 Predictions Max -1.96249 +trainer/Q2 Predictions Min -87.2463 +trainer/Q Targets Mean -73.985 +trainer/Q Targets Std 16.9641 +trainer/Q Targets Max -3.02418 +trainer/Q Targets Min -86.6532 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0163259 +trainer/policy/mean Std 0.70747 +trainer/policy/mean Max 0.999394 +trainer/policy/mean Min -0.999293 +trainer/policy/std Mean 0.418784 +trainer/policy/std Std 0.020173 +trainer/policy/std Max 0.439113 +trainer/policy/std Min 0.388555 +trainer/Advantage Weights Mean 0.0519302 +trainer/Advantage Weights Std 0.484718 +trainer/Advantage Weights Max 6.70598 +trainer/Advantage Weights Min 4.49253e-18 +trainer/Advantage Score Mean -0.950802 +trainer/Advantage Score Std 0.521298 +trainer/Advantage Score Max 0.1903 +trainer/Advantage Score Min -3.99441 +trainer/V1 Predictions Mean -73.5297 +trainer/V1 Predictions Std 17.5455 +trainer/V1 Predictions Max -0.855582 +trainer/V1 Predictions Min -86.4637 +trainer/VF Loss 0.117756 +expl/num steps total 520000 +expl/num paths total 649 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00950413 +expl/Actions Std 0.817363 +expl/Actions Max 2.31067 +expl/Actions Min -2.37725 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 495266 +eval/num paths total 523 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.14078 +eval/Actions Std 0.696073 +eval/Actions Max 0.999776 +eval/Actions Min -0.999201 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.11563e-05 +time/evaluation sampling (s) 4.99096 +time/exploration sampling (s) 7.19569 +time/logging (s) 0.0123733 +time/saving (s) 0.0141589 +time/training (s) 19.5426 +time/epoch (s) 31.7558 +time/total (s) 11681.8 +Epoch -481 +------------------------------ ---------------- +2022-05-15 21:17:41.552334 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -480 finished +------------------------------ ---------------- +epoch -480 +replay_buffer/size 999047 +trainer/num train calls 521000 +trainer/QF1 Loss 0.567038 +trainer/QF2 Loss 0.59329 +trainer/Policy Loss 9.28874 +trainer/Q1 Predictions Mean -72.7322 +trainer/Q1 Predictions Std 17.7278 +trainer/Q1 Predictions Max -1.28142 +trainer/Q1 Predictions Min -87.6105 +trainer/Q2 Predictions Mean -72.8061 +trainer/Q2 Predictions Std 17.6832 +trainer/Q2 Predictions Max -2.17428 +trainer/Q2 Predictions Min -87.4676 +trainer/Q Targets Mean -72.6768 +trainer/Q Targets Std 17.949 +trainer/Q Targets Max -0.879044 +trainer/Q Targets Min -86.768 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0047865 +trainer/policy/mean Std 0.723831 +trainer/policy/mean Max 0.999862 +trainer/policy/mean Min -0.999416 +trainer/policy/std Mean 0.41872 +trainer/policy/std Std 0.0203823 +trainer/policy/std Max 0.438406 +trainer/policy/std Min 0.385004 +trainer/Advantage Weights Mean 2.98935 +trainer/Advantage Weights Std 13.8817 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.12697e-17 +trainer/Advantage Score Mean -0.500574 +trainer/Advantage Score Std 0.678163 +trainer/Advantage Score Max 1.15099 +trainer/Advantage Score Min -3.77264 +trainer/V1 Predictions Mean -72.4531 +trainer/V1 Predictions Std 18.0936 +trainer/V1 Predictions Max -0.299382 +trainer/V1 Predictions Min -86.7897 +trainer/VF Loss 0.0841698 +expl/num steps total 521000 +expl/num paths total 650 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0157715 +expl/Actions Std 0.825337 +expl/Actions Max 2.36365 +expl/Actions Min -2.62526 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 495725 +eval/num paths total 524 +eval/path length Mean 459 +eval/path length Std 0 +eval/path length Max 459 +eval/path length Min 459 +eval/Rewards Mean 0.00217865 +eval/Rewards Std 0.0466251 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0282769 +eval/Actions Std 0.73801 +eval/Actions Max 0.999635 +eval/Actions Min -0.999455 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.97908e-06 +time/evaluation sampling (s) 5.5235 +time/exploration sampling (s) 7.0329 +time/logging (s) 0.00885623 +time/saving (s) 0.0157724 +time/training (s) 19.565 +time/epoch (s) 32.1461 +time/total (s) 11713.9 +Epoch -480 +------------------------------ ---------------- +2022-05-15 21:18:13.649781 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -479 finished +------------------------------ ---------------- +epoch -479 +replay_buffer/size 999047 +trainer/num train calls 522000 +trainer/QF1 Loss 0.617894 +trainer/QF2 Loss 0.562226 +trainer/Policy Loss 11.7954 +trainer/Q1 Predictions Mean -74.6224 +trainer/Q1 Predictions Std 16.7569 +trainer/Q1 Predictions Max -1.80154 +trainer/Q1 Predictions Min -88.2459 +trainer/Q2 Predictions Mean -74.5579 +trainer/Q2 Predictions Std 16.8004 +trainer/Q2 Predictions Max -1.75735 +trainer/Q2 Predictions Min -87.5991 +trainer/Q Targets Mean -74.3751 +trainer/Q Targets Std 16.717 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0445 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0154861 +trainer/policy/mean Std 0.725026 +trainer/policy/mean Max 0.998773 +trainer/policy/mean Min -0.997992 +trainer/policy/std Mean 0.419768 +trainer/policy/std Std 0.019671 +trainer/policy/std Max 0.439412 +trainer/policy/std Min 0.38851 +trainer/Advantage Weights Mean 4.06858 +trainer/Advantage Weights Std 16.8695 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.27318e-13 +trainer/Advantage Score Mean -0.341025 +trainer/Advantage Score Std 0.501383 +trainer/Advantage Score Max 1.05614 +trainer/Advantage Score Min -2.78206 +trainer/V1 Predictions Mean -74.1351 +trainer/V1 Predictions Std 16.8257 +trainer/V1 Predictions Max -1.75308 +trainer/V1 Predictions Min -87.2928 +trainer/VF Loss 0.0531979 +expl/num steps total 522000 +expl/num paths total 652 +expl/path length Mean 500 +expl/path length Std 460 +expl/path length Max 960 +expl/path length Min 40 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0459771 +expl/Actions Std 0.827737 +expl/Actions Max 2.53679 +expl/Actions Min -2.31969 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 496725 +eval/num paths total 525 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.217696 +eval/Actions Std 0.727695 +eval/Actions Max 0.999704 +eval/Actions Min -0.999571 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.852e-06 +time/evaluation sampling (s) 5.73903 +time/exploration sampling (s) 6.96455 +time/logging (s) 0.0114283 +time/saving (s) 0.0158868 +time/training (s) 19.3552 +time/epoch (s) 32.0861 +time/total (s) 11746 +Epoch -479 +------------------------------ ---------------- +2022-05-15 21:18:45.880959 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -478 finished +------------------------------ ---------------- +epoch -478 +replay_buffer/size 999047 +trainer/num train calls 523000 +trainer/QF1 Loss 0.75351 +trainer/QF2 Loss 0.680102 +trainer/Policy Loss 11.1782 +trainer/Q1 Predictions Mean -69.0522 +trainer/Q1 Predictions Std 22.884 +trainer/Q1 Predictions Max 0.340432 +trainer/Q1 Predictions Min -85.4117 +trainer/Q2 Predictions Mean -69.1085 +trainer/Q2 Predictions Std 22.8283 +trainer/Q2 Predictions Max -0.042122 +trainer/Q2 Predictions Min -85.6881 +trainer/Q Targets Mean -68.8542 +trainer/Q Targets Std 23.2323 +trainer/Q Targets Max 0.19467 +trainer/Q Targets Min -85.4646 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0109744 +trainer/policy/mean Std 0.718822 +trainer/policy/mean Max 0.998315 +trainer/policy/mean Min -0.998463 +trainer/policy/std Mean 0.418824 +trainer/policy/std Std 0.018839 +trainer/policy/std Max 0.438948 +trainer/policy/std Min 0.388161 +trainer/Advantage Weights Mean 2.84438 +trainer/Advantage Weights Std 14.5763 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.97568e-20 +trainer/Advantage Score Mean -0.593963 +trainer/Advantage Score Std 0.840677 +trainer/Advantage Score Max 1.65707 +trainer/Advantage Score Min -4.53708 +trainer/V1 Predictions Mean -68.5799 +trainer/V1 Predictions Std 23.2776 +trainer/V1 Predictions Max 0.806266 +trainer/V1 Predictions Min -85.3178 +trainer/VF Loss 0.125589 +expl/num steps total 523000 +expl/num paths total 654 +expl/path length Mean 500 +expl/path length Std 385 +expl/path length Max 885 +expl/path length Min 115 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0103277 +expl/Actions Std 0.821564 +expl/Actions Max 2.11777 +expl/Actions Min -2.44741 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 497483 +eval/num paths total 526 +eval/path length Mean 758 +eval/path length Std 0 +eval/path length Max 758 +eval/path length Min 758 +eval/Rewards Mean 0.00131926 +eval/Rewards Std 0.0362977 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0214343 +eval/Actions Std 0.733547 +eval/Actions Max 0.998701 +eval/Actions Min -0.999339 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.13088e-06 +time/evaluation sampling (s) 5.06391 +time/exploration sampling (s) 7.03808 +time/logging (s) 0.0108521 +time/saving (s) 0.0168443 +time/training (s) 20.0875 +time/epoch (s) 32.2172 +time/total (s) 11778.2 +Epoch -478 +------------------------------ ---------------- +2022-05-15 21:19:17.584978 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -477 finished +------------------------------ ---------------- +epoch -477 +replay_buffer/size 999047 +trainer/num train calls 524000 +trainer/QF1 Loss 0.736252 +trainer/QF2 Loss 0.709863 +trainer/Policy Loss 13.2462 +trainer/Q1 Predictions Mean -73.0085 +trainer/Q1 Predictions Std 18.8067 +trainer/Q1 Predictions Max -0.753723 +trainer/Q1 Predictions Min -86.5017 +trainer/Q2 Predictions Mean -73.0608 +trainer/Q2 Predictions Std 18.8206 +trainer/Q2 Predictions Max 0.460348 +trainer/Q2 Predictions Min -86.3307 +trainer/Q Targets Mean -72.8684 +trainer/Q Targets Std 18.8323 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9635 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00940388 +trainer/policy/mean Std 0.70882 +trainer/policy/mean Max 0.999649 +trainer/policy/mean Min -0.998328 +trainer/policy/std Mean 0.41838 +trainer/policy/std Std 0.0201799 +trainer/policy/std Max 0.439163 +trainer/policy/std Min 0.385294 +trainer/Advantage Weights Mean 3.84611 +trainer/Advantage Weights Std 17.0876 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.09173e-18 +trainer/Advantage Score Mean -0.491046 +trainer/Advantage Score Std 0.605899 +trainer/Advantage Score Max 0.823868 +trainer/Advantage Score Min -4.03178 +trainer/V1 Predictions Mean -72.6038 +trainer/V1 Predictions Std 18.9666 +trainer/V1 Predictions Max -0.446458 +trainer/V1 Predictions Min -85.8462 +trainer/VF Loss 0.072062 +expl/num steps total 524000 +expl/num paths total 655 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0256888 +expl/Actions Std 0.799317 +expl/Actions Max 2.25255 +expl/Actions Min -2.6016 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 498483 +eval/num paths total 527 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.169871 +eval/Actions Std 0.691365 +eval/Actions Max 0.997533 +eval/Actions Min -0.997623 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.01586e-06 +time/evaluation sampling (s) 5.6496 +time/exploration sampling (s) 6.89514 +time/logging (s) 0.0123977 +time/saving (s) 0.0180202 +time/training (s) 19.1164 +time/epoch (s) 31.6915 +time/total (s) 11809.9 +Epoch -477 +------------------------------ ---------------- +2022-05-15 21:19:49.467836 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -476 finished +------------------------------ ---------------- +epoch -476 +replay_buffer/size 999047 +trainer/num train calls 525000 +trainer/QF1 Loss 0.677959 +trainer/QF2 Loss 0.608112 +trainer/Policy Loss 24.2565 +trainer/Q1 Predictions Mean -73.827 +trainer/Q1 Predictions Std 18.6363 +trainer/Q1 Predictions Max 0.431502 +trainer/Q1 Predictions Min -86.5816 +trainer/Q2 Predictions Mean -73.812 +trainer/Q2 Predictions Std 18.7086 +trainer/Q2 Predictions Max 0.982042 +trainer/Q2 Predictions Min -86.5086 +trainer/Q Targets Mean -73.9956 +trainer/Q Targets Std 18.7008 +trainer/Q Targets Max 0.0293547 +trainer/Q Targets Min -86.493 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00843186 +trainer/policy/mean Std 0.717997 +trainer/policy/mean Max 0.997972 +trainer/policy/mean Min -0.997635 +trainer/policy/std Mean 0.418584 +trainer/policy/std Std 0.0205383 +trainer/policy/std Max 0.441186 +trainer/policy/std Min 0.382557 +trainer/Advantage Weights Mean 5.59357 +trainer/Advantage Weights Std 18.5481 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.37624e-17 +trainer/Advantage Score Mean -0.274121 +trainer/Advantage Score Std 0.617088 +trainer/Advantage Score Max 0.895048 +trainer/Advantage Score Min -3.82784 +trainer/V1 Predictions Mean -73.7308 +trainer/V1 Predictions Std 18.8549 +trainer/V1 Predictions Max 1.93424 +trainer/V1 Predictions Min -86.4904 +trainer/VF Loss 0.0623305 +expl/num steps total 525000 +expl/num paths total 656 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00895164 +expl/Actions Std 0.802504 +expl/Actions Max 2.53733 +expl/Actions Min -2.36369 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 498927 +eval/num paths total 528 +eval/path length Mean 444 +eval/path length Std 0 +eval/path length Max 444 +eval/path length Min 444 +eval/Rewards Mean 0.00225225 +eval/Rewards Std 0.0474044 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0287748 +eval/Actions Std 0.735678 +eval/Actions Max 0.999569 +eval/Actions Min -0.998452 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0909e-05 +time/evaluation sampling (s) 5.28805 +time/exploration sampling (s) 6.82543 +time/logging (s) 0.00928044 +time/saving (s) 0.0159537 +time/training (s) 19.7246 +time/epoch (s) 31.8633 +time/total (s) 11841.8 +Epoch -476 +------------------------------ ---------------- +2022-05-15 21:20:20.959027 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -475 finished +------------------------------ ---------------- +epoch -475 +replay_buffer/size 999047 +trainer/num train calls 526000 +trainer/QF1 Loss 2.47488 +trainer/QF2 Loss 2.45977 +trainer/Policy Loss 22.6047 +trainer/Q1 Predictions Mean -71.9971 +trainer/Q1 Predictions Std 20.2996 +trainer/Q1 Predictions Max 0.248924 +trainer/Q1 Predictions Min -86.5588 +trainer/Q2 Predictions Mean -72.0327 +trainer/Q2 Predictions Std 20.2151 +trainer/Q2 Predictions Max -0.517142 +trainer/Q2 Predictions Min -86.1511 +trainer/Q Targets Mean -71.9554 +trainer/Q Targets Std 20.4526 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8656 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0253445 +trainer/policy/mean Std 0.71837 +trainer/policy/mean Max 0.999281 +trainer/policy/mean Min -0.999761 +trainer/policy/std Mean 0.418464 +trainer/policy/std Std 0.0208943 +trainer/policy/std Max 0.440404 +trainer/policy/std Min 0.383673 +trainer/Advantage Weights Mean 5.3448 +trainer/Advantage Weights Std 17.9317 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.19993e-18 +trainer/Advantage Score Mean -0.24415 +trainer/Advantage Score Std 0.617793 +trainer/Advantage Score Max 2.02399 +trainer/Advantage Score Min -4.00115 +trainer/V1 Predictions Mean -71.828 +trainer/V1 Predictions Std 20.35 +trainer/V1 Predictions Max -0.794345 +trainer/V1 Predictions Min -85.9187 +trainer/VF Loss 0.0770195 +expl/num steps total 526000 +expl/num paths total 657 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.170647 +expl/Actions Std 0.802567 +expl/Actions Max 2.31791 +expl/Actions Min -2.11286 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 499606 +eval/num paths total 529 +eval/path length Mean 679 +eval/path length Std 0 +eval/path length Max 679 +eval/path length Min 679 +eval/Rewards Mean 0.00147275 +eval/Rewards Std 0.0383482 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0241254 +eval/Actions Std 0.730432 +eval/Actions Max 0.999744 +eval/Actions Min -0.999397 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.05887e-05 +time/evaluation sampling (s) 5.30325 +time/exploration sampling (s) 6.44662 +time/logging (s) 0.00731149 +time/saving (s) 0.0186229 +time/training (s) 19.6981 +time/epoch (s) 31.4739 +time/total (s) 11873.3 +Epoch -475 +------------------------------ ---------------- +2022-05-15 21:20:52.756508 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -474 finished +------------------------------ ---------------- +epoch -474 +replay_buffer/size 999047 +trainer/num train calls 527000 +trainer/QF1 Loss 0.587115 +trainer/QF2 Loss 0.611785 +trainer/Policy Loss 16.9306 +trainer/Q1 Predictions Mean -73.9146 +trainer/Q1 Predictions Std 17.2547 +trainer/Q1 Predictions Max -0.875492 +trainer/Q1 Predictions Min -88.3057 +trainer/Q2 Predictions Mean -73.9296 +trainer/Q2 Predictions Std 17.2999 +trainer/Q2 Predictions Max -0.419255 +trainer/Q2 Predictions Min -88.2327 +trainer/Q Targets Mean -73.9108 +trainer/Q Targets Std 17.0944 +trainer/Q Targets Max -1.23242 +trainer/Q Targets Min -87.46 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0236278 +trainer/policy/mean Std 0.710872 +trainer/policy/mean Max 0.99808 +trainer/policy/mean Min -0.99759 +trainer/policy/std Mean 0.419586 +trainer/policy/std Std 0.0213632 +trainer/policy/std Max 0.439938 +trainer/policy/std Min 0.385786 +trainer/Advantage Weights Mean 3.96188 +trainer/Advantage Weights Std 16.6726 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.58514e-11 +trainer/Advantage Score Mean -0.325332 +trainer/Advantage Score Std 0.463713 +trainer/Advantage Score Max 0.852333 +trainer/Advantage Score Min -2.48678 +trainer/V1 Predictions Mean -73.6941 +trainer/V1 Predictions Std 17.1948 +trainer/V1 Predictions Max -0.45342 +trainer/V1 Predictions Min -87.4987 +trainer/VF Loss 0.0477407 +expl/num steps total 527000 +expl/num paths total 659 +expl/path length Mean 500 +expl/path length Std 340 +expl/path length Max 840 +expl/path length Min 160 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0572776 +expl/Actions Std 0.840968 +expl/Actions Max 2.45904 +expl/Actions Min -2.52641 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 500606 +eval/num paths total 530 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0977103 +eval/Actions Std 0.694508 +eval/Actions Max 0.999417 +eval/Actions Min -0.999128 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.5635e-05 +time/evaluation sampling (s) 4.99718 +time/exploration sampling (s) 6.83838 +time/logging (s) 0.0127764 +time/saving (s) 0.0188642 +time/training (s) 19.9264 +time/epoch (s) 31.7936 +time/total (s) 11905.1 +Epoch -474 +------------------------------ ---------------- +2022-05-15 21:21:24.249206 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -473 finished +------------------------------ ---------------- +epoch -473 +replay_buffer/size 999047 +trainer/num train calls 528000 +trainer/QF1 Loss 0.445058 +trainer/QF2 Loss 0.48315 +trainer/Policy Loss 41.1532 +trainer/Q1 Predictions Mean -74.0391 +trainer/Q1 Predictions Std 17.1657 +trainer/Q1 Predictions Max -0.85377 +trainer/Q1 Predictions Min -87.64 +trainer/Q2 Predictions Mean -73.9423 +trainer/Q2 Predictions Std 17.1363 +trainer/Q2 Predictions Max -0.791487 +trainer/Q2 Predictions Min -87.2406 +trainer/Q Targets Mean -73.9541 +trainer/Q Targets Std 17.2805 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4858 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.028211 +trainer/policy/mean Std 0.7344 +trainer/policy/mean Max 0.999853 +trainer/policy/mean Min -0.998826 +trainer/policy/std Mean 0.420306 +trainer/policy/std Std 0.0204497 +trainer/policy/std Max 0.441133 +trainer/policy/std Min 0.387465 +trainer/Advantage Weights Mean 8.93317 +trainer/Advantage Weights Std 24.6352 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.02101e-19 +trainer/Advantage Score Mean -0.31953 +trainer/Advantage Score Std 0.717779 +trainer/Advantage Score Max 1.38985 +trainer/Advantage Score Min -4.37283 +trainer/V1 Predictions Mean -73.6354 +trainer/V1 Predictions Std 17.6067 +trainer/V1 Predictions Max 0.606786 +trainer/V1 Predictions Min -87.0708 +trainer/VF Loss 0.100544 +expl/num steps total 528000 +expl/num paths total 660 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0509288 +expl/Actions Std 0.832934 +expl/Actions Max 2.65459 +expl/Actions Min -2.2216 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 501495 +eval/num paths total 531 +eval/path length Mean 889 +eval/path length Std 0 +eval/path length Max 889 +eval/path length Min 889 +eval/Rewards Mean 0.00112486 +eval/Rewards Std 0.0335201 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0458319 +eval/Actions Std 0.723406 +eval/Actions Max 0.999469 +eval/Actions Min -0.999601 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2293e-05 +time/evaluation sampling (s) 5.05898 +time/exploration sampling (s) 6.74137 +time/logging (s) 0.0116677 +time/saving (s) 0.0156795 +time/training (s) 19.6466 +time/epoch (s) 31.4743 +time/total (s) 11936.6 +Epoch -473 +------------------------------ ---------------- +2022-05-15 21:21:56.612498 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -472 finished +------------------------------ ---------------- +epoch -472 +replay_buffer/size 999047 +trainer/num train calls 529000 +trainer/QF1 Loss 0.738472 +trainer/QF2 Loss 0.780642 +trainer/Policy Loss 20.1143 +trainer/Q1 Predictions Mean -75.3677 +trainer/Q1 Predictions Std 14.608 +trainer/Q1 Predictions Max -2.3703 +trainer/Q1 Predictions Min -86.1956 +trainer/Q2 Predictions Mean -75.3504 +trainer/Q2 Predictions Std 14.655 +trainer/Q2 Predictions Max -1.82558 +trainer/Q2 Predictions Min -86.1576 +trainer/Q Targets Mean -75.5681 +trainer/Q Targets Std 14.6576 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5333 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0105734 +trainer/policy/mean Std 0.727758 +trainer/policy/mean Max 0.999235 +trainer/policy/mean Min -0.997824 +trainer/policy/std Mean 0.420797 +trainer/policy/std Std 0.0201774 +trainer/policy/std Max 0.441385 +trainer/policy/std Min 0.387156 +trainer/Advantage Weights Mean 6.38829 +trainer/Advantage Weights Std 21.8037 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.01089e-21 +trainer/Advantage Score Mean -0.291556 +trainer/Advantage Score Std 0.574943 +trainer/Advantage Score Max 1.07774 +trainer/Advantage Score Min -4.61559 +trainer/V1 Predictions Mean -75.4157 +trainer/V1 Predictions Std 14.6459 +trainer/V1 Predictions Max -3.392 +trainer/V1 Predictions Min -86.429 +trainer/VF Loss 0.0736245 +expl/num steps total 529000 +expl/num paths total 662 +expl/path length Mean 500 +expl/path length Std 396 +expl/path length Max 896 +expl/path length Min 104 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0576901 +expl/Actions Std 0.822735 +expl/Actions Max 2.36256 +expl/Actions Min -2.37118 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 502495 +eval/num paths total 532 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.101327 +eval/Actions Std 0.740553 +eval/Actions Max 0.998945 +eval/Actions Min -0.999321 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.29137e-05 +time/evaluation sampling (s) 5.35153 +time/exploration sampling (s) 7.26238 +time/logging (s) 0.00783917 +time/saving (s) 0.0171983 +time/training (s) 19.7045 +time/epoch (s) 32.3435 +time/total (s) 11968.9 +Epoch -472 +------------------------------ ---------------- +2022-05-15 21:22:28.897110 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -471 finished +------------------------------ ---------------- +epoch -471 +replay_buffer/size 999047 +trainer/num train calls 530000 +trainer/QF1 Loss 1.38988 +trainer/QF2 Loss 1.44183 +trainer/Policy Loss 14.8797 +trainer/Q1 Predictions Mean -73.0086 +trainer/Q1 Predictions Std 17.8538 +trainer/Q1 Predictions Max -0.450891 +trainer/Q1 Predictions Min -86.4048 +trainer/Q2 Predictions Mean -73.0006 +trainer/Q2 Predictions Std 17.9095 +trainer/Q2 Predictions Max 0.196114 +trainer/Q2 Predictions Min -86.261 +trainer/Q Targets Mean -73.3 +trainer/Q Targets Std 17.3111 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.133 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0269942 +trainer/policy/mean Std 0.726185 +trainer/policy/mean Max 0.999249 +trainer/policy/mean Min -0.999453 +trainer/policy/std Mean 0.420801 +trainer/policy/std Std 0.0207344 +trainer/policy/std Max 0.441128 +trainer/policy/std Min 0.386936 +trainer/Advantage Weights Mean 5.29832 +trainer/Advantage Weights Std 19.5486 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.75523e-20 +trainer/Advantage Score Mean -0.34476 +trainer/Advantage Score Std 0.558158 +trainer/Advantage Score Max 1.60997 +trainer/Advantage Score Min -4.40033 +trainer/V1 Predictions Mean -72.9677 +trainer/V1 Predictions Std 17.6421 +trainer/V1 Predictions Max 0.00543618 +trainer/V1 Predictions Min -85.9842 +trainer/VF Loss 0.0773336 +expl/num steps total 530000 +expl/num paths total 663 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0180942 +expl/Actions Std 0.83331 +expl/Actions Max 2.42795 +expl/Actions Min -2.14307 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 503495 +eval/num paths total 533 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0877811 +eval/Actions Std 0.704554 +eval/Actions Max 0.997871 +eval/Actions Min -0.998762 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02464e-05 +time/evaluation sampling (s) 5.96266 +time/exploration sampling (s) 7.08645 +time/logging (s) 0.0104845 +time/saving (s) 0.0127081 +time/training (s) 19.2001 +time/epoch (s) 32.2724 +time/total (s) 12001.2 +Epoch -471 +------------------------------ ---------------- +2022-05-15 21:23:00.298646 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -470 finished +------------------------------ ---------------- +epoch -470 +replay_buffer/size 999047 +trainer/num train calls 531000 +trainer/QF1 Loss 1.19573 +trainer/QF2 Loss 1.1428 +trainer/Policy Loss 7.76207 +trainer/Q1 Predictions Mean -73.3689 +trainer/Q1 Predictions Std 19.505 +trainer/Q1 Predictions Max -1.03832 +trainer/Q1 Predictions Min -87.524 +trainer/Q2 Predictions Mean -73.364 +trainer/Q2 Predictions Std 19.5146 +trainer/Q2 Predictions Max -0.372148 +trainer/Q2 Predictions Min -87.7846 +trainer/Q Targets Mean -72.9085 +trainer/Q Targets Std 19.8591 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3458 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0102979 +trainer/policy/mean Std 0.720612 +trainer/policy/mean Max 0.998559 +trainer/policy/mean Min -0.999419 +trainer/policy/std Mean 0.420082 +trainer/policy/std Std 0.0215345 +trainer/policy/std Max 0.442134 +trainer/policy/std Min 0.385581 +trainer/Advantage Weights Mean 3.45888 +trainer/Advantage Weights Std 16.2424 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.71951e-15 +trainer/Advantage Score Mean -0.437806 +trainer/Advantage Score Std 0.565146 +trainer/Advantage Score Max 2.54664 +trainer/Advantage Score Min -3.39967 +trainer/V1 Predictions Mean -72.742 +trainer/V1 Predictions Std 19.793 +trainer/V1 Predictions Max -0.9952 +trainer/V1 Predictions Min -87.2159 +trainer/VF Loss 0.0852305 +expl/num steps total 531000 +expl/num paths total 665 +expl/path length Mean 500 +expl/path length Std 207 +expl/path length Max 707 +expl/path length Min 293 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0447936 +expl/Actions Std 0.807368 +expl/Actions Max 2.29455 +expl/Actions Min -2.30554 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 504105 +eval/num paths total 534 +eval/path length Mean 610 +eval/path length Std 0 +eval/path length Max 610 +eval/path length Min 610 +eval/Rewards Mean 0.00163934 +eval/Rewards Std 0.0404556 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0303412 +eval/Actions Std 0.72765 +eval/Actions Max 0.999636 +eval/Actions Min -0.999002 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.25673e-05 +time/evaluation sampling (s) 5.31106 +time/exploration sampling (s) 6.90257 +time/logging (s) 0.00951526 +time/saving (s) 0.015421 +time/training (s) 19.1465 +time/epoch (s) 31.3851 +time/total (s) 12032.6 +Epoch -470 +------------------------------ ---------------- +2022-05-15 21:23:31.555613 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -469 finished +------------------------------ ---------------- +epoch -469 +replay_buffer/size 999047 +trainer/num train calls 532000 +trainer/QF1 Loss 5.85334 +trainer/QF2 Loss 5.90234 +trainer/Policy Loss 7.07836 +trainer/Q1 Predictions Mean -74.6978 +trainer/Q1 Predictions Std 16.2491 +trainer/Q1 Predictions Max -0.654076 +trainer/Q1 Predictions Min -88.2631 +trainer/Q2 Predictions Mean -74.6786 +trainer/Q2 Predictions Std 16.2383 +trainer/Q2 Predictions Max -1.32611 +trainer/Q2 Predictions Min -87.8909 +trainer/Q Targets Mean -74.0412 +trainer/Q Targets Std 16.5283 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.8118 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.011993 +trainer/policy/mean Std 0.721188 +trainer/policy/mean Max 0.997928 +trainer/policy/mean Min -0.999463 +trainer/policy/std Mean 0.421331 +trainer/policy/std Std 0.0213433 +trainer/policy/std Max 0.444992 +trainer/policy/std Min 0.385445 +trainer/Advantage Weights Mean 1.78399 +trainer/Advantage Weights Std 10.1156 +trainer/Advantage Weights Max 97.3436 +trainer/Advantage Weights Min 6.89597e-19 +trainer/Advantage Score Mean -0.447495 +trainer/Advantage Score Std 0.551549 +trainer/Advantage Score Max 0.457825 +trainer/Advantage Score Min -4.18182 +trainer/V1 Predictions Mean -73.9543 +trainer/V1 Predictions Std 16.4454 +trainer/V1 Predictions Max -0.906712 +trainer/V1 Predictions Min -87.9974 +trainer/VF Loss 0.0543008 +expl/num steps total 532000 +expl/num paths total 666 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.132173 +expl/Actions Std 0.798293 +expl/Actions Max 2.23978 +expl/Actions Min -2.25054 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 504980 +eval/num paths total 535 +eval/path length Mean 875 +eval/path length Std 0 +eval/path length Max 875 +eval/path length Min 875 +eval/Rewards Mean 0.00114286 +eval/Rewards Std 0.0337868 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0422473 +eval/Actions Std 0.742598 +eval/Actions Max 0.999881 +eval/Actions Min -0.999471 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.92761e-06 +time/evaluation sampling (s) 4.88951 +time/exploration sampling (s) 7.19889 +time/logging (s) 0.0106923 +time/saving (s) 0.0150424 +time/training (s) 19.1285 +time/epoch (s) 31.2427 +time/total (s) 12063.8 +Epoch -469 +------------------------------ ---------------- +2022-05-15 21:24:04.027357 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -468 finished +------------------------------ ---------------- +epoch -468 +replay_buffer/size 999047 +trainer/num train calls 533000 +trainer/QF1 Loss 0.965132 +trainer/QF2 Loss 0.904754 +trainer/Policy Loss 11.257 +trainer/Q1 Predictions Mean -75.3727 +trainer/Q1 Predictions Std 15.238 +trainer/Q1 Predictions Max -2.81122 +trainer/Q1 Predictions Min -88.4405 +trainer/Q2 Predictions Mean -75.34 +trainer/Q2 Predictions Std 15.1848 +trainer/Q2 Predictions Max -3.08533 +trainer/Q2 Predictions Min -88.0141 +trainer/Q Targets Mean -75.3074 +trainer/Q Targets Std 15.3622 +trainer/Q Targets Max -2.219 +trainer/Q Targets Min -86.7737 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0142076 +trainer/policy/mean Std 0.725776 +trainer/policy/mean Max 0.99991 +trainer/policy/mean Min -0.999523 +trainer/policy/std Mean 0.418676 +trainer/policy/std Std 0.0204134 +trainer/policy/std Max 0.43982 +trainer/policy/std Min 0.384842 +trainer/Advantage Weights Mean 2.71141 +trainer/Advantage Weights Std 12.521 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.08642e-10 +trainer/Advantage Score Mean -0.395403 +trainer/Advantage Score Std 0.448392 +trainer/Advantage Score Max 0.94532 +trainer/Advantage Score Min -2.2943 +trainer/V1 Predictions Mean -75.0875 +trainer/V1 Predictions Std 15.37 +trainer/V1 Predictions Max -1.89547 +trainer/V1 Predictions Min -86.655 +trainer/VF Loss 0.0450366 +expl/num steps total 533000 +expl/num paths total 667 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00880512 +expl/Actions Std 0.840627 +expl/Actions Max 2.52475 +expl/Actions Min -2.4154 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 505980 +eval/num paths total 536 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0845288 +eval/Actions Std 0.715343 +eval/Actions Max 0.999285 +eval/Actions Min -0.999814 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.04909e-05 +time/evaluation sampling (s) 5.50911 +time/exploration sampling (s) 7.49409 +time/logging (s) 0.0112056 +time/saving (s) 0.0167192 +time/training (s) 19.4265 +time/epoch (s) 32.4576 +time/total (s) 12096.3 +Epoch -468 +------------------------------ ---------------- +2022-05-15 21:24:35.600733 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -467 finished +------------------------------ ---------------- +epoch -467 +replay_buffer/size 999047 +trainer/num train calls 534000 +trainer/QF1 Loss 0.847083 +trainer/QF2 Loss 0.722867 +trainer/Policy Loss 14.7634 +trainer/Q1 Predictions Mean -74.1105 +trainer/Q1 Predictions Std 16.9921 +trainer/Q1 Predictions Max -1.32766 +trainer/Q1 Predictions Min -87.2437 +trainer/Q2 Predictions Mean -74.0281 +trainer/Q2 Predictions Std 17.049 +trainer/Q2 Predictions Max -0.707458 +trainer/Q2 Predictions Min -87.2176 +trainer/Q Targets Mean -73.6647 +trainer/Q Targets Std 17.3926 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5402 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00888477 +trainer/policy/mean Std 0.723809 +trainer/policy/mean Max 0.998827 +trainer/policy/mean Min -0.998915 +trainer/policy/std Mean 0.416706 +trainer/policy/std Std 0.0213434 +trainer/policy/std Max 0.440229 +trainer/policy/std Min 0.38309 +trainer/Advantage Weights Mean 2.14494 +trainer/Advantage Weights Std 12.6762 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.57936e-20 +trainer/Advantage Score Mean -0.55626 +trainer/Advantage Score Std 0.6316 +trainer/Advantage Score Max 0.811583 +trainer/Advantage Score Min -4.45301 +trainer/V1 Predictions Mean -73.4644 +trainer/V1 Predictions Std 17.3434 +trainer/V1 Predictions Max 1.05245 +trainer/V1 Predictions Min -87.4298 +trainer/VF Loss 0.0791666 +expl/num steps total 534000 +expl/num paths total 669 +expl/path length Mean 500 +expl/path length Std 305 +expl/path length Max 805 +expl/path length Min 195 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0419154 +expl/Actions Std 0.826766 +expl/Actions Max 2.35057 +expl/Actions Min -2.71582 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 506568 +eval/num paths total 537 +eval/path length Mean 588 +eval/path length Std 0 +eval/path length Max 588 +eval/path length Min 588 +eval/Rewards Mean 0.00170068 +eval/Rewards Std 0.0412042 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.032096 +eval/Actions Std 0.727263 +eval/Actions Max 0.999652 +eval/Actions Min -0.99969 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.25724e-05 +time/evaluation sampling (s) 5.54558 +time/exploration sampling (s) 6.80174 +time/logging (s) 0.00909693 +time/saving (s) 0.0202777 +time/training (s) 19.1768 +time/epoch (s) 31.5535 +time/total (s) 12127.9 +Epoch -467 +------------------------------ ---------------- +2022-05-15 21:25:07.430020 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -466 finished +------------------------------ ---------------- +epoch -466 +replay_buffer/size 999047 +trainer/num train calls 535000 +trainer/QF1 Loss 1.01112 +trainer/QF2 Loss 1.02789 +trainer/Policy Loss 13.5811 +trainer/Q1 Predictions Mean -72.8617 +trainer/Q1 Predictions Std 18.6911 +trainer/Q1 Predictions Max -1.21778 +trainer/Q1 Predictions Min -86.1646 +trainer/Q2 Predictions Mean -72.8344 +trainer/Q2 Predictions Std 18.7576 +trainer/Q2 Predictions Max -0.494811 +trainer/Q2 Predictions Min -86.3803 +trainer/Q Targets Mean -72.6698 +trainer/Q Targets Std 18.5695 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.6846 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0275777 +trainer/policy/mean Std 0.717095 +trainer/policy/mean Max 0.999578 +trainer/policy/mean Min -0.998243 +trainer/policy/std Mean 0.417734 +trainer/policy/std Std 0.0205519 +trainer/policy/std Max 0.440387 +trainer/policy/std Min 0.38402 +trainer/Advantage Weights Mean 2.17399 +trainer/Advantage Weights Std 13.8744 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.76017e-13 +trainer/Advantage Score Mean -0.634171 +trainer/Advantage Score Std 0.541014 +trainer/Advantage Score Max 1.20581 +trainer/Advantage Score Min -2.76553 +trainer/V1 Predictions Mean -72.3841 +trainer/V1 Predictions Std 18.7316 +trainer/V1 Predictions Max -0.027991 +trainer/V1 Predictions Min -86.0228 +trainer/VF Loss 0.0788464 +expl/num steps total 535000 +expl/num paths total 670 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0586443 +expl/Actions Std 0.81197 +expl/Actions Max 2.27947 +expl/Actions Min -2.81027 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 507280 +eval/num paths total 538 +eval/path length Mean 712 +eval/path length Std 0 +eval/path length Max 712 +eval/path length Min 712 +eval/Rewards Mean 0.00140449 +eval/Rewards Std 0.0374503 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.00195318 +eval/Actions Std 0.723101 +eval/Actions Max 0.99974 +eval/Actions Min -0.999919 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.25701e-05 +time/evaluation sampling (s) 4.88935 +time/exploration sampling (s) 6.38375 +time/logging (s) 0.0111867 +time/saving (s) 0.0181618 +time/training (s) 20.5132 +time/epoch (s) 31.8157 +time/total (s) 12159.7 +Epoch -466 +------------------------------ ---------------- +2022-05-15 21:25:39.417954 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -465 finished +------------------------------ ---------------- +epoch -465 +replay_buffer/size 999047 +trainer/num train calls 536000 +trainer/QF1 Loss 1.35891 +trainer/QF2 Loss 1.42262 +trainer/Policy Loss 50.7943 +trainer/Q1 Predictions Mean -74.1176 +trainer/Q1 Predictions Std 16.8411 +trainer/Q1 Predictions Max -0.882143 +trainer/Q1 Predictions Min -86.9646 +trainer/Q2 Predictions Mean -74.0733 +trainer/Q2 Predictions Std 16.8514 +trainer/Q2 Predictions Max 0.0815522 +trainer/Q2 Predictions Min -87.1156 +trainer/Q Targets Mean -74.4819 +trainer/Q Targets Std 16.76 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4706 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0280113 +trainer/policy/mean Std 0.729216 +trainer/policy/mean Max 0.997945 +trainer/policy/mean Min -0.999645 +trainer/policy/std Mean 0.418474 +trainer/policy/std Std 0.0197859 +trainer/policy/std Max 0.43843 +trainer/policy/std Min 0.386662 +trainer/Advantage Weights Mean 11.2036 +trainer/Advantage Weights Std 26.2612 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.89643e-16 +trainer/Advantage Score Mean -0.0588834 +trainer/Advantage Score Std 0.528174 +trainer/Advantage Score Max 3.40219 +trainer/Advantage Score Min -3.54813 +trainer/V1 Predictions Mean -74.2708 +trainer/V1 Predictions Std 16.856 +trainer/V1 Predictions Max -1.37094 +trainer/V1 Predictions Min -87.2635 +trainer/VF Loss 0.120368 +expl/num steps total 536000 +expl/num paths total 671 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0542113 +expl/Actions Std 0.812668 +expl/Actions Max 2.22014 +expl/Actions Min -2.58544 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 508280 +eval/num paths total 539 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0353217 +eval/Actions Std 0.658317 +eval/Actions Max 0.998496 +eval/Actions Min -0.998307 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.30085e-06 +time/evaluation sampling (s) 5.52249 +time/exploration sampling (s) 6.34958 +time/logging (s) 0.0124887 +time/saving (s) 0.0186951 +time/training (s) 20.0688 +time/epoch (s) 31.972 +time/total (s) 12191.7 +Epoch -465 +------------------------------ ---------------- +2022-05-15 21:26:12.548945 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -464 finished +------------------------------ ---------------- +epoch -464 +replay_buffer/size 999047 +trainer/num train calls 537000 +trainer/QF1 Loss 0.96065 +trainer/QF2 Loss 1.03247 +trainer/Policy Loss 23.9117 +trainer/Q1 Predictions Mean -74.9774 +trainer/Q1 Predictions Std 14.7159 +trainer/Q1 Predictions Max -3.36613 +trainer/Q1 Predictions Min -87.1277 +trainer/Q2 Predictions Mean -74.8529 +trainer/Q2 Predictions Std 14.7233 +trainer/Q2 Predictions Max -3.54805 +trainer/Q2 Predictions Min -87.1681 +trainer/Q Targets Mean -75.4049 +trainer/Q Targets Std 14.6193 +trainer/Q Targets Max -4.30328 +trainer/Q Targets Min -87.6273 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0164887 +trainer/policy/mean Std 0.725248 +trainer/policy/mean Max 0.999269 +trainer/policy/mean Min -0.999382 +trainer/policy/std Mean 0.417596 +trainer/policy/std Std 0.01858 +trainer/policy/std Max 0.437151 +trainer/policy/std Min 0.389162 +trainer/Advantage Weights Mean 6.67972 +trainer/Advantage Weights Std 19.2962 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.5059e-19 +trainer/Advantage Score Mean -0.160194 +trainer/Advantage Score Std 0.544054 +trainer/Advantage Score Max 1.7238 +trainer/Advantage Score Min -4.28305 +trainer/V1 Predictions Mean -75.0714 +trainer/V1 Predictions Std 14.8076 +trainer/V1 Predictions Max -2.63661 +trainer/V1 Predictions Min -87.5117 +trainer/VF Loss 0.061168 +expl/num steps total 537000 +expl/num paths total 673 +expl/path length Mean 500 +expl/path length Std 40 +expl/path length Max 540 +expl/path length Min 460 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0272838 +expl/Actions Std 0.830061 +expl/Actions Max 2.47299 +expl/Actions Min -2.55238 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 509280 +eval/num paths total 540 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.264386 +eval/Actions Std 0.724608 +eval/Actions Max 0.999256 +eval/Actions Min -0.999119 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.26806e-06 +time/evaluation sampling (s) 5.73446 +time/exploration sampling (s) 7.62014 +time/logging (s) 0.0133061 +time/saving (s) 0.0196064 +time/training (s) 19.7274 +time/epoch (s) 33.115 +time/total (s) 12224.8 +Epoch -464 +------------------------------ ---------------- +2022-05-15 21:26:44.583584 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -463 finished +------------------------------ ---------------- +epoch -463 +replay_buffer/size 999047 +trainer/num train calls 538000 +trainer/QF1 Loss 1.03861 +trainer/QF2 Loss 1.13422 +trainer/Policy Loss 30.2694 +trainer/Q1 Predictions Mean -72.7756 +trainer/Q1 Predictions Std 17.7199 +trainer/Q1 Predictions Max -1.74564 +trainer/Q1 Predictions Min -86.2217 +trainer/Q2 Predictions Mean -72.7333 +trainer/Q2 Predictions Std 17.6796 +trainer/Q2 Predictions Max -1.73273 +trainer/Q2 Predictions Min -86.135 +trainer/Q Targets Mean -72.903 +trainer/Q Targets Std 17.0935 +trainer/Q Targets Max -3.13214 +trainer/Q Targets Min -85.9451 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0100884 +trainer/policy/mean Std 0.734598 +trainer/policy/mean Max 0.999476 +trainer/policy/mean Min -0.998618 +trainer/policy/std Mean 0.418472 +trainer/policy/std Std 0.0180542 +trainer/policy/std Max 0.439436 +trainer/policy/std Min 0.392119 +trainer/Advantage Weights Mean 6.9018 +trainer/Advantage Weights Std 22.9319 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.75646e-13 +trainer/Advantage Score Mean -0.305116 +trainer/Advantage Score Std 0.469345 +trainer/Advantage Score Max 1.52567 +trainer/Advantage Score Min -2.93703 +trainer/V1 Predictions Mean -72.5699 +trainer/V1 Predictions Std 17.4121 +trainer/V1 Predictions Max -1.6233 +trainer/V1 Predictions Min -85.7151 +trainer/VF Loss 0.058937 +expl/num steps total 538000 +expl/num paths total 674 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.169216 +expl/Actions Std 0.856804 +expl/Actions Max 2.54559 +expl/Actions Min -2.31542 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 510280 +eval/num paths total 541 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0476007 +eval/Actions Std 0.740423 +eval/Actions Max 0.999889 +eval/Actions Min -0.999513 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0313e-05 +time/evaluation sampling (s) 5.86545 +time/exploration sampling (s) 6.65344 +time/logging (s) 0.0112782 +time/saving (s) 0.0156208 +time/training (s) 19.4656 +time/epoch (s) 32.0114 +time/total (s) 12256.8 +Epoch -463 +------------------------------ ---------------- +2022-05-15 21:27:16.521062 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -462 finished +------------------------------ ---------------- +epoch -462 +replay_buffer/size 999047 +trainer/num train calls 539000 +trainer/QF1 Loss 0.700741 +trainer/QF2 Loss 0.699815 +trainer/Policy Loss 31.5577 +trainer/Q1 Predictions Mean -74.604 +trainer/Q1 Predictions Std 17.3073 +trainer/Q1 Predictions Max -1.18655 +trainer/Q1 Predictions Min -87.4709 +trainer/Q2 Predictions Mean -74.5459 +trainer/Q2 Predictions Std 17.3127 +trainer/Q2 Predictions Max -1.80136 +trainer/Q2 Predictions Min -87.2859 +trainer/Q Targets Mean -74.7453 +trainer/Q Targets Std 17.2563 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4314 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00270192 +trainer/policy/mean Std 0.722774 +trainer/policy/mean Max 0.99834 +trainer/policy/mean Min -0.999962 +trainer/policy/std Mean 0.421512 +trainer/policy/std Std 0.0192715 +trainer/policy/std Max 0.444627 +trainer/policy/std Min 0.392484 +trainer/Advantage Weights Mean 5.35295 +trainer/Advantage Weights Std 19.4559 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.04525e-12 +trainer/Advantage Score Mean -0.241457 +trainer/Advantage Score Std 0.480823 +trainer/Advantage Score Max 1.2314 +trainer/Advantage Score Min -2.75868 +trainer/V1 Predictions Mean -74.501 +trainer/V1 Predictions Std 17.358 +trainer/V1 Predictions Max -1.68334 +trainer/V1 Predictions Min -87.6506 +trainer/VF Loss 0.0526406 +expl/num steps total 539000 +expl/num paths total 676 +expl/path length Mean 500 +expl/path length Std 242 +expl/path length Max 742 +expl/path length Min 258 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0319326 +expl/Actions Std 0.824155 +expl/Actions Max 2.08001 +expl/Actions Min -2.38556 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 511280 +eval/num paths total 542 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.119197 +eval/Actions Std 0.624669 +eval/Actions Max 0.999646 +eval/Actions Min -0.99815 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02581e-05 +time/evaluation sampling (s) 4.87206 +time/exploration sampling (s) 7.31155 +time/logging (s) 0.01263 +time/saving (s) 0.0186538 +time/training (s) 19.707 +time/epoch (s) 31.9219 +time/total (s) 12288.7 +Epoch -462 +------------------------------ ---------------- +2022-05-15 21:27:47.465469 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -461 finished +------------------------------ ---------------- +epoch -461 +replay_buffer/size 999047 +trainer/num train calls 540000 +trainer/QF1 Loss 0.835614 +trainer/QF2 Loss 0.840158 +trainer/Policy Loss 39.0822 +trainer/Q1 Predictions Mean -73.0061 +trainer/Q1 Predictions Std 17.2009 +trainer/Q1 Predictions Max -0.65018 +trainer/Q1 Predictions Min -86.1931 +trainer/Q2 Predictions Mean -72.9887 +trainer/Q2 Predictions Std 17.2016 +trainer/Q2 Predictions Max -0.320488 +trainer/Q2 Predictions Min -86.3895 +trainer/Q Targets Mean -73.5111 +trainer/Q Targets Std 17.3059 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9416 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0365955 +trainer/policy/mean Std 0.729832 +trainer/policy/mean Max 0.997461 +trainer/policy/mean Min -0.999744 +trainer/policy/std Mean 0.419509 +trainer/policy/std Std 0.0189188 +trainer/policy/std Max 0.438998 +trainer/policy/std Min 0.392483 +trainer/Advantage Weights Mean 8.8025 +trainer/Advantage Weights Std 21.2277 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.38572e-15 +trainer/Advantage Score Mean -0.128415 +trainer/Advantage Score Std 0.573989 +trainer/Advantage Score Max 1.69987 +trainer/Advantage Score Min -3.36693 +trainer/V1 Predictions Mean -73.2771 +trainer/V1 Predictions Std 17.4219 +trainer/V1 Predictions Max -1.30779 +trainer/V1 Predictions Min -86.8747 +trainer/VF Loss 0.0797152 +expl/num steps total 540000 +expl/num paths total 677 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0548236 +expl/Actions Std 0.828974 +expl/Actions Max 2.57853 +expl/Actions Min -2.28419 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 512280 +eval/num paths total 543 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0500838 +eval/Actions Std 0.732151 +eval/Actions Max 0.999748 +eval/Actions Min -0.999319 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.66433e-06 +time/evaluation sampling (s) 4.99568 +time/exploration sampling (s) 6.87334 +time/logging (s) 0.00997273 +time/saving (s) 0.0137205 +time/training (s) 19.032 +time/epoch (s) 30.9247 +time/total (s) 12319.7 +Epoch -461 +------------------------------ ---------------- +2022-05-15 21:28:18.730338 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -460 finished +------------------------------ ---------------- +epoch -460 +replay_buffer/size 999047 +trainer/num train calls 541000 +trainer/QF1 Loss 0.891119 +trainer/QF2 Loss 0.870652 +trainer/Policy Loss 8.61174 +trainer/Q1 Predictions Mean -74.5876 +trainer/Q1 Predictions Std 16.387 +trainer/Q1 Predictions Max -2.77973 +trainer/Q1 Predictions Min -87.6633 +trainer/Q2 Predictions Mean -74.57 +trainer/Q2 Predictions Std 16.4083 +trainer/Q2 Predictions Max -3.80349 +trainer/Q2 Predictions Min -87.7515 +trainer/Q Targets Mean -74.1542 +trainer/Q Targets Std 16.3328 +trainer/Q Targets Max -3.29619 +trainer/Q Targets Min -86.9595 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00633955 +trainer/policy/mean Std 0.731259 +trainer/policy/mean Max 0.999171 +trainer/policy/mean Min -0.997554 +trainer/policy/std Mean 0.41865 +trainer/policy/std Std 0.0209695 +trainer/policy/std Max 0.441031 +trainer/policy/std Min 0.387611 +trainer/Advantage Weights Mean 2.5753 +trainer/Advantage Weights Std 13.5693 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.91793e-17 +trainer/Advantage Score Mean -0.505553 +trainer/Advantage Score Std 0.605316 +trainer/Advantage Score Max 1.028 +trainer/Advantage Score Min -3.84927 +trainer/V1 Predictions Mean -73.8923 +trainer/V1 Predictions Std 16.5785 +trainer/V1 Predictions Max -1.68889 +trainer/V1 Predictions Min -86.9463 +trainer/VF Loss 0.0708999 +expl/num steps total 541000 +expl/num paths total 679 +expl/path length Mean 500 +expl/path length Std 87 +expl/path length Max 587 +expl/path length Min 413 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0213953 +expl/Actions Std 0.818607 +expl/Actions Max 2.43322 +expl/Actions Min -2.296 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 513280 +eval/num paths total 544 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0757312 +eval/Actions Std 0.73436 +eval/Actions Max 0.999503 +eval/Actions Min -0.999188 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.19801e-05 +time/evaluation sampling (s) 5.45257 +time/exploration sampling (s) 7.17146 +time/logging (s) 0.00863116 +time/saving (s) 0.0119523 +time/training (s) 18.6043 +time/epoch (s) 31.249 +time/total (s) 12350.9 +Epoch -460 +------------------------------ ---------------- +2022-05-15 21:28:50.708760 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -459 finished +------------------------------ ---------------- +epoch -459 +replay_buffer/size 999047 +trainer/num train calls 542000 +trainer/QF1 Loss 6.94614 +trainer/QF2 Loss 6.71422 +trainer/Policy Loss 11.8468 +trainer/Q1 Predictions Mean -72.8506 +trainer/Q1 Predictions Std 18.6063 +trainer/Q1 Predictions Max -0.721211 +trainer/Q1 Predictions Min -87.9901 +trainer/Q2 Predictions Mean -72.7121 +trainer/Q2 Predictions Std 18.5798 +trainer/Q2 Predictions Max -0.103525 +trainer/Q2 Predictions Min -88.0647 +trainer/Q Targets Mean -72.5628 +trainer/Q Targets Std 18.5327 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.926 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0047899 +trainer/policy/mean Std 0.726634 +trainer/policy/mean Max 0.9979 +trainer/policy/mean Min -0.999619 +trainer/policy/std Mean 0.419017 +trainer/policy/std Std 0.0204054 +trainer/policy/std Max 0.439038 +trainer/policy/std Min 0.388597 +trainer/Advantage Weights Mean 2.81982 +trainer/Advantage Weights Std 14.6375 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.49599e-14 +trainer/Advantage Score Mean -0.479828 +trainer/Advantage Score Std 0.592124 +trainer/Advantage Score Max 3.34779 +trainer/Advantage Score Min -3.09846 +trainer/V1 Predictions Mean -72.1969 +trainer/V1 Predictions Std 18.8599 +trainer/V1 Predictions Max -0.0206614 +trainer/V1 Predictions Min -87.5991 +trainer/VF Loss 0.10425 +expl/num steps total 542000 +expl/num paths total 680 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0474232 +expl/Actions Std 0.820801 +expl/Actions Max 2.18043 +expl/Actions Min -2.33713 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 514280 +eval/num paths total 545 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0101124 +eval/Actions Std 0.726504 +eval/Actions Max 0.999582 +eval/Actions Min -0.999334 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.62726e-06 +time/evaluation sampling (s) 5.32698 +time/exploration sampling (s) 7.11215 +time/logging (s) 0.0123693 +time/saving (s) 0.018529 +time/training (s) 19.5018 +time/epoch (s) 31.9719 +time/total (s) 12382.9 +Epoch -459 +------------------------------ ---------------- +2022-05-15 21:29:22.607222 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -458 finished +------------------------------ ---------------- +epoch -458 +replay_buffer/size 999047 +trainer/num train calls 543000 +trainer/QF1 Loss 0.932274 +trainer/QF2 Loss 1.03452 +trainer/Policy Loss 29.0515 +trainer/Q1 Predictions Mean -71.5579 +trainer/Q1 Predictions Std 18.684 +trainer/Q1 Predictions Max -2.28802 +trainer/Q1 Predictions Min -86.1512 +trainer/Q2 Predictions Mean -71.5525 +trainer/Q2 Predictions Std 18.75 +trainer/Q2 Predictions Max -2.35017 +trainer/Q2 Predictions Min -86.1709 +trainer/Q Targets Mean -71.8397 +trainer/Q Targets Std 18.5162 +trainer/Q Targets Max -4.38327 +trainer/Q Targets Min -86.0707 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00582834 +trainer/policy/mean Std 0.722605 +trainer/policy/mean Max 0.998369 +trainer/policy/mean Min -0.998255 +trainer/policy/std Mean 0.419749 +trainer/policy/std Std 0.0193911 +trainer/policy/std Max 0.440882 +trainer/policy/std Min 0.390966 +trainer/Advantage Weights Mean 7.21643 +trainer/Advantage Weights Std 23.7861 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.94777e-17 +trainer/Advantage Score Mean -0.324226 +trainer/Advantage Score Std 0.659074 +trainer/Advantage Score Max 2.0974 +trainer/Advantage Score Min -3.80629 +trainer/V1 Predictions Mean -71.4755 +trainer/V1 Predictions Std 18.7489 +trainer/V1 Predictions Max -2.7114 +trainer/V1 Predictions Min -86.1386 +trainer/VF Loss 0.116622 +expl/num steps total 543000 +expl/num paths total 682 +expl/path length Mean 500 +expl/path length Std 488 +expl/path length Max 988 +expl/path length Min 12 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0117181 +expl/Actions Std 0.799978 +expl/Actions Max 2.47778 +expl/Actions Min -2.29175 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 514900 +eval/num paths total 546 +eval/path length Mean 620 +eval/path length Std 0 +eval/path length Max 620 +eval/path length Min 620 +eval/Rewards Mean 0.0016129 +eval/Rewards Std 0.0401286 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0249729 +eval/Actions Std 0.725891 +eval/Actions Max 0.99989 +eval/Actions Min -0.999871 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.17907e-06 +time/evaluation sampling (s) 5.43748 +time/exploration sampling (s) 6.87678 +time/logging (s) 0.00873978 +time/saving (s) 0.0135714 +time/training (s) 19.5416 +time/epoch (s) 31.8782 +time/total (s) 12414.8 +Epoch -458 +------------------------------ ---------------- +2022-05-15 21:29:54.021264 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -457 finished +------------------------------ ---------------- +epoch -457 +replay_buffer/size 999047 +trainer/num train calls 544000 +trainer/QF1 Loss 0.655259 +trainer/QF2 Loss 0.661709 +trainer/Policy Loss 30.4122 +trainer/Q1 Predictions Mean -72.2687 +trainer/Q1 Predictions Std 18.7007 +trainer/Q1 Predictions Max -0.619958 +trainer/Q1 Predictions Min -86.7551 +trainer/Q2 Predictions Mean -72.3149 +trainer/Q2 Predictions Std 18.8047 +trainer/Q2 Predictions Max -0.225413 +trainer/Q2 Predictions Min -86.6183 +trainer/Q Targets Mean -72.2527 +trainer/Q Targets Std 18.8034 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6124 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00129334 +trainer/policy/mean Std 0.722172 +trainer/policy/mean Max 0.999842 +trainer/policy/mean Min -0.998663 +trainer/policy/std Mean 0.420941 +trainer/policy/std Std 0.0193124 +trainer/policy/std Max 0.441394 +trainer/policy/std Min 0.390432 +trainer/Advantage Weights Mean 5.41649 +trainer/Advantage Weights Std 18.3228 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3326e-18 +trainer/Advantage Score Mean -0.331839 +trainer/Advantage Score Std 0.741382 +trainer/Advantage Score Max 1.03914 +trainer/Advantage Score Min -4.11594 +trainer/V1 Predictions Mean -71.962 +trainer/V1 Predictions Std 19.0408 +trainer/V1 Predictions Max -0.387985 +trainer/V1 Predictions Min -86.6162 +trainer/VF Loss 0.0866579 +expl/num steps total 544000 +expl/num paths total 683 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.015606 +expl/Actions Std 0.921455 +expl/Actions Max 2.48488 +expl/Actions Min -2.43299 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 515468 +eval/num paths total 547 +eval/path length Mean 568 +eval/path length Std 0 +eval/path length Max 568 +eval/path length Min 568 +eval/Rewards Mean 0.00176056 +eval/Rewards Std 0.0419221 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0469154 +eval/Actions Std 0.746629 +eval/Actions Max 0.999654 +eval/Actions Min -0.999642 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.12529e-06 +time/evaluation sampling (s) 4.83026 +time/exploration sampling (s) 7.06088 +time/logging (s) 0.00760136 +time/saving (s) 0.0138884 +time/training (s) 19.4874 +time/epoch (s) 31.4 +time/total (s) 12446.2 +Epoch -457 +------------------------------ ---------------- +2022-05-15 21:30:26.192750 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -456 finished +------------------------------ ---------------- +epoch -456 +replay_buffer/size 999047 +trainer/num train calls 545000 +trainer/QF1 Loss 0.872656 +trainer/QF2 Loss 0.941671 +trainer/Policy Loss 31.6062 +trainer/Q1 Predictions Mean -73.2637 +trainer/Q1 Predictions Std 16.2024 +trainer/Q1 Predictions Max -1.53512 +trainer/Q1 Predictions Min -86.5938 +trainer/Q2 Predictions Mean -73.1655 +trainer/Q2 Predictions Std 16.301 +trainer/Q2 Predictions Max -2.60882 +trainer/Q2 Predictions Min -86.3313 +trainer/Q Targets Mean -73.5399 +trainer/Q Targets Std 16.4803 +trainer/Q Targets Max -3.47433 +trainer/Q Targets Min -86.6437 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0197771 +trainer/policy/mean Std 0.716186 +trainer/policy/mean Max 0.999068 +trainer/policy/mean Min -0.997166 +trainer/policy/std Mean 0.420273 +trainer/policy/std Std 0.0197421 +trainer/policy/std Max 0.440302 +trainer/policy/std Min 0.388573 +trainer/Advantage Weights Mean 6.1131 +trainer/Advantage Weights Std 19.051 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.33599e-13 +trainer/Advantage Score Mean -0.332063 +trainer/Advantage Score Std 0.639342 +trainer/Advantage Score Max 1.19594 +trainer/Advantage Score Min -2.96439 +trainer/V1 Predictions Mean -73.3031 +trainer/V1 Predictions Std 16.6013 +trainer/V1 Predictions Max -1.17706 +trainer/V1 Predictions Min -86.5958 +trainer/VF Loss 0.0764663 +expl/num steps total 545000 +expl/num paths total 684 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0618752 +expl/Actions Std 0.846866 +expl/Actions Max 2.24078 +expl/Actions Min -2.24226 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 516468 +eval/num paths total 548 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00969697 +eval/Actions Std 0.708798 +eval/Actions Max 0.999897 +eval/Actions Min -0.999857 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.62495e-06 +time/evaluation sampling (s) 5.02737 +time/exploration sampling (s) 7.50711 +time/logging (s) 0.012999 +time/saving (s) 0.0164932 +time/training (s) 19.6007 +time/epoch (s) 32.1647 +time/total (s) 12478.4 +Epoch -456 +------------------------------ ---------------- +2022-05-15 21:30:58.341509 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -455 finished +------------------------------ ---------------- +epoch -455 +replay_buffer/size 999047 +trainer/num train calls 546000 +trainer/QF1 Loss 1.15376 +trainer/QF2 Loss 1.1974 +trainer/Policy Loss 21.3171 +trainer/Q1 Predictions Mean -75.2437 +trainer/Q1 Predictions Std 14.9388 +trainer/Q1 Predictions Max -1.05767 +trainer/Q1 Predictions Min -89.1049 +trainer/Q2 Predictions Mean -75.2549 +trainer/Q2 Predictions Std 14.8996 +trainer/Q2 Predictions Max -1.09044 +trainer/Q2 Predictions Min -89.0938 +trainer/Q Targets Mean -75.3597 +trainer/Q Targets Std 14.8451 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.1908 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00590236 +trainer/policy/mean Std 0.725949 +trainer/policy/mean Max 0.99894 +trainer/policy/mean Min -0.999536 +trainer/policy/std Mean 0.418502 +trainer/policy/std Std 0.0203929 +trainer/policy/std Max 0.43835 +trainer/policy/std Min 0.386951 +trainer/Advantage Weights Mean 4.83691 +trainer/Advantage Weights Std 17.5575 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.02304e-16 +trainer/Advantage Score Mean -0.271038 +trainer/Advantage Score Std 0.549276 +trainer/Advantage Score Max 1.49036 +trainer/Advantage Score Min -3.68186 +trainer/V1 Predictions Mean -75.1004 +trainer/V1 Predictions Std 15.0851 +trainer/V1 Predictions Max -0.109961 +trainer/V1 Predictions Min -89.6224 +trainer/VF Loss 0.059963 +expl/num steps total 546000 +expl/num paths total 686 +expl/path length Mean 500 +expl/path length Std 358 +expl/path length Max 858 +expl/path length Min 142 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0390379 +expl/Actions Std 0.84063 +expl/Actions Max 2.52332 +expl/Actions Min -2.23559 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 517212 +eval/num paths total 549 +eval/path length Mean 744 +eval/path length Std 0 +eval/path length Max 744 +eval/path length Min 744 +eval/Rewards Mean 0.00134409 +eval/Rewards Std 0.0366371 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0472731 +eval/Actions Std 0.741818 +eval/Actions Max 0.999938 +eval/Actions Min -0.999757 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.06196e-06 +time/evaluation sampling (s) 5.17745 +time/exploration sampling (s) 7.31505 +time/logging (s) 0.00706529 +time/saving (s) 0.0110191 +time/training (s) 19.6159 +time/epoch (s) 32.1265 +time/total (s) 12510.5 +Epoch -455 +------------------------------ ---------------- +2022-05-15 21:31:31.122902 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -454 finished +------------------------------ ---------------- +epoch -454 +replay_buffer/size 999047 +trainer/num train calls 547000 +trainer/QF1 Loss 2.51329 +trainer/QF2 Loss 2.66728 +trainer/Policy Loss 19.3819 +trainer/Q1 Predictions Mean -71.3542 +trainer/Q1 Predictions Std 20.1013 +trainer/Q1 Predictions Max -0.149905 +trainer/Q1 Predictions Min -86.8056 +trainer/Q2 Predictions Mean -71.3111 +trainer/Q2 Predictions Std 20.0815 +trainer/Q2 Predictions Max -0.971439 +trainer/Q2 Predictions Min -87.1704 +trainer/Q Targets Mean -71.1793 +trainer/Q Targets Std 20.0372 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3149 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0333058 +trainer/policy/mean Std 0.720633 +trainer/policy/mean Max 0.999617 +trainer/policy/mean Min -0.998098 +trainer/policy/std Mean 0.418238 +trainer/policy/std Std 0.0203789 +trainer/policy/std Max 0.438334 +trainer/policy/std Min 0.384837 +trainer/Advantage Weights Mean 4.57067 +trainer/Advantage Weights Std 19.6811 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.13981e-20 +trainer/Advantage Score Mean -0.585951 +trainer/Advantage Score Std 0.746225 +trainer/Advantage Score Max 2.44573 +trainer/Advantage Score Min -4.42369 +trainer/V1 Predictions Mean -70.9389 +trainer/V1 Predictions Std 20.0005 +trainer/V1 Predictions Max 1.24688 +trainer/V1 Predictions Min -85.9707 +trainer/VF Loss 0.148317 +expl/num steps total 547000 +expl/num paths total 687 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.047115 +expl/Actions Std 0.853945 +expl/Actions Max 2.6949 +expl/Actions Min -2.42676 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 518212 +eval/num paths total 550 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.275983 +eval/Actions Std 0.709655 +eval/Actions Max 0.99829 +eval/Actions Min -0.999568 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.97493e-06 +time/evaluation sampling (s) 5.83454 +time/exploration sampling (s) 7.56255 +time/logging (s) 0.0123857 +time/saving (s) 0.0180519 +time/training (s) 19.348 +time/epoch (s) 32.7755 +time/total (s) 12543.3 +Epoch -454 +------------------------------ ---------------- +2022-05-15 21:32:03.188893 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -453 finished +------------------------------ ---------------- +epoch -453 +replay_buffer/size 999047 +trainer/num train calls 548000 +trainer/QF1 Loss 0.567978 +trainer/QF2 Loss 0.588109 +trainer/Policy Loss 23.4389 +trainer/Q1 Predictions Mean -75.4012 +trainer/Q1 Predictions Std 14.1276 +trainer/Q1 Predictions Max -1.93915 +trainer/Q1 Predictions Min -86.152 +trainer/Q2 Predictions Mean -75.385 +trainer/Q2 Predictions Std 14.1439 +trainer/Q2 Predictions Max -0.930145 +trainer/Q2 Predictions Min -86.0988 +trainer/Q Targets Mean -75.6912 +trainer/Q Targets Std 14.077 +trainer/Q Targets Max -2.46228 +trainer/Q Targets Min -86.9644 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00894294 +trainer/policy/mean Std 0.730144 +trainer/policy/mean Max 0.999552 +trainer/policy/mean Min -0.998926 +trainer/policy/std Mean 0.419077 +trainer/policy/std Std 0.0196461 +trainer/policy/std Max 0.44053 +trainer/policy/std Min 0.386165 +trainer/Advantage Weights Mean 3.45889 +trainer/Advantage Weights Std 17.463 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.62195e-12 +trainer/Advantage Score Mean -0.429122 +trainer/Advantage Score Std 0.513078 +trainer/Advantage Score Max 2.60442 +trainer/Advantage Score Min -2.5367 +trainer/V1 Predictions Mean -75.3767 +trainer/V1 Predictions Std 14.285 +trainer/V1 Predictions Max -0.949443 +trainer/V1 Predictions Min -86.865 +trainer/VF Loss 0.076201 +expl/num steps total 548000 +expl/num paths total 688 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0417185 +expl/Actions Std 0.847797 +expl/Actions Max 2.45852 +expl/Actions Min -2.51516 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 519038 +eval/num paths total 551 +eval/path length Mean 826 +eval/path length Std 0 +eval/path length Max 826 +eval/path length Min 826 +eval/Rewards Mean 0.00121065 +eval/Rewards Std 0.0347734 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0523027 +eval/Actions Std 0.725548 +eval/Actions Max 0.999766 +eval/Actions Min -0.999668 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.66409e-06 +time/evaluation sampling (s) 5.55558 +time/exploration sampling (s) 7.15032 +time/logging (s) 0.0113958 +time/saving (s) 0.0180679 +time/training (s) 19.3129 +time/epoch (s) 32.0483 +time/total (s) 12575.3 +Epoch -453 +------------------------------ ---------------- +2022-05-15 21:32:35.303052 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -452 finished +------------------------------ ---------------- +epoch -452 +replay_buffer/size 999047 +trainer/num train calls 549000 +trainer/QF1 Loss 0.874001 +trainer/QF2 Loss 1.06727 +trainer/Policy Loss 18.8502 +trainer/Q1 Predictions Mean -72.3925 +trainer/Q1 Predictions Std 18.8628 +trainer/Q1 Predictions Max -1.98529 +trainer/Q1 Predictions Min -86.5407 +trainer/Q2 Predictions Mean -72.499 +trainer/Q2 Predictions Std 18.8742 +trainer/Q2 Predictions Max -1.11885 +trainer/Q2 Predictions Min -86.5805 +trainer/Q Targets Mean -72.2092 +trainer/Q Targets Std 19.026 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0121 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0375493 +trainer/policy/mean Std 0.721654 +trainer/policy/mean Max 0.999287 +trainer/policy/mean Min -0.9992 +trainer/policy/std Mean 0.417731 +trainer/policy/std Std 0.0192086 +trainer/policy/std Max 0.436478 +trainer/policy/std Min 0.386843 +trainer/Advantage Weights Mean 4.30244 +trainer/Advantage Weights Std 18.2238 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.4578e-14 +trainer/Advantage Score Mean -0.409648 +trainer/Advantage Score Std 0.515824 +trainer/Advantage Score Max 0.950102 +trainer/Advantage Score Min -3.18593 +trainer/V1 Predictions Mean -71.9805 +trainer/V1 Predictions Std 19.051 +trainer/V1 Predictions Max -0.787125 +trainer/V1 Predictions Min -85.8726 +trainer/VF Loss 0.0569507 +expl/num steps total 549000 +expl/num paths total 689 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.093643 +expl/Actions Std 0.844695 +expl/Actions Max 2.56753 +expl/Actions Min -2.51541 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 520038 +eval/num paths total 552 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.126989 +eval/Actions Std 0.733764 +eval/Actions Max 0.999871 +eval/Actions Min -0.999602 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.59983e-05 +time/evaluation sampling (s) 5.22544 +time/exploration sampling (s) 7.29233 +time/logging (s) 0.0112844 +time/saving (s) 0.0195677 +time/training (s) 19.5483 +time/epoch (s) 32.0969 +time/total (s) 12607.4 +Epoch -452 +------------------------------ ---------------- +2022-05-15 21:33:07.342657 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -451 finished +------------------------------ ---------------- +epoch -451 +replay_buffer/size 999047 +trainer/num train calls 550000 +trainer/QF1 Loss 1.85896 +trainer/QF2 Loss 1.79348 +trainer/Policy Loss 14.1323 +trainer/Q1 Predictions Mean -73.8541 +trainer/Q1 Predictions Std 17.4622 +trainer/Q1 Predictions Max -0.198428 +trainer/Q1 Predictions Min -86.4975 +trainer/Q2 Predictions Mean -73.9693 +trainer/Q2 Predictions Std 17.3418 +trainer/Q2 Predictions Max -0.419085 +trainer/Q2 Predictions Min -87.0094 +trainer/Q Targets Mean -73.7303 +trainer/Q Targets Std 17.4465 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1123 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0184377 +trainer/policy/mean Std 0.731765 +trainer/policy/mean Max 0.998734 +trainer/policy/mean Min -0.999563 +trainer/policy/std Mean 0.419429 +trainer/policy/std Std 0.0198335 +trainer/policy/std Max 0.440896 +trainer/policy/std Min 0.387373 +trainer/Advantage Weights Mean 3.43569 +trainer/Advantage Weights Std 15.6796 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.82332e-23 +trainer/Advantage Score Mean -0.44203 +trainer/Advantage Score Std 0.652436 +trainer/Advantage Score Max 2.2721 +trainer/Advantage Score Min -5.1386 +trainer/V1 Predictions Mean -73.6465 +trainer/V1 Predictions Std 17.3474 +trainer/V1 Predictions Max 0.684357 +trainer/V1 Predictions Min -86.3878 +trainer/VF Loss 0.102072 +expl/num steps total 550000 +expl/num paths total 690 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0691648 +expl/Actions Std 0.827551 +expl/Actions Max 2.37398 +expl/Actions Min -2.32139 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 521038 +eval/num paths total 553 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00519493 +eval/Actions Std 0.736729 +eval/Actions Max 0.999608 +eval/Actions Min -0.999575 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.37058e-05 +time/evaluation sampling (s) 5.17405 +time/exploration sampling (s) 6.9072 +time/logging (s) 0.0115673 +time/saving (s) 0.0170066 +time/training (s) 19.9152 +time/epoch (s) 32.0251 +time/total (s) 12639.5 +Epoch -451 +------------------------------ ---------------- +2022-05-15 21:33:39.335562 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -450 finished +------------------------------ ---------------- +epoch -450 +replay_buffer/size 999047 +trainer/num train calls 551000 +trainer/QF1 Loss 0.875849 +trainer/QF2 Loss 0.920188 +trainer/Policy Loss 24.2825 +trainer/Q1 Predictions Mean -72.3741 +trainer/Q1 Predictions Std 19.4143 +trainer/Q1 Predictions Max -0.545388 +trainer/Q1 Predictions Min -86.8448 +trainer/Q2 Predictions Mean -72.3503 +trainer/Q2 Predictions Std 19.4017 +trainer/Q2 Predictions Max -0.39065 +trainer/Q2 Predictions Min -87.1555 +trainer/Q Targets Mean -72.1873 +trainer/Q Targets Std 19.6663 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5243 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0132029 +trainer/policy/mean Std 0.712719 +trainer/policy/mean Max 0.997822 +trainer/policy/mean Min -0.998519 +trainer/policy/std Mean 0.420344 +trainer/policy/std Std 0.0202393 +trainer/policy/std Max 0.442933 +trainer/policy/std Min 0.386917 +trainer/Advantage Weights Mean 4.86818 +trainer/Advantage Weights Std 17.7051 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.53722e-19 +trainer/Advantage Score Mean -0.345282 +trainer/Advantage Score Std 0.590201 +trainer/Advantage Score Max 0.911881 +trainer/Advantage Score Min -4.18716 +trainer/V1 Predictions Mean -71.9221 +trainer/V1 Predictions Std 19.6984 +trainer/V1 Predictions Max 0.107105 +trainer/V1 Predictions Min -87.3611 +trainer/VF Loss 0.0642202 +expl/num steps total 551000 +expl/num paths total 692 +expl/path length Mean 500 +expl/path length Std 13 +expl/path length Max 513 +expl/path length Min 487 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0239498 +expl/Actions Std 0.846164 +expl/Actions Max 2.55975 +expl/Actions Min -2.40423 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 521525 +eval/num paths total 554 +eval/path length Mean 487 +eval/path length Std 0 +eval/path length Max 487 +eval/path length Min 487 +eval/Rewards Mean 0.00205339 +eval/Rewards Std 0.0452678 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0397339 +eval/Actions Std 0.734145 +eval/Actions Max 0.999442 +eval/Actions Min -0.999492 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.09579e-05 +time/evaluation sampling (s) 5.06858 +time/exploration sampling (s) 7.45009 +time/logging (s) 0.010086 +time/saving (s) 0.0325918 +time/training (s) 19.4151 +time/epoch (s) 31.9764 +time/total (s) 12671.5 +Epoch -450 +------------------------------ ---------------- +2022-05-15 21:34:12.577813 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -449 finished +------------------------------ ---------------- +epoch -449 +replay_buffer/size 999047 +trainer/num train calls 552000 +trainer/QF1 Loss 0.987049 +trainer/QF2 Loss 1.04772 +trainer/Policy Loss 14.1781 +trainer/Q1 Predictions Mean -72.8021 +trainer/Q1 Predictions Std 18.6696 +trainer/Q1 Predictions Max -0.658406 +trainer/Q1 Predictions Min -88.6871 +trainer/Q2 Predictions Mean -72.9128 +trainer/Q2 Predictions Std 18.6389 +trainer/Q2 Predictions Max 0.523399 +trainer/Q2 Predictions Min -88.5056 +trainer/Q Targets Mean -72.6815 +trainer/Q Targets Std 18.8537 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4468 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00510938 +trainer/policy/mean Std 0.718745 +trainer/policy/mean Max 0.998846 +trainer/policy/mean Min -0.999114 +trainer/policy/std Mean 0.419632 +trainer/policy/std Std 0.0204597 +trainer/policy/std Max 0.440595 +trainer/policy/std Min 0.384734 +trainer/Advantage Weights Mean 3.83837 +trainer/Advantage Weights Std 16.5513 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.13164e-25 +trainer/Advantage Score Mean -0.465477 +trainer/Advantage Score Std 0.759678 +trainer/Advantage Score Max 0.891075 +trainer/Advantage Score Min -5.54689 +trainer/V1 Predictions Mean -72.3694 +trainer/V1 Predictions Std 19.1018 +trainer/V1 Predictions Max 0.55189 +trainer/V1 Predictions Min -88.0866 +trainer/VF Loss 0.0905693 +expl/num steps total 552000 +expl/num paths total 694 +expl/path length Mean 500 +expl/path length Std 366 +expl/path length Max 866 +expl/path length Min 134 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0359481 +expl/Actions Std 0.815277 +expl/Actions Max 2.28049 +expl/Actions Min -2.41137 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 522525 +eval/num paths total 555 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00977075 +eval/Actions Std 0.735941 +eval/Actions Max 0.999782 +eval/Actions Min -0.999461 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.29277e-05 +time/evaluation sampling (s) 5.32175 +time/exploration sampling (s) 7.88273 +time/logging (s) 0.0120619 +time/saving (s) 0.0164751 +time/training (s) 19.9948 +time/epoch (s) 33.2278 +time/total (s) 12704.7 +Epoch -449 +------------------------------ ---------------- +2022-05-15 21:34:45.269084 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -448 finished +------------------------------ ---------------- +epoch -448 +replay_buffer/size 999047 +trainer/num train calls 553000 +trainer/QF1 Loss 0.694686 +trainer/QF2 Loss 0.635087 +trainer/Policy Loss 14.9094 +trainer/Q1 Predictions Mean -72.422 +trainer/Q1 Predictions Std 18.5874 +trainer/Q1 Predictions Max -2.65298 +trainer/Q1 Predictions Min -86.2438 +trainer/Q2 Predictions Mean -72.5038 +trainer/Q2 Predictions Std 18.6015 +trainer/Q2 Predictions Max -2.18238 +trainer/Q2 Predictions Min -86.379 +trainer/Q Targets Mean -72.5659 +trainer/Q Targets Std 18.9048 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5738 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00569514 +trainer/policy/mean Std 0.727826 +trainer/policy/mean Max 0.999536 +trainer/policy/mean Min -0.999476 +trainer/policy/std Mean 0.419484 +trainer/policy/std Std 0.0218785 +trainer/policy/std Max 0.443649 +trainer/policy/std Min 0.3838 +trainer/Advantage Weights Mean 4.94892 +trainer/Advantage Weights Std 18.8703 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.63032e-11 +trainer/Advantage Score Mean -0.348166 +trainer/Advantage Score Std 0.517407 +trainer/Advantage Score Max 1.44243 +trainer/Advantage Score Min -2.40391 +trainer/V1 Predictions Mean -72.3467 +trainer/V1 Predictions Std 19.0214 +trainer/V1 Predictions Max -0.326826 +trainer/V1 Predictions Min -86.4929 +trainer/VF Loss 0.064089 +expl/num steps total 553000 +expl/num paths total 695 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0332872 +expl/Actions Std 0.815707 +expl/Actions Max 2.29738 +expl/Actions Min -2.42963 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 523525 +eval/num paths total 556 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.184598 +eval/Actions Std 0.795042 +eval/Actions Max 0.999843 +eval/Actions Min -0.999765 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.70485e-06 +time/evaluation sampling (s) 6.03798 +time/exploration sampling (s) 6.87484 +time/logging (s) 0.00743691 +time/saving (s) 0.0157783 +time/training (s) 19.7337 +time/epoch (s) 32.6697 +time/total (s) 12737.4 +Epoch -448 +------------------------------ ---------------- +2022-05-15 21:35:16.278543 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -447 finished +------------------------------ ---------------- +epoch -447 +replay_buffer/size 999047 +trainer/num train calls 554000 +trainer/QF1 Loss 1.00345 +trainer/QF2 Loss 1.105 +trainer/Policy Loss 32.0805 +trainer/Q1 Predictions Mean -71.1679 +trainer/Q1 Predictions Std 19.3003 +trainer/Q1 Predictions Max 0.642883 +trainer/Q1 Predictions Min -85.9227 +trainer/Q2 Predictions Mean -71.1642 +trainer/Q2 Predictions Std 19.3254 +trainer/Q2 Predictions Max -0.615832 +trainer/Q2 Predictions Min -85.8714 +trainer/Q Targets Mean -71.4059 +trainer/Q Targets Std 18.8682 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8954 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0523906 +trainer/policy/mean Std 0.731024 +trainer/policy/mean Max 0.997186 +trainer/policy/mean Min -0.99951 +trainer/policy/std Mean 0.41851 +trainer/policy/std Std 0.0214906 +trainer/policy/std Max 0.442409 +trainer/policy/std Min 0.384303 +trainer/Advantage Weights Mean 7.00016 +trainer/Advantage Weights Std 24.0388 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.63291e-13 +trainer/Advantage Score Mean -0.360757 +trainer/Advantage Score Std 0.600608 +trainer/Advantage Score Max 2.62973 +trainer/Advantage Score Min -2.84004 +trainer/V1 Predictions Mean -71.1957 +trainer/V1 Predictions Std 18.9287 +trainer/V1 Predictions Max -1.15593 +trainer/V1 Predictions Min -85.9546 +trainer/VF Loss 0.12899 +expl/num steps total 554000 +expl/num paths total 697 +expl/path length Mean 500 +expl/path length Std 450 +expl/path length Max 950 +expl/path length Min 50 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0377766 +expl/Actions Std 0.841035 +expl/Actions Max 2.27891 +expl/Actions Min -2.30658 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 524525 +eval/num paths total 557 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.120738 +eval/Actions Std 0.729499 +eval/Actions Max 0.999698 +eval/Actions Min -0.999563 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.88268e-06 +time/evaluation sampling (s) 5.21652 +time/exploration sampling (s) 6.94167 +time/logging (s) 0.0107711 +time/saving (s) 0.0158554 +time/training (s) 18.8161 +time/epoch (s) 31.0009 +time/total (s) 12768.4 +Epoch -447 +------------------------------ ---------------- +2022-05-15 21:35:48.816851 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -446 finished +------------------------------ ---------------- +epoch -446 +replay_buffer/size 999047 +trainer/num train calls 555000 +trainer/QF1 Loss 0.972573 +trainer/QF2 Loss 0.990927 +trainer/Policy Loss 10.9765 +trainer/Q1 Predictions Mean -73.7583 +trainer/Q1 Predictions Std 17.4353 +trainer/Q1 Predictions Max -2.24378 +trainer/Q1 Predictions Min -87.4089 +trainer/Q2 Predictions Mean -73.6663 +trainer/Q2 Predictions Std 17.4505 +trainer/Q2 Predictions Max -1.6728 +trainer/Q2 Predictions Min -87.3979 +trainer/Q Targets Mean -73.6376 +trainer/Q Targets Std 17.7943 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5803 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0160408 +trainer/policy/mean Std 0.718324 +trainer/policy/mean Max 0.998463 +trainer/policy/mean Min -0.998973 +trainer/policy/std Mean 0.41895 +trainer/policy/std Std 0.0212283 +trainer/policy/std Max 0.440076 +trainer/policy/std Min 0.38694 +trainer/Advantage Weights Mean 3.53624 +trainer/Advantage Weights Std 14.9688 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.36796e-13 +trainer/Advantage Score Mean -0.378937 +trainer/Advantage Score Std 0.590369 +trainer/Advantage Score Max 1.07461 +trainer/Advantage Score Min -2.90716 +trainer/V1 Predictions Mean -73.3637 +trainer/V1 Predictions Std 17.8391 +trainer/V1 Predictions Max -0.697748 +trainer/V1 Predictions Min -87.4628 +trainer/VF Loss 0.0637139 +expl/num steps total 555000 +expl/num paths total 699 +expl/path length Mean 500 +expl/path length Std 181 +expl/path length Max 681 +expl/path length Min 319 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0117077 +expl/Actions Std 0.827888 +expl/Actions Max 2.41548 +expl/Actions Min -2.39362 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 525182 +eval/num paths total 558 +eval/path length Mean 657 +eval/path length Std 0 +eval/path length Max 657 +eval/path length Min 657 +eval/Rewards Mean 0.00152207 +eval/Rewards Std 0.038984 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0182575 +eval/Actions Std 0.738181 +eval/Actions Max 0.999279 +eval/Actions Min -0.999363 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2402e-05 +time/evaluation sampling (s) 5.43305 +time/exploration sampling (s) 7.84221 +time/logging (s) 0.00934941 +time/saving (s) 0.0166425 +time/training (s) 19.2212 +time/epoch (s) 32.5225 +time/total (s) 12800.9 +Epoch -446 +------------------------------ ---------------- +2022-05-15 21:36:20.431650 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -445 finished +------------------------------ ---------------- +epoch -445 +replay_buffer/size 999047 +trainer/num train calls 556000 +trainer/QF1 Loss 1.34899 +trainer/QF2 Loss 1.18474 +trainer/Policy Loss 0.325707 +trainer/Q1 Predictions Mean -73.1789 +trainer/Q1 Predictions Std 17.8006 +trainer/Q1 Predictions Max -0.915842 +trainer/Q1 Predictions Min -87.2057 +trainer/Q2 Predictions Mean -73.1241 +trainer/Q2 Predictions Std 17.7081 +trainer/Q2 Predictions Max -0.890784 +trainer/Q2 Predictions Min -86.9626 +trainer/Q Targets Mean -72.6667 +trainer/Q Targets Std 17.867 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3017 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0192377 +trainer/policy/mean Std 0.7287 +trainer/policy/mean Max 0.998287 +trainer/policy/mean Min -0.998873 +trainer/policy/std Mean 0.418568 +trainer/policy/std Std 0.0216109 +trainer/policy/std Max 0.439839 +trainer/policy/std Min 0.384526 +trainer/Advantage Weights Mean 0.150589 +trainer/Advantage Weights Std 1.11493 +trainer/Advantage Weights Max 10.8618 +trainer/Advantage Weights Min 1.58445e-19 +trainer/Advantage Score Mean -0.848466 +trainer/Advantage Score Std 0.609949 +trainer/Advantage Score Max 0.238525 +trainer/Advantage Score Min -4.32889 +trainer/V1 Predictions Mean -72.4055 +trainer/V1 Predictions Std 18.0434 +trainer/V1 Predictions Max 0.0494189 +trainer/V1 Predictions Min -86.0742 +trainer/VF Loss 0.109744 +expl/num steps total 556000 +expl/num paths total 701 +expl/path length Mean 500 +expl/path length Std 185 +expl/path length Max 685 +expl/path length Min 315 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0147167 +expl/Actions Std 0.863061 +expl/Actions Max 2.44414 +expl/Actions Min -2.31527 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 526031 +eval/num paths total 559 +eval/path length Mean 849 +eval/path length Std 0 +eval/path length Max 849 +eval/path length Min 849 +eval/Rewards Mean 0.00117786 +eval/Rewards Std 0.0342997 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0216777 +eval/Actions Std 0.728484 +eval/Actions Max 0.999938 +eval/Actions Min -0.999022 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.72534e-06 +time/evaluation sampling (s) 4.96782 +time/exploration sampling (s) 7.34732 +time/logging (s) 0.0106175 +time/saving (s) 0.0159399 +time/training (s) 19.2593 +time/epoch (s) 31.601 +time/total (s) 12832.5 +Epoch -445 +------------------------------ ---------------- +2022-05-15 21:36:51.799343 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -444 finished +------------------------------ ---------------- +epoch -444 +replay_buffer/size 999047 +trainer/num train calls 557000 +trainer/QF1 Loss 0.91229 +trainer/QF2 Loss 0.66032 +trainer/Policy Loss 15.325 +trainer/Q1 Predictions Mean -72.12 +trainer/Q1 Predictions Std 19.554 +trainer/Q1 Predictions Max 0.542935 +trainer/Q1 Predictions Min -87.35 +trainer/Q2 Predictions Mean -72.1393 +trainer/Q2 Predictions Std 19.5266 +trainer/Q2 Predictions Max -0.0432487 +trainer/Q2 Predictions Min -87.2252 +trainer/Q Targets Mean -72.1171 +trainer/Q Targets Std 19.4156 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7916 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0020989 +trainer/policy/mean Std 0.727726 +trainer/policy/mean Max 0.998204 +trainer/policy/mean Min -0.999775 +trainer/policy/std Mean 0.419516 +trainer/policy/std Std 0.0207559 +trainer/policy/std Max 0.43981 +trainer/policy/std Min 0.38587 +trainer/Advantage Weights Mean 2.79924 +trainer/Advantage Weights Std 14.3142 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.9349e-20 +trainer/Advantage Score Mean -0.484801 +trainer/Advantage Score Std 0.606207 +trainer/Advantage Score Max 1.00539 +trainer/Advantage Score Min -4.46818 +trainer/V1 Predictions Mean -71.7982 +trainer/V1 Predictions Std 19.5879 +trainer/V1 Predictions Max 0.451948 +trainer/V1 Predictions Min -86.7292 +trainer/VF Loss 0.0704674 +expl/num steps total 557000 +expl/num paths total 702 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.079497 +expl/Actions Std 0.819151 +expl/Actions Max 2.45331 +expl/Actions Min -2.36074 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 527031 +eval/num paths total 560 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0365783 +eval/Actions Std 0.735494 +eval/Actions Max 0.999691 +eval/Actions Min -0.999151 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.45288e-06 +time/evaluation sampling (s) 5.07362 +time/exploration sampling (s) 7.20828 +time/logging (s) 0.0121369 +time/saving (s) 0.0184428 +time/training (s) 19.042 +time/epoch (s) 31.3545 +time/total (s) 12863.9 +Epoch -444 +------------------------------ ---------------- +2022-05-15 21:37:24.408551 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -443 finished +------------------------------ ---------------- +epoch -443 +replay_buffer/size 999047 +trainer/num train calls 558000 +trainer/QF1 Loss 0.643416 +trainer/QF2 Loss 0.607443 +trainer/Policy Loss 13.5647 +trainer/Q1 Predictions Mean -74.2604 +trainer/Q1 Predictions Std 16.0594 +trainer/Q1 Predictions Max 0.282326 +trainer/Q1 Predictions Min -86.7083 +trainer/Q2 Predictions Mean -74.4154 +trainer/Q2 Predictions Std 16.083 +trainer/Q2 Predictions Max -0.349351 +trainer/Q2 Predictions Min -86.8829 +trainer/Q Targets Mean -74.5007 +trainer/Q Targets Std 16.1144 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7876 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0124872 +trainer/policy/mean Std 0.722806 +trainer/policy/mean Max 0.998878 +trainer/policy/mean Min -0.999576 +trainer/policy/std Mean 0.419713 +trainer/policy/std Std 0.0205158 +trainer/policy/std Max 0.442759 +trainer/policy/std Min 0.388112 +trainer/Advantage Weights Mean 2.00493 +trainer/Advantage Weights Std 12.2328 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.06604e-22 +trainer/Advantage Score Mean -0.551672 +trainer/Advantage Score Std 0.592116 +trainer/Advantage Score Max 1.1409 +trainer/Advantage Score Min -4.87016 +trainer/V1 Predictions Mean -74.2221 +trainer/V1 Predictions Std 16.295 +trainer/V1 Predictions Max 0.327002 +trainer/V1 Predictions Min -86.8369 +trainer/VF Loss 0.0740035 +expl/num steps total 558000 +expl/num paths total 704 +expl/path length Mean 500 +expl/path length Std 142 +expl/path length Max 642 +expl/path length Min 358 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0299623 +expl/Actions Std 0.840238 +expl/Actions Max 2.21328 +expl/Actions Min -2.47589 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 528031 +eval/num paths total 561 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.04251 +eval/Actions Std 0.717764 +eval/Actions Max 0.998695 +eval/Actions Min -0.999073 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.15696e-06 +time/evaluation sampling (s) 5.37774 +time/exploration sampling (s) 7.4932 +time/logging (s) 0.0112411 +time/saving (s) 0.0117151 +time/training (s) 19.697 +time/epoch (s) 32.5909 +time/total (s) 12896.5 +Epoch -443 +------------------------------ ---------------- +2022-05-15 21:37:55.823026 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -442 finished +------------------------------ ---------------- +epoch -442 +replay_buffer/size 999047 +trainer/num train calls 559000 +trainer/QF1 Loss 0.531634 +trainer/QF2 Loss 0.567871 +trainer/Policy Loss 21.54 +trainer/Q1 Predictions Mean -71.195 +trainer/Q1 Predictions Std 20.2595 +trainer/Q1 Predictions Max -0.0319481 +trainer/Q1 Predictions Min -86.0394 +trainer/Q2 Predictions Mean -71.2019 +trainer/Q2 Predictions Std 20.1953 +trainer/Q2 Predictions Max -0.33642 +trainer/Q2 Predictions Min -86.6312 +trainer/Q Targets Mean -71.4775 +trainer/Q Targets Std 20.1564 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3401 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0221874 +trainer/policy/mean Std 0.725979 +trainer/policy/mean Max 0.998851 +trainer/policy/mean Min -0.999481 +trainer/policy/std Mean 0.417225 +trainer/policy/std Std 0.0198627 +trainer/policy/std Max 0.439244 +trainer/policy/std Min 0.383388 +trainer/Advantage Weights Mean 5.44631 +trainer/Advantage Weights Std 16.8727 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.14943e-13 +trainer/Advantage Score Mean -0.229464 +trainer/Advantage Score Std 0.473938 +trainer/Advantage Score Max 1.05591 +trainer/Advantage Score Min -2.81172 +trainer/V1 Predictions Mean -71.1915 +trainer/V1 Predictions Std 20.3549 +trainer/V1 Predictions Max 0.718703 +trainer/V1 Predictions Min -86.1088 +trainer/VF Loss 0.0455478 +expl/num steps total 559000 +expl/num paths total 705 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.038496 +expl/Actions Std 0.804698 +expl/Actions Max 2.3672 +expl/Actions Min -2.79778 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 529031 +eval/num paths total 562 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0241322 +eval/Actions Std 0.735372 +eval/Actions Max 0.99994 +eval/Actions Min -0.999643 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.99099e-06 +time/evaluation sampling (s) 4.93711 +time/exploration sampling (s) 7.21402 +time/logging (s) 0.0106412 +time/saving (s) 0.0168934 +time/training (s) 19.2186 +time/epoch (s) 31.3973 +time/total (s) 12927.9 +Epoch -442 +------------------------------ ---------------- +2022-05-15 21:38:27.669606 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -441 finished +------------------------------ ---------------- +epoch -441 +replay_buffer/size 999047 +trainer/num train calls 560000 +trainer/QF1 Loss 8.01109 +trainer/QF2 Loss 7.91007 +trainer/Policy Loss 24.8003 +trainer/Q1 Predictions Mean -72.2583 +trainer/Q1 Predictions Std 18.7715 +trainer/Q1 Predictions Max -0.409063 +trainer/Q1 Predictions Min -88.9809 +trainer/Q2 Predictions Mean -72.2177 +trainer/Q2 Predictions Std 18.9051 +trainer/Q2 Predictions Max -1.06028 +trainer/Q2 Predictions Min -89.1048 +trainer/Q Targets Mean -72.3031 +trainer/Q Targets Std 18.8395 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.8912 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0123735 +trainer/policy/mean Std 0.722636 +trainer/policy/mean Max 0.999289 +trainer/policy/mean Min -0.998546 +trainer/policy/std Mean 0.418225 +trainer/policy/std Std 0.0213632 +trainer/policy/std Max 0.441084 +trainer/policy/std Min 0.383844 +trainer/Advantage Weights Mean 5.08241 +trainer/Advantage Weights Std 19.3327 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.06078e-25 +trainer/Advantage Score Mean -0.437589 +trainer/Advantage Score Std 0.662585 +trainer/Advantage Score Max 1.39924 +trainer/Advantage Score Min -5.59431 +trainer/V1 Predictions Mean -72.2058 +trainer/V1 Predictions Std 18.8924 +trainer/V1 Predictions Max -1.06659 +trainer/V1 Predictions Min -88.0349 +trainer/VF Loss 0.0837207 +expl/num steps total 560000 +expl/num paths total 707 +expl/path length Mean 500 +expl/path length Std 182 +expl/path length Max 682 +expl/path length Min 318 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0295096 +expl/Actions Std 0.826932 +expl/Actions Max 2.83834 +expl/Actions Min -2.43122 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 529805 +eval/num paths total 563 +eval/path length Mean 774 +eval/path length Std 0 +eval/path length Max 774 +eval/path length Min 774 +eval/Rewards Mean 0.00129199 +eval/Rewards Std 0.035921 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0286287 +eval/Actions Std 0.717522 +eval/Actions Max 0.999758 +eval/Actions Min -0.999477 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.50399e-05 +time/evaluation sampling (s) 5.14805 +time/exploration sampling (s) 7.13682 +time/logging (s) 0.0100918 +time/saving (s) 0.0155288 +time/training (s) 19.5186 +time/epoch (s) 31.8291 +time/total (s) 12959.7 +Epoch -441 +------------------------------ ---------------- +2022-05-15 21:39:00.004348 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -440 finished +------------------------------ ---------------- +epoch -440 +replay_buffer/size 999047 +trainer/num train calls 561000 +trainer/QF1 Loss 0.943703 +trainer/QF2 Loss 0.915275 +trainer/Policy Loss 8.8739 +trainer/Q1 Predictions Mean -73.3665 +trainer/Q1 Predictions Std 17.7968 +trainer/Q1 Predictions Max -1.5909 +trainer/Q1 Predictions Min -87.4287 +trainer/Q2 Predictions Mean -73.4608 +trainer/Q2 Predictions Std 17.7565 +trainer/Q2 Predictions Max -0.635291 +trainer/Q2 Predictions Min -87.2142 +trainer/Q Targets Mean -73.2207 +trainer/Q Targets Std 17.6414 +trainer/Q Targets Max -1.82051 +trainer/Q Targets Min -86.8777 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00989125 +trainer/policy/mean Std 0.717696 +trainer/policy/mean Max 0.999692 +trainer/policy/mean Min -0.997914 +trainer/policy/std Mean 0.419978 +trainer/policy/std Std 0.0199953 +trainer/policy/std Max 0.440441 +trainer/policy/std Min 0.388456 +trainer/Advantage Weights Mean 2.34805 +trainer/Advantage Weights Std 13.5069 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.37262e-12 +trainer/Advantage Score Mean -0.469593 +trainer/Advantage Score Std 0.496946 +trainer/Advantage Score Max 1.41585 +trainer/Advantage Score Min -2.73143 +trainer/V1 Predictions Mean -72.9785 +trainer/V1 Predictions Std 17.8062 +trainer/V1 Predictions Max -0.0155473 +trainer/V1 Predictions Min -86.7436 +trainer/VF Loss 0.0593548 +expl/num steps total 561000 +expl/num paths total 709 +expl/path length Mean 500 +expl/path length Std 394 +expl/path length Max 894 +expl/path length Min 106 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0316459 +expl/Actions Std 0.812762 +expl/Actions Max 2.81684 +expl/Actions Min -2.28303 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 530805 +eval/num paths total 564 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.308991 +eval/Actions Std 0.732407 +eval/Actions Max 0.999321 +eval/Actions Min -0.999554 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27302e-05 +time/evaluation sampling (s) 5.37065 +time/exploration sampling (s) 7.69749 +time/logging (s) 0.0115786 +time/saving (s) 0.0156324 +time/training (s) 19.2266 +time/epoch (s) 32.3219 +time/total (s) 12992 +Epoch -440 +------------------------------ ---------------- +2022-05-15 21:39:32.211598 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -439 finished +------------------------------ ---------------- +epoch -439 +replay_buffer/size 999047 +trainer/num train calls 562000 +trainer/QF1 Loss 0.88513 +trainer/QF2 Loss 0.814451 +trainer/Policy Loss 26.3569 +trainer/Q1 Predictions Mean -72.4692 +trainer/Q1 Predictions Std 17.998 +trainer/Q1 Predictions Max -0.524838 +trainer/Q1 Predictions Min -87.452 +trainer/Q2 Predictions Mean -72.486 +trainer/Q2 Predictions Std 18.0264 +trainer/Q2 Predictions Max 0.243943 +trainer/Q2 Predictions Min -87.4855 +trainer/Q Targets Mean -72.6594 +trainer/Q Targets Std 18.2025 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0999 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00515604 +trainer/policy/mean Std 0.721884 +trainer/policy/mean Max 0.997586 +trainer/policy/mean Min -0.999802 +trainer/policy/std Mean 0.418913 +trainer/policy/std Std 0.0192682 +trainer/policy/std Max 0.440382 +trainer/policy/std Min 0.390104 +trainer/Advantage Weights Mean 6.03689 +trainer/Advantage Weights Std 18.9385 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.29254e-18 +trainer/Advantage Score Mean -0.369881 +trainer/Advantage Score Std 0.712106 +trainer/Advantage Score Max 0.914116 +trainer/Advantage Score Min -3.92173 +trainer/V1 Predictions Mean -72.334 +trainer/V1 Predictions Std 18.4282 +trainer/V1 Predictions Max 0.882519 +trainer/V1 Predictions Min -87.0356 +trainer/VF Loss 0.0842498 +expl/num steps total 562000 +expl/num paths total 710 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.031142 +expl/Actions Std 0.811361 +expl/Actions Max 2.26296 +expl/Actions Min -2.3608 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 531512 +eval/num paths total 565 +eval/path length Mean 707 +eval/path length Std 0 +eval/path length Max 707 +eval/path length Min 707 +eval/Rewards Mean 0.00141443 +eval/Rewards Std 0.0375823 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0366639 +eval/Actions Std 0.747406 +eval/Actions Max 0.999483 +eval/Actions Min -0.999283 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.35835e-06 +time/evaluation sampling (s) 5.07106 +time/exploration sampling (s) 7.02111 +time/logging (s) 0.00993817 +time/saving (s) 0.0154104 +time/training (s) 20.0734 +time/epoch (s) 32.191 +time/total (s) 13024.2 +Epoch -439 +------------------------------ ---------------- +2022-05-15 21:40:04.537673 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -438 finished +------------------------------ ---------------- +epoch -438 +replay_buffer/size 999047 +trainer/num train calls 563000 +trainer/QF1 Loss 0.738741 +trainer/QF2 Loss 0.860942 +trainer/Policy Loss 1.30509 +trainer/Q1 Predictions Mean -74.1962 +trainer/Q1 Predictions Std 16.106 +trainer/Q1 Predictions Max -3.09174 +trainer/Q1 Predictions Min -87.0011 +trainer/Q2 Predictions Mean -74.3075 +trainer/Q2 Predictions Std 16.1784 +trainer/Q2 Predictions Max -3.04774 +trainer/Q2 Predictions Min -86.9202 +trainer/Q Targets Mean -73.93 +trainer/Q Targets Std 15.9713 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1148 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00378069 +trainer/policy/mean Std 0.720637 +trainer/policy/mean Max 0.999576 +trainer/policy/mean Min -0.99904 +trainer/policy/std Mean 0.418657 +trainer/policy/std Std 0.0200539 +trainer/policy/std Max 0.439894 +trainer/policy/std Min 0.386091 +trainer/Advantage Weights Mean 0.318768 +trainer/Advantage Weights Std 2.08249 +trainer/Advantage Weights Max 30.638 +trainer/Advantage Weights Min 4.44542e-30 +trainer/Advantage Score Mean -0.628893 +trainer/Advantage Score Std 0.712965 +trainer/Advantage Score Max 0.342224 +trainer/Advantage Score Min -6.75857 +trainer/V1 Predictions Mean -73.6018 +trainer/V1 Predictions Std 16.2257 +trainer/V1 Predictions Max -0.841979 +trainer/V1 Predictions Min -86.94 +trainer/VF Loss 0.091243 +expl/num steps total 563000 +expl/num paths total 711 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0446199 +expl/Actions Std 0.828151 +expl/Actions Max 2.35877 +expl/Actions Min -2.43087 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 532512 +eval/num paths total 566 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.210475 +eval/Actions Std 0.644465 +eval/Actions Max 0.999554 +eval/Actions Min -0.999246 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0876e-05 +time/evaluation sampling (s) 5.43113 +time/exploration sampling (s) 6.95328 +time/logging (s) 0.01132 +time/saving (s) 0.0158568 +time/training (s) 19.9002 +time/epoch (s) 32.3118 +time/total (s) 13056.5 +Epoch -438 +------------------------------ ---------------- +2022-05-15 21:40:36.586186 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -437 finished +------------------------------ ---------------- +epoch -437 +replay_buffer/size 999047 +trainer/num train calls 564000 +trainer/QF1 Loss 0.84309 +trainer/QF2 Loss 0.787516 +trainer/Policy Loss 17.6754 +trainer/Q1 Predictions Mean -71.665 +trainer/Q1 Predictions Std 18.8317 +trainer/Q1 Predictions Max -2.4956 +trainer/Q1 Predictions Min -87.2419 +trainer/Q2 Predictions Mean -71.678 +trainer/Q2 Predictions Std 18.8928 +trainer/Q2 Predictions Max -2.45729 +trainer/Q2 Predictions Min -87.1112 +trainer/Q Targets Mean -71.6322 +trainer/Q Targets Std 18.7639 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2264 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0229583 +trainer/policy/mean Std 0.705721 +trainer/policy/mean Max 0.997821 +trainer/policy/mean Min -0.997527 +trainer/policy/std Mean 0.418691 +trainer/policy/std Std 0.0199843 +trainer/policy/std Max 0.438571 +trainer/policy/std Min 0.386505 +trainer/Advantage Weights Mean 4.24288 +trainer/Advantage Weights Std 17.0772 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.01351e-16 +trainer/Advantage Score Mean -0.366395 +trainer/Advantage Score Std 0.512179 +trainer/Advantage Score Max 1.41866 +trainer/Advantage Score Min -3.52292 +trainer/V1 Predictions Mean -71.4061 +trainer/V1 Predictions Std 18.831 +trainer/V1 Predictions Max -2.7547 +trainer/V1 Predictions Min -87.0745 +trainer/VF Loss 0.0618058 +expl/num steps total 564000 +expl/num paths total 713 +expl/path length Mean 500 +expl/path length Std 122 +expl/path length Max 622 +expl/path length Min 378 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.017488 +expl/Actions Std 0.824063 +expl/Actions Max 2.25315 +expl/Actions Min -2.59446 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 533125 +eval/num paths total 567 +eval/path length Mean 613 +eval/path length Std 0 +eval/path length Max 613 +eval/path length Min 613 +eval/Rewards Mean 0.00163132 +eval/Rewards Std 0.0403567 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0437066 +eval/Actions Std 0.720615 +eval/Actions Max 0.999598 +eval/Actions Min -0.999221 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.2736e-06 +time/evaluation sampling (s) 5.40701 +time/exploration sampling (s) 6.85019 +time/logging (s) 0.0102631 +time/saving (s) 0.0153522 +time/training (s) 19.7498 +time/epoch (s) 32.0327 +time/total (s) 13088.6 +Epoch -437 +------------------------------ ---------------- +2022-05-15 21:41:07.083402 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -436 finished +------------------------------ ---------------- +epoch -436 +replay_buffer/size 999047 +trainer/num train calls 565000 +trainer/QF1 Loss 0.830609 +trainer/QF2 Loss 0.754486 +trainer/Policy Loss 13.1125 +trainer/Q1 Predictions Mean -73.2622 +trainer/Q1 Predictions Std 16.8608 +trainer/Q1 Predictions Max 0.000144094 +trainer/Q1 Predictions Min -86.2034 +trainer/Q2 Predictions Mean -73.2867 +trainer/Q2 Predictions Std 16.8929 +trainer/Q2 Predictions Max -0.956555 +trainer/Q2 Predictions Min -86.4566 +trainer/Q Targets Mean -73.5296 +trainer/Q Targets Std 16.6872 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2429 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0110712 +trainer/policy/mean Std 0.721224 +trainer/policy/mean Max 0.999578 +trainer/policy/mean Min -0.998775 +trainer/policy/std Mean 0.419139 +trainer/policy/std Std 0.0187611 +trainer/policy/std Max 0.440159 +trainer/policy/std Min 0.39 +trainer/Advantage Weights Mean 2.62069 +trainer/Advantage Weights Std 11.7526 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.17463e-12 +trainer/Advantage Score Mean -0.364036 +trainer/Advantage Score Std 0.484902 +trainer/Advantage Score Max 1.02216 +trainer/Advantage Score Min -2.64758 +trainer/V1 Predictions Mean -73.2177 +trainer/V1 Predictions Std 16.8791 +trainer/V1 Predictions Max 0.911501 +trainer/V1 Predictions Min -86.2388 +trainer/VF Loss 0.0460983 +expl/num steps total 565000 +expl/num paths total 714 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.173727 +expl/Actions Std 0.854711 +expl/Actions Max 2.53652 +expl/Actions Min -2.43557 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 534125 +eval/num paths total 568 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0371352 +eval/Actions Std 0.725368 +eval/Actions Max 0.999777 +eval/Actions Min -0.999991 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.88198e-06 +time/evaluation sampling (s) 4.8221 +time/exploration sampling (s) 5.67782 +time/logging (s) 0.00846537 +time/saving (s) 0.0200597 +time/training (s) 19.9538 +time/epoch (s) 30.4822 +time/total (s) 13119.1 +Epoch -436 +------------------------------ ---------------- +2022-05-15 21:41:39.062724 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -435 finished +------------------------------ ---------------- +epoch -435 +replay_buffer/size 999047 +trainer/num train calls 566000 +trainer/QF1 Loss 0.732256 +trainer/QF2 Loss 0.823075 +trainer/Policy Loss 9.23683 +trainer/Q1 Predictions Mean -72.73 +trainer/Q1 Predictions Std 17.8421 +trainer/Q1 Predictions Max -0.525436 +trainer/Q1 Predictions Min -87.2937 +trainer/Q2 Predictions Mean -72.7749 +trainer/Q2 Predictions Std 17.9181 +trainer/Q2 Predictions Max -0.255925 +trainer/Q2 Predictions Min -87.2535 +trainer/Q Targets Mean -72.8099 +trainer/Q Targets Std 17.6896 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1889 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00638091 +trainer/policy/mean Std 0.722741 +trainer/policy/mean Max 0.999589 +trainer/policy/mean Min -0.999239 +trainer/policy/std Mean 0.418907 +trainer/policy/std Std 0.0201098 +trainer/policy/std Max 0.438994 +trainer/policy/std Min 0.386386 +trainer/Advantage Weights Mean 3.41437 +trainer/Advantage Weights Std 14.3742 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.78749e-13 +trainer/Advantage Score Mean -0.264969 +trainer/Advantage Score Std 0.504228 +trainer/Advantage Score Max 1.72792 +trainer/Advantage Score Min -2.77603 +trainer/V1 Predictions Mean -72.46 +trainer/V1 Predictions Std 17.9235 +trainer/V1 Predictions Max 0.144492 +trainer/V1 Predictions Min -87.0661 +trainer/VF Loss 0.0510112 +expl/num steps total 566000 +expl/num paths total 716 +expl/path length Mean 500 +expl/path length Std 131 +expl/path length Max 631 +expl/path length Min 369 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.039306 +expl/Actions Std 0.831803 +expl/Actions Max 2.50487 +expl/Actions Min -2.47594 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 535125 +eval/num paths total 569 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.267396 +eval/Actions Std 0.639053 +eval/Actions Max 0.999871 +eval/Actions Min -0.999518 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.16243e-05 +time/evaluation sampling (s) 4.67574 +time/exploration sampling (s) 6.98124 +time/logging (s) 0.00954604 +time/saving (s) 0.0118915 +time/training (s) 20.2868 +time/epoch (s) 31.9653 +time/total (s) 13151 +Epoch -435 +------------------------------ ---------------- +2022-05-15 21:42:10.154125 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -434 finished +------------------------------ ---------------- +epoch -434 +replay_buffer/size 999047 +trainer/num train calls 567000 +trainer/QF1 Loss 1.20246 +trainer/QF2 Loss 1.30628 +trainer/Policy Loss 21.6704 +trainer/Q1 Predictions Mean -70.9211 +trainer/Q1 Predictions Std 22.0746 +trainer/Q1 Predictions Max -0.885504 +trainer/Q1 Predictions Min -87.8741 +trainer/Q2 Predictions Mean -71.0203 +trainer/Q2 Predictions Std 22.0774 +trainer/Q2 Predictions Max -0.643706 +trainer/Q2 Predictions Min -87.8622 +trainer/Q Targets Mean -70.5841 +trainer/Q Targets Std 22.1988 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1477 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00877229 +trainer/policy/mean Std 0.719163 +trainer/policy/mean Max 0.998464 +trainer/policy/mean Min -0.998051 +trainer/policy/std Mean 0.420479 +trainer/policy/std Std 0.0218917 +trainer/policy/std Max 0.442809 +trainer/policy/std Min 0.384077 +trainer/Advantage Weights Mean 5.40346 +trainer/Advantage Weights Std 19.4102 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.70084e-11 +trainer/Advantage Score Mean -0.356418 +trainer/Advantage Score Std 0.538696 +trainer/Advantage Score Max 1.12049 +trainer/Advantage Score Min -2.3165 +trainer/V1 Predictions Mean -70.4175 +trainer/V1 Predictions Std 22.2243 +trainer/V1 Predictions Max 0.300494 +trainer/V1 Predictions Min -86.7181 +trainer/VF Loss 0.0659661 +expl/num steps total 567000 +expl/num paths total 717 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0488483 +expl/Actions Std 0.800216 +expl/Actions Max 2.27658 +expl/Actions Min -2.27878 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 535458 +eval/num paths total 570 +eval/path length Mean 333 +eval/path length Std 0 +eval/path length Max 333 +eval/path length Min 333 +eval/Rewards Mean 0.003003 +eval/Rewards Std 0.0547173 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0178234 +eval/Actions Std 0.760884 +eval/Actions Max 0.999082 +eval/Actions Min -0.999347 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.27531e-05 +time/evaluation sampling (s) 4.60193 +time/exploration sampling (s) 7.27779 +time/logging (s) 0.00911188 +time/saving (s) 0.0177864 +time/training (s) 19.1716 +time/epoch (s) 31.0783 +time/total (s) 13182.1 +Epoch -434 +------------------------------ ---------------- +2022-05-15 21:42:41.571005 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -433 finished +------------------------------ ---------------- +epoch -433 +replay_buffer/size 999047 +trainer/num train calls 568000 +trainer/QF1 Loss 0.757528 +trainer/QF2 Loss 0.811915 +trainer/Policy Loss 7.74796 +trainer/Q1 Predictions Mean -72.3148 +trainer/Q1 Predictions Std 19.2458 +trainer/Q1 Predictions Max -1.42095 +trainer/Q1 Predictions Min -85.8473 +trainer/Q2 Predictions Mean -72.2822 +trainer/Q2 Predictions Std 19.2081 +trainer/Q2 Predictions Max -1.00426 +trainer/Q2 Predictions Min -85.8784 +trainer/Q Targets Mean -72.3198 +trainer/Q Targets Std 19.2677 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1768 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000821966 +trainer/policy/mean Std 0.728666 +trainer/policy/mean Max 0.998124 +trainer/policy/mean Min -0.998397 +trainer/policy/std Mean 0.418535 +trainer/policy/std Std 0.0219203 +trainer/policy/std Max 0.441715 +trainer/policy/std Min 0.381674 +trainer/Advantage Weights Mean 2.44306 +trainer/Advantage Weights Std 12.6859 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.81071e-18 +trainer/Advantage Score Mean -0.462745 +trainer/Advantage Score Std 0.676072 +trainer/Advantage Score Max 1.03295 +trainer/Advantage Score Min -4.04131 +trainer/V1 Predictions Mean -71.9659 +trainer/V1 Predictions Std 19.6422 +trainer/V1 Predictions Max -0.819464 +trainer/V1 Predictions Min -86.0395 +trainer/VF Loss 0.0758213 +expl/num steps total 568000 +expl/num paths total 718 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.114309 +expl/Actions Std 0.820237 +expl/Actions Max 2.37546 +expl/Actions Min -2.24741 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 536458 +eval/num paths total 571 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.241057 +eval/Actions Std 0.67064 +eval/Actions Max 0.999473 +eval/Actions Min -0.998766 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.7013e-06 +time/evaluation sampling (s) 5.26366 +time/exploration sampling (s) 6.93366 +time/logging (s) 0.00769744 +time/saving (s) 0.0112615 +time/training (s) 19.1832 +time/epoch (s) 31.3995 +time/total (s) 13213.5 +Epoch -433 +------------------------------ ---------------- +2022-05-15 21:43:13.977830 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -432 finished +------------------------------ ---------------- +epoch -432 +replay_buffer/size 999047 +trainer/num train calls 569000 +trainer/QF1 Loss 0.982935 +trainer/QF2 Loss 0.951164 +trainer/Policy Loss 17.7154 +trainer/Q1 Predictions Mean -73.9949 +trainer/Q1 Predictions Std 17.5625 +trainer/Q1 Predictions Max -1.66996 +trainer/Q1 Predictions Min -87.2683 +trainer/Q2 Predictions Mean -73.9756 +trainer/Q2 Predictions Std 17.5664 +trainer/Q2 Predictions Max -1.43792 +trainer/Q2 Predictions Min -87.263 +trainer/Q Targets Mean -73.9101 +trainer/Q Targets Std 17.4581 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3202 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0232555 +trainer/policy/mean Std 0.728026 +trainer/policy/mean Max 0.998724 +trainer/policy/mean Min -0.999655 +trainer/policy/std Mean 0.419659 +trainer/policy/std Std 0.0209772 +trainer/policy/std Max 0.443883 +trainer/policy/std Min 0.387216 +trainer/Advantage Weights Mean 4.83734 +trainer/Advantage Weights Std 19.929 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.46371e-16 +trainer/Advantage Score Mean -0.425272 +trainer/Advantage Score Std 0.617573 +trainer/Advantage Score Max 1.6102 +trainer/Advantage Score Min -3.59397 +trainer/V1 Predictions Mean -73.651 +trainer/V1 Predictions Std 17.4985 +trainer/V1 Predictions Max -0.305586 +trainer/V1 Predictions Min -86.7168 +trainer/VF Loss 0.0858786 +expl/num steps total 569000 +expl/num paths total 720 +expl/path length Mean 500 +expl/path length Std 31 +expl/path length Max 531 +expl/path length Min 469 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0223585 +expl/Actions Std 0.832259 +expl/Actions Max 2.32683 +expl/Actions Min -2.36463 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 537018 +eval/num paths total 572 +eval/path length Mean 560 +eval/path length Std 0 +eval/path length Max 560 +eval/path length Min 560 +eval/Rewards Mean 0.00178571 +eval/Rewards Std 0.04222 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.038699 +eval/Actions Std 0.740819 +eval/Actions Max 0.999401 +eval/Actions Min -0.999102 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.69088e-06 +time/evaluation sampling (s) 5.41596 +time/exploration sampling (s) 7.37895 +time/logging (s) 0.00964523 +time/saving (s) 0.0155035 +time/training (s) 19.5777 +time/epoch (s) 32.3977 +time/total (s) 13245.9 +Epoch -432 +------------------------------ ---------------- +2022-05-15 21:43:44.443548 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -431 finished +------------------------------ ---------------- +epoch -431 +replay_buffer/size 999047 +trainer/num train calls 570000 +trainer/QF1 Loss 0.690673 +trainer/QF2 Loss 0.613606 +trainer/Policy Loss 11.6519 +trainer/Q1 Predictions Mean -71.6491 +trainer/Q1 Predictions Std 18.6751 +trainer/Q1 Predictions Max -1.30727 +trainer/Q1 Predictions Min -86.3854 +trainer/Q2 Predictions Mean -71.7531 +trainer/Q2 Predictions Std 18.6007 +trainer/Q2 Predictions Max -1.23994 +trainer/Q2 Predictions Min -86.3302 +trainer/Q Targets Mean -71.7538 +trainer/Q Targets Std 18.6151 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4877 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0269323 +trainer/policy/mean Std 0.719628 +trainer/policy/mean Max 0.998605 +trainer/policy/mean Min -0.999389 +trainer/policy/std Mean 0.420828 +trainer/policy/std Std 0.0209581 +trainer/policy/std Max 0.44185 +trainer/policy/std Min 0.387043 +trainer/Advantage Weights Mean 3.01756 +trainer/Advantage Weights Std 14.5559 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.39127e-12 +trainer/Advantage Score Mean -0.4351 +trainer/Advantage Score Std 0.542199 +trainer/Advantage Score Max 0.792923 +trainer/Advantage Score Min -2.56307 +trainer/V1 Predictions Mean -71.5243 +trainer/V1 Predictions Std 18.7175 +trainer/V1 Predictions Max -0.887603 +trainer/V1 Predictions Min -86.4876 +trainer/VF Loss 0.0578536 +expl/num steps total 570000 +expl/num paths total 722 +expl/path length Mean 500 +expl/path length Std 290 +expl/path length Max 790 +expl/path length Min 210 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0177151 +expl/Actions Std 0.833114 +expl/Actions Max 2.29502 +expl/Actions Min -2.36619 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 537936 +eval/num paths total 573 +eval/path length Mean 918 +eval/path length Std 0 +eval/path length Max 918 +eval/path length Min 918 +eval/Rewards Mean 0.00108932 +eval/Rewards Std 0.0329869 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0225981 +eval/Actions Std 0.71927 +eval/Actions Max 0.999819 +eval/Actions Min -0.999871 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.21649e-05 +time/evaluation sampling (s) 5.34163 +time/exploration sampling (s) 6.33048 +time/logging (s) 0.0113137 +time/saving (s) 0.0161627 +time/training (s) 18.751 +time/epoch (s) 30.4506 +time/total (s) 13276.4 +Epoch -431 +------------------------------ ---------------- +2022-05-15 21:44:17.191222 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -430 finished +------------------------------ ---------------- +epoch -430 +replay_buffer/size 999047 +trainer/num train calls 571000 +trainer/QF1 Loss 15.7142 +trainer/QF2 Loss 15.8876 +trainer/Policy Loss 25.0892 +trainer/Q1 Predictions Mean -72.53 +trainer/Q1 Predictions Std 18.4748 +trainer/Q1 Predictions Max -0.0243279 +trainer/Q1 Predictions Min -87.1944 +trainer/Q2 Predictions Mean -72.4464 +trainer/Q2 Predictions Std 18.5431 +trainer/Q2 Predictions Max -0.379304 +trainer/Q2 Predictions Min -87.222 +trainer/Q Targets Mean -72.4302 +trainer/Q Targets Std 18.9652 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4162 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000832552 +trainer/policy/mean Std 0.725993 +trainer/policy/mean Max 0.999125 +trainer/policy/mean Min -0.997997 +trainer/policy/std Mean 0.419073 +trainer/policy/std Std 0.0199021 +trainer/policy/std Max 0.439887 +trainer/policy/std Min 0.38895 +trainer/Advantage Weights Mean 4.7235 +trainer/Advantage Weights Std 16.7308 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.56791e-16 +trainer/Advantage Score Mean -0.304865 +trainer/Advantage Score Std 0.611588 +trainer/Advantage Score Max 0.789259 +trainer/Advantage Score Min -3.55694 +trainer/V1 Predictions Mean -72.4101 +trainer/V1 Predictions Std 18.6902 +trainer/V1 Predictions Max 1.15504 +trainer/V1 Predictions Min -87.687 +trainer/VF Loss 0.0596079 +expl/num steps total 571000 +expl/num paths total 724 +expl/path length Mean 500 +expl/path length Std 447 +expl/path length Max 947 +expl/path length Min 53 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0269009 +expl/Actions Std 0.819576 +expl/Actions Max 2.30384 +expl/Actions Min -2.07097 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 538936 +eval/num paths total 574 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0964776 +eval/Actions Std 0.691837 +eval/Actions Max 0.999956 +eval/Actions Min -0.999674 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.30298e-06 +time/evaluation sampling (s) 5.62488 +time/exploration sampling (s) 7.34668 +time/logging (s) 0.0121834 +time/saving (s) 0.0185808 +time/training (s) 19.7327 +time/epoch (s) 32.7351 +time/total (s) 13309.1 +Epoch -430 +------------------------------ ---------------- +2022-05-15 21:44:49.170511 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -429 finished +------------------------------ ---------------- +epoch -429 +replay_buffer/size 999047 +trainer/num train calls 572000 +trainer/QF1 Loss 0.819475 +trainer/QF2 Loss 0.720604 +trainer/Policy Loss 14.9951 +trainer/Q1 Predictions Mean -73.6217 +trainer/Q1 Predictions Std 18.1576 +trainer/Q1 Predictions Max -0.295522 +trainer/Q1 Predictions Min -87.5461 +trainer/Q2 Predictions Mean -73.4838 +trainer/Q2 Predictions Std 18.2141 +trainer/Q2 Predictions Max -0.422599 +trainer/Q2 Predictions Min -87.6825 +trainer/Q Targets Mean -73.4169 +trainer/Q Targets Std 18.297 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7388 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00215235 +trainer/policy/mean Std 0.712856 +trainer/policy/mean Max 0.999092 +trainer/policy/mean Min -0.999216 +trainer/policy/std Mean 0.419465 +trainer/policy/std Std 0.0208237 +trainer/policy/std Max 0.442079 +trainer/policy/std Min 0.390102 +trainer/Advantage Weights Mean 4.45385 +trainer/Advantage Weights Std 18.5994 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.01942e-16 +trainer/Advantage Score Mean -0.336762 +trainer/Advantage Score Std 0.582709 +trainer/Advantage Score Max 1.78352 +trainer/Advantage Score Min -3.68221 +trainer/V1 Predictions Mean -73.2181 +trainer/V1 Predictions Std 18.3793 +trainer/V1 Predictions Max 0.368653 +trainer/V1 Predictions Min -87.5719 +trainer/VF Loss 0.0743186 +expl/num steps total 572000 +expl/num paths total 726 +expl/path length Mean 500 +expl/path length Std 418 +expl/path length Max 918 +expl/path length Min 82 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00870099 +expl/Actions Std 0.824806 +expl/Actions Max 2.39119 +expl/Actions Min -2.43651 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 539918 +eval/num paths total 576 +eval/path length Mean 491 +eval/path length Std 4 +eval/path length Max 495 +eval/path length Min 487 +eval/Rewards Mean 0.00203666 +eval/Rewards Std 0.0450834 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0330952 +eval/Actions Std 0.730281 +eval/Actions Max 0.999545 +eval/Actions Min -0.999332 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 1.28951e-05 +time/evaluation sampling (s) 5.26919 +time/exploration sampling (s) 6.7287 +time/logging (s) 0.0123144 +time/saving (s) 0.0182779 +time/training (s) 19.9322 +time/epoch (s) 31.9607 +time/total (s) 13341.1 +Epoch -429 +------------------------------ ---------------- +2022-05-15 21:45:21.545451 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -428 finished +------------------------------ ---------------- +epoch -428 +replay_buffer/size 999047 +trainer/num train calls 573000 +trainer/QF1 Loss 0.543937 +trainer/QF2 Loss 0.508938 +trainer/Policy Loss 12.4069 +trainer/Q1 Predictions Mean -73.2805 +trainer/Q1 Predictions Std 15.2727 +trainer/Q1 Predictions Max -4.82861 +trainer/Q1 Predictions Min -86.8086 +trainer/Q2 Predictions Mean -73.2904 +trainer/Q2 Predictions Std 15.2049 +trainer/Q2 Predictions Max -4.44825 +trainer/Q2 Predictions Min -86.7971 +trainer/Q Targets Mean -73.4997 +trainer/Q Targets Std 15.1889 +trainer/Q Targets Max -7.77847 +trainer/Q Targets Min -87.3637 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0142946 +trainer/policy/mean Std 0.723263 +trainer/policy/mean Max 0.998696 +trainer/policy/mean Min -0.999349 +trainer/policy/std Mean 0.416662 +trainer/policy/std Std 0.0210317 +trainer/policy/std Max 0.437783 +trainer/policy/std Min 0.384262 +trainer/Advantage Weights Mean 1.95044 +trainer/Advantage Weights Std 11.5755 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.61205e-19 +trainer/Advantage Score Mean -0.429138 +trainer/Advantage Score Std 0.592328 +trainer/Advantage Score Max 1.25991 +trainer/Advantage Score Min -4.1596 +trainer/V1 Predictions Mean -73.2462 +trainer/V1 Predictions Std 15.3153 +trainer/V1 Predictions Max -6.2682 +trainer/V1 Predictions Min -87.1014 +trainer/VF Loss 0.0678053 +expl/num steps total 573000 +expl/num paths total 728 +expl/path length Mean 500 +expl/path length Std 449 +expl/path length Max 949 +expl/path length Min 51 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0340661 +expl/Actions Std 0.839157 +expl/Actions Max 2.25227 +expl/Actions Min -2.26254 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 540918 +eval/num paths total 577 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.221998 +eval/Actions Std 0.726074 +eval/Actions Max 0.999314 +eval/Actions Min -0.998836 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.91207e-06 +time/evaluation sampling (s) 5.79432 +time/exploration sampling (s) 6.34067 +time/logging (s) 0.0113816 +time/saving (s) 0.0163228 +time/training (s) 20.1936 +time/epoch (s) 32.3563 +time/total (s) 13373.5 +Epoch -428 +------------------------------ ---------------- +2022-05-15 21:45:52.521898 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -427 finished +------------------------------ ---------------- +epoch -427 +replay_buffer/size 999047 +trainer/num train calls 574000 +trainer/QF1 Loss 0.99931 +trainer/QF2 Loss 1.00049 +trainer/Policy Loss 20.8825 +trainer/Q1 Predictions Mean -71.3551 +trainer/Q1 Predictions Std 19.3701 +trainer/Q1 Predictions Max 0.215358 +trainer/Q1 Predictions Min -86.1915 +trainer/Q2 Predictions Mean -71.3667 +trainer/Q2 Predictions Std 19.5043 +trainer/Q2 Predictions Max -0.00663727 +trainer/Q2 Predictions Min -86.297 +trainer/Q Targets Mean -71.7593 +trainer/Q Targets Std 19.5007 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5377 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0379252 +trainer/policy/mean Std 0.705843 +trainer/policy/mean Max 0.998983 +trainer/policy/mean Min -0.999145 +trainer/policy/std Mean 0.417459 +trainer/policy/std Std 0.0215396 +trainer/policy/std Max 0.441617 +trainer/policy/std Min 0.38216 +trainer/Advantage Weights Mean 4.03062 +trainer/Advantage Weights Std 17.6145 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.85623e-19 +trainer/Advantage Score Mean -0.447819 +trainer/Advantage Score Std 0.717824 +trainer/Advantage Score Max 0.866656 +trainer/Advantage Score Min -4.1824 +trainer/V1 Predictions Mean -71.4142 +trainer/V1 Predictions Std 19.7084 +trainer/V1 Predictions Max 1.19915 +trainer/V1 Predictions Min -86.6041 +trainer/VF Loss 0.0844801 +expl/num steps total 574000 +expl/num paths total 729 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0312657 +expl/Actions Std 0.826468 +expl/Actions Max 2.12546 +expl/Actions Min -2.25802 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 541918 +eval/num paths total 578 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0260889 +eval/Actions Std 0.701795 +eval/Actions Max 0.999693 +eval/Actions Min -0.998949 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02301e-05 +time/evaluation sampling (s) 5.0175 +time/exploration sampling (s) 6.39734 +time/logging (s) 0.012553 +time/saving (s) 0.0165278 +time/training (s) 19.5204 +time/epoch (s) 30.9643 +time/total (s) 13404.4 +Epoch -427 +------------------------------ ---------------- +2022-05-15 21:46:24.348561 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -426 finished +------------------------------ ---------------- +epoch -426 +replay_buffer/size 999047 +trainer/num train calls 575000 +trainer/QF1 Loss 0.951124 +trainer/QF2 Loss 1.07032 +trainer/Policy Loss 17.5745 +trainer/Q1 Predictions Mean -71.7419 +trainer/Q1 Predictions Std 19.9709 +trainer/Q1 Predictions Max 0.181542 +trainer/Q1 Predictions Min -86.5886 +trainer/Q2 Predictions Mean -71.5725 +trainer/Q2 Predictions Std 20.0201 +trainer/Q2 Predictions Max -0.439766 +trainer/Q2 Predictions Min -86.7351 +trainer/Q Targets Mean -72.021 +trainer/Q Targets Std 19.9284 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0463 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0192154 +trainer/policy/mean Std 0.722931 +trainer/policy/mean Max 0.997613 +trainer/policy/mean Min -0.999281 +trainer/policy/std Mean 0.416223 +trainer/policy/std Std 0.0209854 +trainer/policy/std Max 0.439651 +trainer/policy/std Min 0.384062 +trainer/Advantage Weights Mean 4.07287 +trainer/Advantage Weights Std 16.5185 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.67713e-17 +trainer/Advantage Score Mean -0.318739 +trainer/Advantage Score Std 0.633018 +trainer/Advantage Score Max 2.61347 +trainer/Advantage Score Min -3.86269 +trainer/V1 Predictions Mean -71.7505 +trainer/V1 Predictions Std 20.0212 +trainer/V1 Predictions Max 0.759123 +trainer/V1 Predictions Min -86.8789 +trainer/VF Loss 0.0881108 +expl/num steps total 575000 +expl/num paths total 731 +expl/path length Mean 500 +expl/path length Std 190 +expl/path length Max 690 +expl/path length Min 310 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.04737 +expl/Actions Std 0.827021 +expl/Actions Max 2.36472 +expl/Actions Min -2.23626 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 542551 +eval/num paths total 579 +eval/path length Mean 633 +eval/path length Std 0 +eval/path length Max 633 +eval/path length Min 633 +eval/Rewards Mean 0.00157978 +eval/Rewards Std 0.039715 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0548843 +eval/Actions Std 0.732959 +eval/Actions Max 0.999436 +eval/Actions Min -0.999824 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.21919e-05 +time/evaluation sampling (s) 5.41113 +time/exploration sampling (s) 6.8636 +time/logging (s) 0.0104722 +time/saving (s) 0.0152278 +time/training (s) 19.5107 +time/epoch (s) 31.8111 +time/total (s) 13436.3 +Epoch -426 +------------------------------ ---------------- +2022-05-15 21:46:55.846484 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -425 finished +------------------------------ ---------------- +epoch -425 +replay_buffer/size 999047 +trainer/num train calls 576000 +trainer/QF1 Loss 0.467868 +trainer/QF2 Loss 0.462961 +trainer/Policy Loss 14.9333 +trainer/Q1 Predictions Mean -72.7211 +trainer/Q1 Predictions Std 18.8798 +trainer/Q1 Predictions Max -0.580819 +trainer/Q1 Predictions Min -86.0898 +trainer/Q2 Predictions Mean -72.7728 +trainer/Q2 Predictions Std 18.9084 +trainer/Q2 Predictions Max -0.343117 +trainer/Q2 Predictions Min -86.1219 +trainer/Q Targets Mean -72.5522 +trainer/Q Targets Std 19.0475 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7106 +trainer/rewards Mean -0.976562 +trainer/rewards Std 0.151288 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0234375 +trainer/terminals Std 0.151288 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00469496 +trainer/policy/mean Std 0.728158 +trainer/policy/mean Max 0.998156 +trainer/policy/mean Min -0.997712 +trainer/policy/std Mean 0.41586 +trainer/policy/std Std 0.0214371 +trainer/policy/std Max 0.438044 +trainer/policy/std Min 0.38374 +trainer/Advantage Weights Mean 2.13742 +trainer/Advantage Weights Std 13.775 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.67869e-17 +trainer/Advantage Score Mean -0.502189 +trainer/Advantage Score Std 0.553279 +trainer/Advantage Score Max 0.977875 +trainer/Advantage Score Min -3.74072 +trainer/V1 Predictions Mean -72.3564 +trainer/V1 Predictions Std 19.0169 +trainer/V1 Predictions Max 0.101194 +trainer/V1 Predictions Min -85.4425 +trainer/VF Loss 0.0644834 +expl/num steps total 576000 +expl/num paths total 733 +expl/path length Mean 500 +expl/path length Std 201 +expl/path length Max 701 +expl/path length Min 299 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0318026 +expl/Actions Std 0.831549 +expl/Actions Max 2.31749 +expl/Actions Min -2.31094 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 543551 +eval/num paths total 580 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00777801 +eval/Actions Std 0.772938 +eval/Actions Max 0.999826 +eval/Actions Min -0.998817 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0612e-05 +time/evaluation sampling (s) 4.80976 +time/exploration sampling (s) 6.69381 +time/logging (s) 0.0116468 +time/saving (s) 0.0150735 +time/training (s) 19.9545 +time/epoch (s) 31.4848 +time/total (s) 13467.7 +Epoch -425 +------------------------------ ---------------- +2022-05-15 21:47:27.976552 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -424 finished +------------------------------ ---------------- +epoch -424 +replay_buffer/size 999047 +trainer/num train calls 577000 +trainer/QF1 Loss 1.14302 +trainer/QF2 Loss 1.18293 +trainer/Policy Loss 6.03476 +trainer/Q1 Predictions Mean -73.0095 +trainer/Q1 Predictions Std 18.7819 +trainer/Q1 Predictions Max -2.72657 +trainer/Q1 Predictions Min -86.9367 +trainer/Q2 Predictions Mean -73.0003 +trainer/Q2 Predictions Std 18.7807 +trainer/Q2 Predictions Max -1.93446 +trainer/Q2 Predictions Min -87.203 +trainer/Q Targets Mean -72.567 +trainer/Q Targets Std 18.4122 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4647 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0037325 +trainer/policy/mean Std 0.723441 +trainer/policy/mean Max 0.999453 +trainer/policy/mean Min -0.999212 +trainer/policy/std Mean 0.414383 +trainer/policy/std Std 0.0210978 +trainer/policy/std Max 0.434937 +trainer/policy/std Min 0.382534 +trainer/Advantage Weights Mean 2.2684 +trainer/Advantage Weights Std 13.2732 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.48814e-14 +trainer/Advantage Score Mean -0.550363 +trainer/Advantage Score Std 0.51861 +trainer/Advantage Score Max 1.92808 +trainer/Advantage Score Min -3.09868 +trainer/V1 Predictions Mean -72.2787 +trainer/V1 Predictions Std 18.5918 +trainer/V1 Predictions Max 0.371387 +trainer/V1 Predictions Min -86.4052 +trainer/VF Loss 0.075874 +expl/num steps total 577000 +expl/num paths total 735 +expl/path length Mean 500 +expl/path length Std 416 +expl/path length Max 916 +expl/path length Min 84 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0323214 +expl/Actions Std 0.82457 +expl/Actions Max 2.33498 +expl/Actions Min -2.27636 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 543912 +eval/num paths total 581 +eval/path length Mean 361 +eval/path length Std 0 +eval/path length Max 361 +eval/path length Min 361 +eval/Rewards Mean 0.00277008 +eval/Rewards Std 0.0525586 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0270975 +eval/Actions Std 0.749727 +eval/Actions Max 0.999902 +eval/Actions Min -0.999528 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.45036e-06 +time/evaluation sampling (s) 5.29729 +time/exploration sampling (s) 7.01791 +time/logging (s) 0.00590869 +time/saving (s) 0.0177457 +time/training (s) 19.7742 +time/epoch (s) 32.1131 +time/total (s) 13499.9 +Epoch -424 +------------------------------ ---------------- +2022-05-15 21:47:59.471153 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -423 finished +------------------------------ ---------------- +epoch -423 +replay_buffer/size 999047 +trainer/num train calls 578000 +trainer/QF1 Loss 0.638612 +trainer/QF2 Loss 0.786379 +trainer/Policy Loss 12.4132 +trainer/Q1 Predictions Mean -72.6185 +trainer/Q1 Predictions Std 18.6926 +trainer/Q1 Predictions Max -1.82456 +trainer/Q1 Predictions Min -86.882 +trainer/Q2 Predictions Mean -72.7532 +trainer/Q2 Predictions Std 18.6277 +trainer/Q2 Predictions Max -1.75332 +trainer/Q2 Predictions Min -86.858 +trainer/Q Targets Mean -72.7078 +trainer/Q Targets Std 18.6348 +trainer/Q Targets Max -2.68571 +trainer/Q Targets Min -86.6178 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00380123 +trainer/policy/mean Std 0.725411 +trainer/policy/mean Max 0.999853 +trainer/policy/mean Min -0.999383 +trainer/policy/std Mean 0.417238 +trainer/policy/std Std 0.0202556 +trainer/policy/std Max 0.43715 +trainer/policy/std Min 0.386699 +trainer/Advantage Weights Mean 4.61789 +trainer/Advantage Weights Std 17.6033 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.63176e-14 +trainer/Advantage Score Mean -0.307526 +trainer/Advantage Score Std 0.571077 +trainer/Advantage Score Max 1.1235 +trainer/Advantage Score Min -3.12685 +trainer/V1 Predictions Mean -72.4237 +trainer/V1 Predictions Std 18.872 +trainer/V1 Predictions Max -1.8986 +trainer/V1 Predictions Min -86.5169 +trainer/VF Loss 0.059888 +expl/num steps total 578000 +expl/num paths total 737 +expl/path length Mean 500 +expl/path length Std 379 +expl/path length Max 879 +expl/path length Min 121 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0513509 +expl/Actions Std 0.826601 +expl/Actions Max 2.26619 +expl/Actions Min -2.37858 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 544658 +eval/num paths total 582 +eval/path length Mean 746 +eval/path length Std 0 +eval/path length Max 746 +eval/path length Min 746 +eval/Rewards Mean 0.00134048 +eval/Rewards Std 0.0365881 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0456934 +eval/Actions Std 0.737525 +eval/Actions Max 0.999866 +eval/Actions Min -0.999555 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.99775e-06 +time/evaluation sampling (s) 5.13905 +time/exploration sampling (s) 7.01384 +time/logging (s) 0.00741753 +time/saving (s) 0.0115029 +time/training (s) 19.3134 +time/epoch (s) 31.4853 +time/total (s) 13531.4 +Epoch -423 +------------------------------ ---------------- +2022-05-15 21:48:30.685531 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -422 finished +------------------------------ ---------------- +epoch -422 +replay_buffer/size 999047 +trainer/num train calls 579000 +trainer/QF1 Loss 0.637788 +trainer/QF2 Loss 0.611254 +trainer/Policy Loss 9.05734 +trainer/Q1 Predictions Mean -73.7346 +trainer/Q1 Predictions Std 17.5754 +trainer/Q1 Predictions Max -0.0478044 +trainer/Q1 Predictions Min -86.6653 +trainer/Q2 Predictions Mean -73.6653 +trainer/Q2 Predictions Std 17.6403 +trainer/Q2 Predictions Max -0.357667 +trainer/Q2 Predictions Min -86.4323 +trainer/Q Targets Mean -73.3696 +trainer/Q Targets Std 17.5431 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3348 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0160128 +trainer/policy/mean Std 0.718419 +trainer/policy/mean Max 0.998445 +trainer/policy/mean Min -0.998407 +trainer/policy/std Mean 0.416996 +trainer/policy/std Std 0.0209806 +trainer/policy/std Max 0.44008 +trainer/policy/std Min 0.384079 +trainer/Advantage Weights Mean 2.99046 +trainer/Advantage Weights Std 14.1383 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.68457e-12 +trainer/Advantage Score Mean -0.397847 +trainer/Advantage Score Std 0.490375 +trainer/Advantage Score Max 0.88781 +trainer/Advantage Score Min -2.63269 +trainer/V1 Predictions Mean -73.1171 +trainer/V1 Predictions Std 17.5731 +trainer/V1 Predictions Max -0.531 +trainer/V1 Predictions Min -86.1811 +trainer/VF Loss 0.0515545 +expl/num steps total 579000 +expl/num paths total 739 +expl/path length Mean 500 +expl/path length Std 456 +expl/path length Max 956 +expl/path length Min 44 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.038995 +expl/Actions Std 0.813495 +expl/Actions Max 2.32096 +expl/Actions Min -2.16685 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 545658 +eval/num paths total 583 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0231285 +eval/Actions Std 0.748336 +eval/Actions Max 0.999417 +eval/Actions Min -0.999845 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.13693e-06 +time/evaluation sampling (s) 5.14824 +time/exploration sampling (s) 6.67036 +time/logging (s) 0.0123523 +time/saving (s) 0.0187167 +time/training (s) 19.3576 +time/epoch (s) 31.2073 +time/total (s) 13562.6 +Epoch -422 +------------------------------ ---------------- +2022-05-15 21:49:02.451393 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -421 finished +------------------------------ ---------------- +epoch -421 +replay_buffer/size 999047 +trainer/num train calls 580000 +trainer/QF1 Loss 0.814878 +trainer/QF2 Loss 0.696615 +trainer/Policy Loss 18.1982 +trainer/Q1 Predictions Mean -73.5112 +trainer/Q1 Predictions Std 16.1671 +trainer/Q1 Predictions Max -3.06466 +trainer/Q1 Predictions Min -90.0512 +trainer/Q2 Predictions Mean -73.4477 +trainer/Q2 Predictions Std 16.1359 +trainer/Q2 Predictions Max -3.1807 +trainer/Q2 Predictions Min -89.656 +trainer/Q Targets Mean -73.5678 +trainer/Q Targets Std 16.1029 +trainer/Q Targets Max -3.76359 +trainer/Q Targets Min -89.1874 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0193748 +trainer/policy/mean Std 0.730486 +trainer/policy/mean Max 0.999072 +trainer/policy/mean Min -0.998942 +trainer/policy/std Mean 0.416899 +trainer/policy/std Std 0.021568 +trainer/policy/std Max 0.440582 +trainer/policy/std Min 0.383171 +trainer/Advantage Weights Mean 4.91999 +trainer/Advantage Weights Std 19.1263 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.22742e-09 +trainer/Advantage Score Mean -0.312806 +trainer/Advantage Score Std 0.481528 +trainer/Advantage Score Max 1.50707 +trainer/Advantage Score Min -1.95516 +trainer/V1 Predictions Mean -73.4083 +trainer/V1 Predictions Std 16.1619 +trainer/V1 Predictions Max -3.42382 +trainer/V1 Predictions Min -89.0336 +trainer/VF Loss 0.0633171 +expl/num steps total 580000 +expl/num paths total 740 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.1197 +expl/Actions Std 0.863307 +expl/Actions Max 2.53647 +expl/Actions Min -2.53815 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 546354 +eval/num paths total 584 +eval/path length Mean 696 +eval/path length Std 0 +eval/path length Max 696 +eval/path length Min 696 +eval/Rewards Mean 0.00143678 +eval/Rewards Std 0.0378777 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0277137 +eval/Actions Std 0.73431 +eval/Actions Max 0.999124 +eval/Actions Min -0.999978 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.22588e-06 +time/evaluation sampling (s) 4.84394 +time/exploration sampling (s) 7.62015 +time/logging (s) 0.01147 +time/saving (s) 0.0189365 +time/training (s) 19.2523 +time/epoch (s) 31.7468 +time/total (s) 13594.3 +Epoch -421 +------------------------------ ---------------- +2022-05-15 21:49:35.188798 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -420 finished +------------------------------ ---------------- +epoch -420 +replay_buffer/size 999047 +trainer/num train calls 581000 +trainer/QF1 Loss 9.70835 +trainer/QF2 Loss 9.64469 +trainer/Policy Loss 18.1522 +trainer/Q1 Predictions Mean -72.3779 +trainer/Q1 Predictions Std 19.0004 +trainer/Q1 Predictions Max -0.583641 +trainer/Q1 Predictions Min -88.3482 +trainer/Q2 Predictions Mean -72.4714 +trainer/Q2 Predictions Std 18.9872 +trainer/Q2 Predictions Max -0.828484 +trainer/Q2 Predictions Min -88.4349 +trainer/Q Targets Mean -72.4521 +trainer/Q Targets Std 18.7899 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.3889 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0239215 +trainer/policy/mean Std 0.733596 +trainer/policy/mean Max 0.999509 +trainer/policy/mean Min -0.99883 +trainer/policy/std Mean 0.417807 +trainer/policy/std Std 0.0211189 +trainer/policy/std Max 0.44304 +trainer/policy/std Min 0.386732 +trainer/Advantage Weights Mean 4.4869 +trainer/Advantage Weights Std 17.4067 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.44186e-16 +trainer/Advantage Score Mean -0.36254 +trainer/Advantage Score Std 0.530977 +trainer/Advantage Score Max 2.28883 +trainer/Advantage Score Min -3.59486 +trainer/V1 Predictions Mean -72.34 +trainer/V1 Predictions Std 18.8062 +trainer/V1 Predictions Max -0.206648 +trainer/V1 Predictions Min -87.7836 +trainer/VF Loss 0.0763132 +expl/num steps total 581000 +expl/num paths total 742 +expl/path length Mean 500 +expl/path length Std 293 +expl/path length Max 793 +expl/path length Min 207 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0290355 +expl/Actions Std 0.825299 +expl/Actions Max 2.24898 +expl/Actions Min -2.26639 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 547055 +eval/num paths total 585 +eval/path length Mean 701 +eval/path length Std 0 +eval/path length Max 701 +eval/path length Min 701 +eval/Rewards Mean 0.00142653 +eval/Rewards Std 0.0377425 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0424217 +eval/Actions Std 0.730703 +eval/Actions Max 0.999693 +eval/Actions Min -0.999317 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.39122e-06 +time/evaluation sampling (s) 5.35571 +time/exploration sampling (s) 7.55478 +time/logging (s) 0.0106918 +time/saving (s) 0.0159962 +time/training (s) 19.7816 +time/epoch (s) 32.7187 +time/total (s) 13627.1 +Epoch -420 +------------------------------ ---------------- +2022-05-15 21:50:07.259242 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -419 finished +------------------------------ ---------------- +epoch -419 +replay_buffer/size 999047 +trainer/num train calls 582000 +trainer/QF1 Loss 0.531877 +trainer/QF2 Loss 0.584211 +trainer/Policy Loss 5.16949 +trainer/Q1 Predictions Mean -72.4384 +trainer/Q1 Predictions Std 18.0561 +trainer/Q1 Predictions Max -0.944009 +trainer/Q1 Predictions Min -86.1675 +trainer/Q2 Predictions Mean -72.4101 +trainer/Q2 Predictions Std 18.1148 +trainer/Q2 Predictions Max 0.305716 +trainer/Q2 Predictions Min -86.1888 +trainer/Q Targets Mean -72.2328 +trainer/Q Targets Std 18.3537 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8123 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0181408 +trainer/policy/mean Std 0.735474 +trainer/policy/mean Max 0.999584 +trainer/policy/mean Min -0.999505 +trainer/policy/std Mean 0.416494 +trainer/policy/std Std 0.0217522 +trainer/policy/std Max 0.439499 +trainer/policy/std Min 0.38594 +trainer/Advantage Weights Mean 1.33454 +trainer/Advantage Weights Std 10.7764 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.0058e-28 +trainer/Advantage Score Mean -0.750724 +trainer/Advantage Score Std 0.833161 +trainer/Advantage Score Max 0.832314 +trainer/Advantage Score Min -6.23922 +trainer/V1 Predictions Mean -71.9684 +trainer/V1 Predictions Std 18.6075 +trainer/V1 Predictions Max 0.395956 +trainer/V1 Predictions Min -85.7092 +trainer/VF Loss 0.130747 +expl/num steps total 582000 +expl/num paths total 744 +expl/path length Mean 500 +expl/path length Std 261 +expl/path length Max 761 +expl/path length Min 239 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0279011 +expl/Actions Std 0.839365 +expl/Actions Max 2.32232 +expl/Actions Min -2.29255 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 548055 +eval/num paths total 586 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0853887 +eval/Actions Std 0.756168 +eval/Actions Max 0.999949 +eval/Actions Min -0.999817 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27438e-05 +time/evaluation sampling (s) 5.78374 +time/exploration sampling (s) 6.91513 +time/logging (s) 0.0124389 +time/saving (s) 0.0182534 +time/training (s) 19.3284 +time/epoch (s) 32.058 +time/total (s) 13659.1 +Epoch -419 +------------------------------ ---------------- +2022-05-15 21:50:38.457517 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -418 finished +------------------------------ ---------------- +epoch -418 +replay_buffer/size 999047 +trainer/num train calls 583000 +trainer/QF1 Loss 0.620548 +trainer/QF2 Loss 0.624637 +trainer/Policy Loss 27.6198 +trainer/Q1 Predictions Mean -72.6493 +trainer/Q1 Predictions Std 17.8933 +trainer/Q1 Predictions Max -1.14759 +trainer/Q1 Predictions Min -89.1218 +trainer/Q2 Predictions Mean -72.7412 +trainer/Q2 Predictions Std 17.8701 +trainer/Q2 Predictions Max -0.685771 +trainer/Q2 Predictions Min -89.1328 +trainer/Q Targets Mean -72.7847 +trainer/Q Targets Std 17.808 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4026 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0110622 +trainer/policy/mean Std 0.714221 +trainer/policy/mean Max 0.998222 +trainer/policy/mean Min -0.999178 +trainer/policy/std Mean 0.416287 +trainer/policy/std Std 0.0221459 +trainer/policy/std Max 0.439917 +trainer/policy/std Min 0.384027 +trainer/Advantage Weights Mean 6.95698 +trainer/Advantage Weights Std 22.5753 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.48314e-31 +trainer/Advantage Score Mean -0.292584 +trainer/Advantage Score Std 0.694634 +trainer/Advantage Score Max 1.08786 +trainer/Advantage Score Min -6.96785 +trainer/V1 Predictions Mean -72.5075 +trainer/V1 Predictions Std 17.8789 +trainer/V1 Predictions Max -1.04403 +trainer/V1 Predictions Min -88.6215 +trainer/VF Loss 0.0859003 +expl/num steps total 583000 +expl/num paths total 745 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.127142 +expl/Actions Std 0.820959 +expl/Actions Max 2.45693 +expl/Actions Min -2.28828 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 548826 +eval/num paths total 587 +eval/path length Mean 771 +eval/path length Std 0 +eval/path length Max 771 +eval/path length Min 771 +eval/Rewards Mean 0.00129702 +eval/Rewards Std 0.0359908 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0625591 +eval/Actions Std 0.726205 +eval/Actions Max 0.999925 +eval/Actions Min -0.99895 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.3357e-05 +time/evaluation sampling (s) 5.25755 +time/exploration sampling (s) 7.20078 +time/logging (s) 0.0115097 +time/saving (s) 0.0184118 +time/training (s) 18.691 +time/epoch (s) 31.1793 +time/total (s) 13690.3 +Epoch -418 +------------------------------ ---------------- +2022-05-15 21:51:10.548662 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -417 finished +------------------------------ ---------------- +epoch -417 +replay_buffer/size 999047 +trainer/num train calls 584000 +trainer/QF1 Loss 0.572709 +trainer/QF2 Loss 0.481125 +trainer/Policy Loss 23.1326 +trainer/Q1 Predictions Mean -75.7121 +trainer/Q1 Predictions Std 12.8222 +trainer/Q1 Predictions Max -1.8503 +trainer/Q1 Predictions Min -86.0629 +trainer/Q2 Predictions Mean -75.7492 +trainer/Q2 Predictions Std 12.9049 +trainer/Q2 Predictions Max -1.50493 +trainer/Q2 Predictions Min -86.2549 +trainer/Q Targets Mean -75.7971 +trainer/Q Targets Std 12.9969 +trainer/Q Targets Max -1.62491 +trainer/Q Targets Min -85.8593 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00968486 +trainer/policy/mean Std 0.717991 +trainer/policy/mean Max 0.999173 +trainer/policy/mean Min -0.998287 +trainer/policy/std Mean 0.416851 +trainer/policy/std Std 0.0204006 +trainer/policy/std Max 0.438253 +trainer/policy/std Min 0.387734 +trainer/Advantage Weights Mean 6.09163 +trainer/Advantage Weights Std 21.3024 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.3111e-11 +trainer/Advantage Score Mean -0.305976 +trainer/Advantage Score Std 0.525993 +trainer/Advantage Score Max 1.61991 +trainer/Advantage Score Min -2.3339 +trainer/V1 Predictions Mean -75.5417 +trainer/V1 Predictions Std 12.9982 +trainer/V1 Predictions Max -1.57066 +trainer/V1 Predictions Min -85.6693 +trainer/VF Loss 0.06815 +expl/num steps total 584000 +expl/num paths total 746 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.173172 +expl/Actions Std 0.804339 +expl/Actions Max 2.44704 +expl/Actions Min -2.23643 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 549352 +eval/num paths total 588 +eval/path length Mean 526 +eval/path length Std 0 +eval/path length Max 526 +eval/path length Min 526 +eval/Rewards Mean 0.00190114 +eval/Rewards Std 0.0435606 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0372202 +eval/Actions Std 0.739497 +eval/Actions Max 0.999898 +eval/Actions Min -0.999689 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.06404e-05 +time/evaluation sampling (s) 5.32443 +time/exploration sampling (s) 7.04697 +time/logging (s) 0.00881185 +time/saving (s) 0.0167069 +time/training (s) 19.6737 +time/epoch (s) 32.0707 +time/total (s) 13722.4 +Epoch -417 +------------------------------ ---------------- +2022-05-15 21:51:42.560042 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -416 finished +------------------------------ ---------------- +epoch -416 +replay_buffer/size 999047 +trainer/num train calls 585000 +trainer/QF1 Loss 0.496005 +trainer/QF2 Loss 0.613439 +trainer/Policy Loss 32.5015 +trainer/Q1 Predictions Mean -74.1416 +trainer/Q1 Predictions Std 16.2061 +trainer/Q1 Predictions Max -2.14234 +trainer/Q1 Predictions Min -86.2955 +trainer/Q2 Predictions Mean -74.1371 +trainer/Q2 Predictions Std 16.1584 +trainer/Q2 Predictions Max -2.08239 +trainer/Q2 Predictions Min -86.1302 +trainer/Q Targets Mean -74.071 +trainer/Q Targets Std 16.2639 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.334 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00554195 +trainer/policy/mean Std 0.723815 +trainer/policy/mean Max 0.999284 +trainer/policy/mean Min -0.99882 +trainer/policy/std Mean 0.416425 +trainer/policy/std Std 0.0202881 +trainer/policy/std Max 0.435632 +trainer/policy/std Min 0.383638 +trainer/Advantage Weights Mean 7.06918 +trainer/Advantage Weights Std 23.0794 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.47547e-20 +trainer/Advantage Score Mean -0.296373 +trainer/Advantage Score Std 0.633495 +trainer/Advantage Score Max 1.62884 +trainer/Advantage Score Min -4.56627 +trainer/V1 Predictions Mean -73.8499 +trainer/V1 Predictions Std 16.2506 +trainer/V1 Predictions Max -1.8268 +trainer/V1 Predictions Min -86.15 +trainer/VF Loss 0.092274 +expl/num steps total 585000 +expl/num paths total 747 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0487355 +expl/Actions Std 0.829586 +expl/Actions Max 2.43997 +expl/Actions Min -2.17309 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 550352 +eval/num paths total 589 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.167137 +eval/Actions Std 0.678917 +eval/Actions Max 0.998381 +eval/Actions Min -0.999294 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.10082e-05 +time/evaluation sampling (s) 5.22295 +time/exploration sampling (s) 7.16405 +time/logging (s) 0.0123202 +time/saving (s) 0.0179812 +time/training (s) 19.5777 +time/epoch (s) 31.995 +time/total (s) 13754.4 +Epoch -416 +------------------------------ ---------------- +2022-05-15 21:52:14.828568 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -415 finished +------------------------------ ---------------- +epoch -415 +replay_buffer/size 999047 +trainer/num train calls 586000 +trainer/QF1 Loss 1.21337 +trainer/QF2 Loss 1.1517 +trainer/Policy Loss 64.4907 +trainer/Q1 Predictions Mean -69.9961 +trainer/Q1 Predictions Std 21.12 +trainer/Q1 Predictions Max -1.71244 +trainer/Q1 Predictions Min -86.9547 +trainer/Q2 Predictions Mean -70.0733 +trainer/Q2 Predictions Std 21.0984 +trainer/Q2 Predictions Max -1.26043 +trainer/Q2 Predictions Min -87.4022 +trainer/Q Targets Mean -70.6675 +trainer/Q Targets Std 21.0137 +trainer/Q Targets Max -3.0818 +trainer/Q Targets Min -88.0045 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00496882 +trainer/policy/mean Std 0.72996 +trainer/policy/mean Max 0.999263 +trainer/policy/mean Min -0.999264 +trainer/policy/std Mean 0.417104 +trainer/policy/std Std 0.0220647 +trainer/policy/std Max 0.438844 +trainer/policy/std Min 0.382163 +trainer/Advantage Weights Mean 13.1322 +trainer/Advantage Weights Std 28.186 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.22475e-12 +trainer/Advantage Score Mean -0.134645 +trainer/Advantage Score Std 0.652351 +trainer/Advantage Score Max 1.6955 +trainer/Advantage Score Min -2.74283 +trainer/V1 Predictions Mean -70.3311 +trainer/V1 Predictions Std 21.3555 +trainer/V1 Predictions Max -0.893481 +trainer/V1 Predictions Min -88.1116 +trainer/VF Loss 0.112776 +expl/num steps total 586000 +expl/num paths total 748 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.103719 +expl/Actions Std 0.792756 +expl/Actions Max 2.35926 +expl/Actions Min -2.26341 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 551055 +eval/num paths total 590 +eval/path length Mean 703 +eval/path length Std 0 +eval/path length Max 703 +eval/path length Min 703 +eval/Rewards Mean 0.00142248 +eval/Rewards Std 0.0376889 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0403405 +eval/Actions Std 0.731186 +eval/Actions Max 0.999779 +eval/Actions Min -0.999546 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.27782e-05 +time/evaluation sampling (s) 5.21527 +time/exploration sampling (s) 7.29299 +time/logging (s) 0.0113341 +time/saving (s) 0.0194918 +time/training (s) 19.7106 +time/epoch (s) 32.2497 +time/total (s) 13786.6 +Epoch -415 +------------------------------ ---------------- +2022-05-15 21:52:46.620519 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -414 finished +------------------------------ ---------------- +epoch -414 +replay_buffer/size 999047 +trainer/num train calls 587000 +trainer/QF1 Loss 1.07038 +trainer/QF2 Loss 0.959628 +trainer/Policy Loss 45.7468 +trainer/Q1 Predictions Mean -73.8712 +trainer/Q1 Predictions Std 17.1158 +trainer/Q1 Predictions Max -2.52344 +trainer/Q1 Predictions Min -88.4657 +trainer/Q2 Predictions Mean -73.9574 +trainer/Q2 Predictions Std 17.0496 +trainer/Q2 Predictions Max -3.07615 +trainer/Q2 Predictions Min -88.426 +trainer/Q Targets Mean -74.1111 +trainer/Q Targets Std 16.9616 +trainer/Q Targets Max -1.94235 +trainer/Q Targets Min -88.7097 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0131176 +trainer/policy/mean Std 0.731844 +trainer/policy/mean Max 0.998958 +trainer/policy/mean Min -0.999918 +trainer/policy/std Mean 0.417218 +trainer/policy/std Std 0.0202786 +trainer/policy/std Max 0.438245 +trainer/policy/std Min 0.386377 +trainer/Advantage Weights Mean 8.9408 +trainer/Advantage Weights Std 24.1124 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19266e-13 +trainer/Advantage Score Mean -0.126639 +trainer/Advantage Score Std 0.496098 +trainer/Advantage Score Max 1.5378 +trainer/Advantage Score Min -2.97574 +trainer/V1 Predictions Mean -73.8662 +trainer/V1 Predictions Std 17.0549 +trainer/V1 Predictions Max -2.6243 +trainer/V1 Predictions Min -88.663 +trainer/VF Loss 0.0685257 +expl/num steps total 587000 +expl/num paths total 750 +expl/path length Mean 500 +expl/path length Std 267 +expl/path length Max 767 +expl/path length Min 233 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0326876 +expl/Actions Std 0.838611 +expl/Actions Max 2.34175 +expl/Actions Min -2.23843 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 552055 +eval/num paths total 591 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.14348 +eval/Actions Std 0.790118 +eval/Actions Max 0.999538 +eval/Actions Min -0.999724 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.1174e-05 +time/evaluation sampling (s) 5.47285 +time/exploration sampling (s) 6.90614 +time/logging (s) 0.011669 +time/saving (s) 0.0194466 +time/training (s) 19.364 +time/epoch (s) 31.7741 +time/total (s) 13818.4 +Epoch -414 +------------------------------ ---------------- +2022-05-15 21:53:19.006416 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -413 finished +------------------------------ ---------------- +epoch -413 +replay_buffer/size 999047 +trainer/num train calls 588000 +trainer/QF1 Loss 1.62451 +trainer/QF2 Loss 1.70058 +trainer/Policy Loss 15.9316 +trainer/Q1 Predictions Mean -71.3658 +trainer/Q1 Predictions Std 19.8615 +trainer/Q1 Predictions Max -0.787298 +trainer/Q1 Predictions Min -86.6283 +trainer/Q2 Predictions Mean -71.4503 +trainer/Q2 Predictions Std 19.8234 +trainer/Q2 Predictions Max -1.14691 +trainer/Q2 Predictions Min -86.7171 +trainer/Q Targets Mean -71.0948 +trainer/Q Targets Std 19.9675 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9696 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.019013 +trainer/policy/mean Std 0.720083 +trainer/policy/mean Max 0.997763 +trainer/policy/mean Min -0.998938 +trainer/policy/std Mean 0.41655 +trainer/policy/std Std 0.0212076 +trainer/policy/std Max 0.438964 +trainer/policy/std Min 0.383846 +trainer/Advantage Weights Mean 3.46005 +trainer/Advantage Weights Std 15.0567 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.87472e-20 +trainer/Advantage Score Mean -0.540747 +trainer/Advantage Score Std 0.764254 +trainer/Advantage Score Max 0.818787 +trainer/Advantage Score Min -4.54232 +trainer/V1 Predictions Mean -70.8141 +trainer/V1 Predictions Std 20.0528 +trainer/V1 Predictions Max 0.357178 +trainer/V1 Predictions Min -86.1203 +trainer/VF Loss 0.0990782 +expl/num steps total 588000 +expl/num paths total 752 +expl/path length Mean 500 +expl/path length Std 26 +expl/path length Max 526 +expl/path length Min 474 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0318444 +expl/Actions Std 0.836513 +expl/Actions Max 2.4081 +expl/Actions Min -2.4326 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 552892 +eval/num paths total 592 +eval/path length Mean 837 +eval/path length Std 0 +eval/path length Max 837 +eval/path length Min 837 +eval/Rewards Mean 0.00119474 +eval/Rewards Std 0.0345444 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0343316 +eval/Actions Std 0.730524 +eval/Actions Max 0.999763 +eval/Actions Min -0.999316 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.913e-06 +time/evaluation sampling (s) 5.3825 +time/exploration sampling (s) 7.14736 +time/logging (s) 0.00989341 +time/saving (s) 0.0168942 +time/training (s) 19.8132 +time/epoch (s) 32.3699 +time/total (s) 13850.8 +Epoch -413 +------------------------------ ---------------- +2022-05-15 21:53:50.414522 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -412 finished +------------------------------ ---------------- +epoch -412 +replay_buffer/size 999047 +trainer/num train calls 589000 +trainer/QF1 Loss 0.682859 +trainer/QF2 Loss 0.668429 +trainer/Policy Loss 10.2045 +trainer/Q1 Predictions Mean -73.62 +trainer/Q1 Predictions Std 16.5162 +trainer/Q1 Predictions Max -2.12513 +trainer/Q1 Predictions Min -86.8637 +trainer/Q2 Predictions Mean -73.565 +trainer/Q2 Predictions Std 16.4453 +trainer/Q2 Predictions Max -1.60749 +trainer/Q2 Predictions Min -86.8829 +trainer/Q Targets Mean -73.5519 +trainer/Q Targets Std 16.516 +trainer/Q Targets Max -2.31263 +trainer/Q Targets Min -86.816 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0239801 +trainer/policy/mean Std 0.71765 +trainer/policy/mean Max 0.999578 +trainer/policy/mean Min -0.99969 +trainer/policy/std Mean 0.41713 +trainer/policy/std Std 0.0212277 +trainer/policy/std Max 0.439733 +trainer/policy/std Min 0.385515 +trainer/Advantage Weights Mean 3.68217 +trainer/Advantage Weights Std 17.1879 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.63967e-19 +trainer/Advantage Score Mean -0.48006 +trainer/Advantage Score Std 0.680704 +trainer/Advantage Score Max 2.41015 +trainer/Advantage Score Min -4.32546 +trainer/V1 Predictions Mean -73.31 +trainer/V1 Predictions Std 16.6323 +trainer/V1 Predictions Max -1.08044 +trainer/V1 Predictions Min -86.6536 +trainer/VF Loss 0.105736 +expl/num steps total 589000 +expl/num paths total 754 +expl/path length Mean 500 +expl/path length Std 121 +expl/path length Max 621 +expl/path length Min 379 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.028865 +expl/Actions Std 0.830167 +expl/Actions Max 2.42245 +expl/Actions Min -2.45043 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 553529 +eval/num paths total 593 +eval/path length Mean 637 +eval/path length Std 0 +eval/path length Max 637 +eval/path length Min 637 +eval/Rewards Mean 0.00156986 +eval/Rewards Std 0.0395903 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.0128283 +eval/Actions Std 0.75647 +eval/Actions Max 0.999114 +eval/Actions Min -0.999602 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2245e-05 +time/evaluation sampling (s) 5.0593 +time/exploration sampling (s) 7.03079 +time/logging (s) 0.00964196 +time/saving (s) 0.0146798 +time/training (s) 19.2787 +time/epoch (s) 31.3932 +time/total (s) 13882.2 +Epoch -412 +------------------------------ ---------------- +2022-05-15 21:54:21.069544 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -411 finished +------------------------------ ---------------- +epoch -411 +replay_buffer/size 999047 +trainer/num train calls 590000 +trainer/QF1 Loss 5.50615 +trainer/QF2 Loss 5.47135 +trainer/Policy Loss 7.12003 +trainer/Q1 Predictions Mean -71.1993 +trainer/Q1 Predictions Std 19.502 +trainer/Q1 Predictions Max -0.57656 +trainer/Q1 Predictions Min -86.9569 +trainer/Q2 Predictions Mean -71.1488 +trainer/Q2 Predictions Std 19.4282 +trainer/Q2 Predictions Max -0.357335 +trainer/Q2 Predictions Min -86.9397 +trainer/Q Targets Mean -70.9121 +trainer/Q Targets Std 19.4825 +trainer/Q Targets Max 0.556208 +trainer/Q Targets Min -87.2413 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00984308 +trainer/policy/mean Std 0.719613 +trainer/policy/mean Max 0.998203 +trainer/policy/mean Min -0.998162 +trainer/policy/std Mean 0.414893 +trainer/policy/std Std 0.022498 +trainer/policy/std Max 0.438921 +trainer/policy/std Min 0.382168 +trainer/Advantage Weights Mean 1.52432 +trainer/Advantage Weights Std 7.77698 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.4245e-20 +trainer/Advantage Score Mean -0.534837 +trainer/Advantage Score Std 0.685659 +trainer/Advantage Score Max 0.69315 +trainer/Advantage Score Min -4.40469 +trainer/V1 Predictions Mean -70.6644 +trainer/V1 Predictions Std 19.7235 +trainer/V1 Predictions Max 0.826438 +trainer/V1 Predictions Min -87.2353 +trainer/VF Loss 0.080671 +expl/num steps total 590000 +expl/num paths total 756 +expl/path length Mean 500 +expl/path length Std 330 +expl/path length Max 830 +expl/path length Min 170 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0181592 +expl/Actions Std 0.82133 +expl/Actions Max 2.49309 +expl/Actions Min -2.18543 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 554529 +eval/num paths total 594 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0640045 +eval/Actions Std 0.747593 +eval/Actions Max 0.999816 +eval/Actions Min -0.999377 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.26064e-05 +time/evaluation sampling (s) 5.14383 +time/exploration sampling (s) 6.45968 +time/logging (s) 0.011775 +time/saving (s) 0.0165602 +time/training (s) 19.0089 +time/epoch (s) 30.6408 +time/total (s) 13912.8 +Epoch -411 +------------------------------ ---------------- +2022-05-15 21:54:52.509715 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -410 finished +------------------------------ ---------------- +epoch -410 +replay_buffer/size 999047 +trainer/num train calls 591000 +trainer/QF1 Loss 0.5485 +trainer/QF2 Loss 0.68177 +trainer/Policy Loss 18.3457 +trainer/Q1 Predictions Mean -73.6647 +trainer/Q1 Predictions Std 17.4451 +trainer/Q1 Predictions Max 1.31678 +trainer/Q1 Predictions Min -86.3723 +trainer/Q2 Predictions Mean -73.7389 +trainer/Q2 Predictions Std 17.3796 +trainer/Q2 Predictions Max 0.698358 +trainer/Q2 Predictions Min -86.2998 +trainer/Q Targets Mean -73.6256 +trainer/Q Targets Std 17.2505 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1686 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00087914 +trainer/policy/mean Std 0.721993 +trainer/policy/mean Max 0.998311 +trainer/policy/mean Min -0.998983 +trainer/policy/std Mean 0.415703 +trainer/policy/std Std 0.0225178 +trainer/policy/std Max 0.439242 +trainer/policy/std Min 0.378863 +trainer/Advantage Weights Mean 4.96636 +trainer/Advantage Weights Std 18.9727 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.01313e-14 +trainer/Advantage Score Mean -0.266443 +trainer/Advantage Score Std 0.515411 +trainer/Advantage Score Max 2.92463 +trainer/Advantage Score Min -3.22231 +trainer/V1 Predictions Mean -73.4433 +trainer/V1 Predictions Std 17.2274 +trainer/V1 Predictions Max 0.439291 +trainer/V1 Predictions Min -85.9562 +trainer/VF Loss 0.0831577 +expl/num steps total 591000 +expl/num paths total 757 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0609529 +expl/Actions Std 0.838023 +expl/Actions Max 2.22172 +expl/Actions Min -2.54887 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 555529 +eval/num paths total 595 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.138815 +eval/Actions Std 0.777891 +eval/Actions Max 0.999191 +eval/Actions Min -0.999734 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.05498e-06 +time/evaluation sampling (s) 4.74251 +time/exploration sampling (s) 7.24863 +time/logging (s) 0.0131112 +time/saving (s) 0.0168116 +time/training (s) 19.4073 +time/epoch (s) 31.4284 +time/total (s) 13944.3 +Epoch -410 +------------------------------ ---------------- +2022-05-15 21:55:24.531775 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -409 finished +------------------------------ ---------------- +epoch -409 +replay_buffer/size 999047 +trainer/num train calls 592000 +trainer/QF1 Loss 0.66474 +trainer/QF2 Loss 0.666915 +trainer/Policy Loss 32.0755 +trainer/Q1 Predictions Mean -72.9599 +trainer/Q1 Predictions Std 18.0628 +trainer/Q1 Predictions Max -0.936323 +trainer/Q1 Predictions Min -89.7259 +trainer/Q2 Predictions Mean -73.0495 +trainer/Q2 Predictions Std 17.896 +trainer/Q2 Predictions Max -1.78557 +trainer/Q2 Predictions Min -89.6445 +trainer/Q Targets Mean -73.0048 +trainer/Q Targets Std 17.8689 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4063 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.041608 +trainer/policy/mean Std 0.734504 +trainer/policy/mean Max 0.999506 +trainer/policy/mean Min -0.999869 +trainer/policy/std Mean 0.416247 +trainer/policy/std Std 0.0212128 +trainer/policy/std Max 0.438242 +trainer/policy/std Min 0.38424 +trainer/Advantage Weights Mean 6.71852 +trainer/Advantage Weights Std 22.1565 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.67736e-20 +trainer/Advantage Score Mean -0.432049 +trainer/Advantage Score Std 0.701824 +trainer/Advantage Score Max 1.35049 +trainer/Advantage Score Min -4.4509 +trainer/V1 Predictions Mean -72.7202 +trainer/V1 Predictions Std 17.9663 +trainer/V1 Predictions Max -0.859874 +trainer/V1 Predictions Min -87.9117 +trainer/VF Loss 0.0974095 +expl/num steps total 592000 +expl/num paths total 758 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0624664 +expl/Actions Std 0.856518 +expl/Actions Max 2.19702 +expl/Actions Min -2.39854 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 556529 +eval/num paths total 596 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.063125 +eval/Actions Std 0.711795 +eval/Actions Max 0.999666 +eval/Actions Min -0.999926 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.01101e-06 +time/evaluation sampling (s) 5.3293 +time/exploration sampling (s) 7.05982 +time/logging (s) 0.0115467 +time/saving (s) 0.0157191 +time/training (s) 19.5874 +time/epoch (s) 32.0038 +time/total (s) 13976.3 +Epoch -409 +------------------------------ ---------------- +2022-05-15 21:55:56.013479 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -408 finished +------------------------------ ---------------- +epoch -408 +replay_buffer/size 999047 +trainer/num train calls 593000 +trainer/QF1 Loss 0.679457 +trainer/QF2 Loss 0.573376 +trainer/Policy Loss 11.0712 +trainer/Q1 Predictions Mean -73.4351 +trainer/Q1 Predictions Std 16.4569 +trainer/Q1 Predictions Max -0.318227 +trainer/Q1 Predictions Min -86.5147 +trainer/Q2 Predictions Mean -73.4698 +trainer/Q2 Predictions Std 16.5308 +trainer/Q2 Predictions Max 0.282445 +trainer/Q2 Predictions Min -86.1092 +trainer/Q Targets Mean -73.5111 +trainer/Q Targets Std 16.366 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2841 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0162504 +trainer/policy/mean Std 0.712315 +trainer/policy/mean Max 0.998538 +trainer/policy/mean Min -0.999371 +trainer/policy/std Mean 0.417554 +trainer/policy/std Std 0.0219423 +trainer/policy/std Max 0.440559 +trainer/policy/std Min 0.38499 +trainer/Advantage Weights Mean 2.88685 +trainer/Advantage Weights Std 13.84 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.25591e-22 +trainer/Advantage Score Mean -0.544438 +trainer/Advantage Score Std 0.72789 +trainer/Advantage Score Max 0.82254 +trainer/Advantage Score Min -4.92086 +trainer/V1 Predictions Mean -73.1724 +trainer/V1 Predictions Std 16.5296 +trainer/V1 Predictions Max 0.342924 +trainer/V1 Predictions Min -86.3594 +trainer/VF Loss 0.091541 +expl/num steps total 593000 +expl/num paths total 760 +expl/path length Mean 500 +expl/path length Std 324 +expl/path length Max 824 +expl/path length Min 176 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0594044 +expl/Actions Std 0.818195 +expl/Actions Max 2.39432 +expl/Actions Min -2.18717 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 557529 +eval/num paths total 597 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.252193 +eval/Actions Std 0.73738 +eval/Actions Max 0.999755 +eval/Actions Min -0.999296 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.01307e-06 +time/evaluation sampling (s) 5.15934 +time/exploration sampling (s) 6.84908 +time/logging (s) 0.0124492 +time/saving (s) 0.0185465 +time/training (s) 19.4281 +time/epoch (s) 31.4675 +time/total (s) 14007.8 +Epoch -408 +------------------------------ ---------------- +2022-05-15 21:56:28.469387 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -407 finished +------------------------------ ---------------- +epoch -407 +replay_buffer/size 999047 +trainer/num train calls 594000 +trainer/QF1 Loss 0.743062 +trainer/QF2 Loss 0.74065 +trainer/Policy Loss 19.2881 +trainer/Q1 Predictions Mean -72.2421 +trainer/Q1 Predictions Std 19.1437 +trainer/Q1 Predictions Max 0.574735 +trainer/Q1 Predictions Min -86.6424 +trainer/Q2 Predictions Mean -72.1483 +trainer/Q2 Predictions Std 19.1611 +trainer/Q2 Predictions Max -0.16099 +trainer/Q2 Predictions Min -86.2134 +trainer/Q Targets Mean -72.4571 +trainer/Q Targets Std 19.1088 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3851 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00296603 +trainer/policy/mean Std 0.724823 +trainer/policy/mean Max 0.999004 +trainer/policy/mean Min -0.999107 +trainer/policy/std Mean 0.41616 +trainer/policy/std Std 0.0205734 +trainer/policy/std Max 0.43932 +trainer/policy/std Min 0.38452 +trainer/Advantage Weights Mean 5.30519 +trainer/Advantage Weights Std 19.0006 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.05358e-23 +trainer/Advantage Score Mean -0.349018 +trainer/Advantage Score Std 0.702843 +trainer/Advantage Score Max 1.51196 +trainer/Advantage Score Min -5.13394 +trainer/V1 Predictions Mean -72.2019 +trainer/V1 Predictions Std 19.2819 +trainer/V1 Predictions Max 0.237235 +trainer/V1 Predictions Min -87.0191 +trainer/VF Loss 0.0937648 +expl/num steps total 594000 +expl/num paths total 761 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0260737 +expl/Actions Std 0.81497 +expl/Actions Max 2.41938 +expl/Actions Min -2.4543 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 558529 +eval/num paths total 598 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.153303 +eval/Actions Std 0.674679 +eval/Actions Max 0.999323 +eval/Actions Min -0.999063 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.44008e-06 +time/evaluation sampling (s) 5.4457 +time/exploration sampling (s) 7.60599 +time/logging (s) 0.0123557 +time/saving (s) 0.0189608 +time/training (s) 19.3548 +time/epoch (s) 32.4378 +time/total (s) 14040.2 +Epoch -407 +------------------------------ ---------------- +2022-05-15 21:57:00.979276 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -406 finished +------------------------------ ---------------- +epoch -406 +replay_buffer/size 999047 +trainer/num train calls 595000 +trainer/QF1 Loss 1.04687 +trainer/QF2 Loss 0.934039 +trainer/Policy Loss 10.8807 +trainer/Q1 Predictions Mean -70.7301 +trainer/Q1 Predictions Std 21.4734 +trainer/Q1 Predictions Max -1.50928 +trainer/Q1 Predictions Min -88.2466 +trainer/Q2 Predictions Mean -70.6505 +trainer/Q2 Predictions Std 21.5315 +trainer/Q2 Predictions Max -1.37227 +trainer/Q2 Predictions Min -88.1949 +trainer/Q Targets Mean -70.4918 +trainer/Q Targets Std 21.2917 +trainer/Q Targets Max -0.319274 +trainer/Q Targets Min -87.5205 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0197542 +trainer/policy/mean Std 0.732716 +trainer/policy/mean Max 0.999656 +trainer/policy/mean Min -0.999034 +trainer/policy/std Mean 0.417884 +trainer/policy/std Std 0.0206438 +trainer/policy/std Max 0.440473 +trainer/policy/std Min 0.386624 +trainer/Advantage Weights Mean 2.72722 +trainer/Advantage Weights Std 13.3461 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.76668e-14 +trainer/Advantage Score Mean -0.411821 +trainer/Advantage Score Std 0.51548 +trainer/Advantage Score Max 1.3858 +trainer/Advantage Score Min -3.01863 +trainer/V1 Predictions Mean -70.2257 +trainer/V1 Predictions Std 21.4448 +trainer/V1 Predictions Max -1.02478 +trainer/V1 Predictions Min -87.2935 +trainer/VF Loss 0.0580196 +expl/num steps total 595000 +expl/num paths total 763 +expl/path length Mean 500 +expl/path length Std 247 +expl/path length Max 747 +expl/path length Min 253 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0694817 +expl/Actions Std 0.830702 +expl/Actions Max 2.56516 +expl/Actions Min -2.4486 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 559529 +eval/num paths total 599 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0891626 +eval/Actions Std 0.735594 +eval/Actions Max 0.999615 +eval/Actions Min -0.999542 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.05118e-05 +time/evaluation sampling (s) 5.55424 +time/exploration sampling (s) 7.6453 +time/logging (s) 0.0133345 +time/saving (s) 0.0163439 +time/training (s) 19.2639 +time/epoch (s) 32.4931 +time/total (s) 14072.7 +Epoch -406 +------------------------------ ---------------- +2022-05-15 21:57:32.971403 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -405 finished +------------------------------ ---------------- +epoch -405 +replay_buffer/size 999047 +trainer/num train calls 596000 +trainer/QF1 Loss 0.889889 +trainer/QF2 Loss 1.12501 +trainer/Policy Loss 8.65615 +trainer/Q1 Predictions Mean -71.8681 +trainer/Q1 Predictions Std 19.1194 +trainer/Q1 Predictions Max -0.17914 +trainer/Q1 Predictions Min -86.9646 +trainer/Q2 Predictions Mean -71.8228 +trainer/Q2 Predictions Std 19.1298 +trainer/Q2 Predictions Max 0.0114877 +trainer/Q2 Predictions Min -87.2182 +trainer/Q Targets Mean -71.808 +trainer/Q Targets Std 18.9398 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5769 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00172137 +trainer/policy/mean Std 0.722672 +trainer/policy/mean Max 0.999503 +trainer/policy/mean Min -0.999239 +trainer/policy/std Mean 0.417866 +trainer/policy/std Std 0.0215844 +trainer/policy/std Max 0.440297 +trainer/policy/std Min 0.385842 +trainer/Advantage Weights Mean 2.82709 +trainer/Advantage Weights Std 14.7955 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.5832e-29 +trainer/Advantage Score Mean -0.608256 +trainer/Advantage Score Std 0.830684 +trainer/Advantage Score Max 1.31817 +trainer/Advantage Score Min -6.52526 +trainer/V1 Predictions Mean -71.4444 +trainer/V1 Predictions Std 19.2873 +trainer/V1 Predictions Max 0.43253 +trainer/V1 Predictions Min -86.2944 +trainer/VF Loss 0.122716 +expl/num steps total 596000 +expl/num paths total 765 +expl/path length Mean 500 +expl/path length Std 305 +expl/path length Max 805 +expl/path length Min 195 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0467085 +expl/Actions Std 0.830206 +expl/Actions Max 2.28409 +expl/Actions Min -2.22332 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 560325 +eval/num paths total 600 +eval/path length Mean 796 +eval/path length Std 0 +eval/path length Max 796 +eval/path length Min 796 +eval/Rewards Mean 0.00125628 +eval/Rewards Std 0.0354218 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0110182 +eval/Actions Std 0.738101 +eval/Actions Max 0.999077 +eval/Actions Min -0.999838 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.40864e-06 +time/evaluation sampling (s) 5.84158 +time/exploration sampling (s) 6.39166 +time/logging (s) 0.011436 +time/saving (s) 0.0182302 +time/training (s) 19.712 +time/epoch (s) 31.9749 +time/total (s) 14104.7 +Epoch -405 +------------------------------ ---------------- +2022-05-15 21:58:05.066892 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -404 finished +------------------------------ ---------------- +epoch -404 +replay_buffer/size 999047 +trainer/num train calls 597000 +trainer/QF1 Loss 0.934863 +trainer/QF2 Loss 1.07209 +trainer/Policy Loss 12.5202 +trainer/Q1 Predictions Mean -71.6732 +trainer/Q1 Predictions Std 19.2174 +trainer/Q1 Predictions Max -1.01568 +trainer/Q1 Predictions Min -86.3382 +trainer/Q2 Predictions Mean -71.6827 +trainer/Q2 Predictions Std 19.2126 +trainer/Q2 Predictions Max -1.24539 +trainer/Q2 Predictions Min -86.3472 +trainer/Q Targets Mean -71.7016 +trainer/Q Targets Std 19.1817 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3629 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00270694 +trainer/policy/mean Std 0.726277 +trainer/policy/mean Max 0.999238 +trainer/policy/mean Min -0.999659 +trainer/policy/std Mean 0.417472 +trainer/policy/std Std 0.0210909 +trainer/policy/std Max 0.438025 +trainer/policy/std Min 0.383709 +trainer/Advantage Weights Mean 3.28893 +trainer/Advantage Weights Std 16.3356 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.58295e-20 +trainer/Advantage Score Mean -0.537303 +trainer/Advantage Score Std 0.680219 +trainer/Advantage Score Max 3.55908 +trainer/Advantage Score Min -4.51028 +trainer/V1 Predictions Mean -71.4026 +trainer/V1 Predictions Std 19.2777 +trainer/V1 Predictions Max -2.03527 +trainer/V1 Predictions Min -86.257 +trainer/VF Loss 0.127128 +expl/num steps total 597000 +expl/num paths total 767 +expl/path length Mean 500 +expl/path length Std 197 +expl/path length Max 697 +expl/path length Min 303 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0265102 +expl/Actions Std 0.841814 +expl/Actions Max 2.30967 +expl/Actions Min -2.27893 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 560852 +eval/num paths total 601 +eval/path length Mean 527 +eval/path length Std 0 +eval/path length Max 527 +eval/path length Min 527 +eval/Rewards Mean 0.00189753 +eval/Rewards Std 0.0435193 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0341632 +eval/Actions Std 0.742304 +eval/Actions Max 0.999316 +eval/Actions Min -0.999651 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.18782e-05 +time/evaluation sampling (s) 5.59987 +time/exploration sampling (s) 6.4246 +time/logging (s) 0.0098983 +time/saving (s) 0.0158894 +time/training (s) 20.0252 +time/epoch (s) 32.0755 +time/total (s) 14136.8 +Epoch -404 +------------------------------ ---------------- +2022-05-15 21:58:36.142396 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -403 finished +------------------------------ ---------------- +epoch -403 +replay_buffer/size 999047 +trainer/num train calls 598000 +trainer/QF1 Loss 0.928055 +trainer/QF2 Loss 0.979021 +trainer/Policy Loss 31.3637 +trainer/Q1 Predictions Mean -70.4778 +trainer/Q1 Predictions Std 19.4465 +trainer/Q1 Predictions Max -1.48044 +trainer/Q1 Predictions Min -87.9527 +trainer/Q2 Predictions Mean -70.4531 +trainer/Q2 Predictions Std 19.3842 +trainer/Q2 Predictions Max -1.3666 +trainer/Q2 Predictions Min -87.6945 +trainer/Q Targets Mean -70.9052 +trainer/Q Targets Std 19.3126 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9841 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.021573 +trainer/policy/mean Std 0.729233 +trainer/policy/mean Max 0.999631 +trainer/policy/mean Min -0.999496 +trainer/policy/std Mean 0.418232 +trainer/policy/std Std 0.020918 +trainer/policy/std Max 0.438692 +trainer/policy/std Min 0.386381 +trainer/Advantage Weights Mean 8.7438 +trainer/Advantage Weights Std 24.7228 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.40261e-20 +trainer/Advantage Score Mean -0.221249 +trainer/Advantage Score Std 0.617717 +trainer/Advantage Score Max 2.02246 +trainer/Advantage Score Min -4.57134 +trainer/V1 Predictions Mean -70.5827 +trainer/V1 Predictions Std 19.4798 +trainer/V1 Predictions Max -0.694653 +trainer/V1 Predictions Min -88.0119 +trainer/VF Loss 0.0871971 +expl/num steps total 598000 +expl/num paths total 768 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0253931 +expl/Actions Std 0.82639 +expl/Actions Max 2.46713 +expl/Actions Min -2.30887 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 561797 +eval/num paths total 602 +eval/path length Mean 945 +eval/path length Std 0 +eval/path length Max 945 +eval/path length Min 945 +eval/Rewards Mean 0.0010582 +eval/Rewards Std 0.0325128 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0209162 +eval/Actions Std 0.729702 +eval/Actions Max 0.999645 +eval/Actions Min -0.999811 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.34229e-06 +time/evaluation sampling (s) 4.54212 +time/exploration sampling (s) 6.77535 +time/logging (s) 0.00875983 +time/saving (s) 0.0128899 +time/training (s) 19.7185 +time/epoch (s) 31.0576 +time/total (s) 14167.8 +Epoch -403 +------------------------------ ---------------- +2022-05-15 21:59:07.159314 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -402 finished +------------------------------ ---------------- +epoch -402 +replay_buffer/size 999047 +trainer/num train calls 599000 +trainer/QF1 Loss 1.11678 +trainer/QF2 Loss 1.15477 +trainer/Policy Loss 3.15978 +trainer/Q1 Predictions Mean -72.9357 +trainer/Q1 Predictions Std 18.7143 +trainer/Q1 Predictions Max -1.16392 +trainer/Q1 Predictions Min -87.1167 +trainer/Q2 Predictions Mean -73.0355 +trainer/Q2 Predictions Std 18.7509 +trainer/Q2 Predictions Max -0.74561 +trainer/Q2 Predictions Min -87.1419 +trainer/Q Targets Mean -72.6237 +trainer/Q Targets Std 19.0862 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4279 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0272851 +trainer/policy/mean Std 0.735464 +trainer/policy/mean Max 0.999616 +trainer/policy/mean Min -0.999894 +trainer/policy/std Mean 0.41764 +trainer/policy/std Std 0.0212472 +trainer/policy/std Max 0.439102 +trainer/policy/std Min 0.385585 +trainer/Advantage Weights Mean 0.988576 +trainer/Advantage Weights Std 7.2682 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.09469e-16 +trainer/Advantage Score Mean -0.565445 +trainer/Advantage Score Std 0.557818 +trainer/Advantage Score Max 0.621277 +trainer/Advantage Score Min -3.54317 +trainer/V1 Predictions Mean -72.3752 +trainer/V1 Predictions Std 19.0764 +trainer/V1 Predictions Max -0.500513 +trainer/V1 Predictions Min -86.2494 +trainer/VF Loss 0.0658495 +expl/num steps total 599000 +expl/num paths total 770 +expl/path length Mean 500 +expl/path length Std 59 +expl/path length Max 559 +expl/path length Min 441 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0072762 +expl/Actions Std 0.832808 +expl/Actions Max 2.41775 +expl/Actions Min -2.33192 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 562797 +eval/num paths total 603 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0283595 +eval/Actions Std 0.722659 +eval/Actions Max 0.999708 +eval/Actions Min -0.99962 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.17e-06 +time/evaluation sampling (s) 4.76554 +time/exploration sampling (s) 7.59975 +time/logging (s) 0.01011 +time/saving (s) 0.0184538 +time/training (s) 18.6116 +time/epoch (s) 31.0054 +time/total (s) 14198.8 +Epoch -402 +------------------------------ ---------------- +2022-05-15 21:59:39.553021 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -401 finished +------------------------------ ---------------- +epoch -401 +replay_buffer/size 999047 +trainer/num train calls 600000 +trainer/QF1 Loss 0.498065 +trainer/QF2 Loss 0.578634 +trainer/Policy Loss 12.0522 +trainer/Q1 Predictions Mean -73.7371 +trainer/Q1 Predictions Std 16.1794 +trainer/Q1 Predictions Max -2.48869 +trainer/Q1 Predictions Min -86.236 +trainer/Q2 Predictions Mean -73.7241 +trainer/Q2 Predictions Std 16.2762 +trainer/Q2 Predictions Max -3.49216 +trainer/Q2 Predictions Min -86.7191 +trainer/Q Targets Mean -73.7815 +trainer/Q Targets Std 16.2803 +trainer/Q Targets Max -2.03778 +trainer/Q Targets Min -85.9247 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0145682 +trainer/policy/mean Std 0.729637 +trainer/policy/mean Max 0.99962 +trainer/policy/mean Min -0.998846 +trainer/policy/std Mean 0.417681 +trainer/policy/std Std 0.0198687 +trainer/policy/std Max 0.439262 +trainer/policy/std Min 0.385569 +trainer/Advantage Weights Mean 2.91593 +trainer/Advantage Weights Std 15.3167 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19366e-16 +trainer/Advantage Score Mean -0.432243 +trainer/Advantage Score Std 0.53278 +trainer/Advantage Score Max 1.0545 +trainer/Advantage Score Min -3.66643 +trainer/V1 Predictions Mean -73.5353 +trainer/V1 Predictions Std 16.3231 +trainer/V1 Predictions Max -2.92516 +trainer/V1 Predictions Min -85.752 +trainer/VF Loss 0.058699 +expl/num steps total 600000 +expl/num paths total 771 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.027307 +expl/Actions Std 0.849426 +expl/Actions Max 2.36185 +expl/Actions Min -2.24028 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 563532 +eval/num paths total 604 +eval/path length Mean 735 +eval/path length Std 0 +eval/path length Max 735 +eval/path length Min 735 +eval/Rewards Mean 0.00136054 +eval/Rewards Std 0.0368605 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0082287 +eval/Actions Std 0.742562 +eval/Actions Max 0.999412 +eval/Actions Min -0.998908 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.63983e-05 +time/evaluation sampling (s) 5.41861 +time/exploration sampling (s) 7.48591 +time/logging (s) 0.0100846 +time/saving (s) 0.0153241 +time/training (s) 19.4463 +time/epoch (s) 32.3762 +time/total (s) 14231.2 +Epoch -401 +------------------------------ ---------------- +2022-05-15 22:00:12.884710 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -400 finished +------------------------------ ---------------- +epoch -400 +replay_buffer/size 999047 +trainer/num train calls 601000 +trainer/QF1 Loss 1.98132 +trainer/QF2 Loss 1.8721 +trainer/Policy Loss 1.52499 +trainer/Q1 Predictions Mean -72.9519 +trainer/Q1 Predictions Std 18.7363 +trainer/Q1 Predictions Max -0.352024 +trainer/Q1 Predictions Min -88.7775 +trainer/Q2 Predictions Mean -73.02 +trainer/Q2 Predictions Std 18.6866 +trainer/Q2 Predictions Max -0.433467 +trainer/Q2 Predictions Min -88.6491 +trainer/Q Targets Mean -72.4539 +trainer/Q Targets Std 18.4652 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5444 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000936074 +trainer/policy/mean Std 0.722304 +trainer/policy/mean Max 0.999251 +trainer/policy/mean Min -0.99921 +trainer/policy/std Mean 0.417963 +trainer/policy/std Std 0.0197747 +trainer/policy/std Max 0.437982 +trainer/policy/std Min 0.389237 +trainer/Advantage Weights Mean 0.613575 +trainer/Advantage Weights Std 4.60603 +trainer/Advantage Weights Max 68.9827 +trainer/Advantage Weights Min 1.42739e-23 +trainer/Advantage Score Mean -0.641841 +trainer/Advantage Score Std 0.636077 +trainer/Advantage Score Max 0.423386 +trainer/Advantage Score Min -5.26036 +trainer/V1 Predictions Mean -72.2705 +trainer/V1 Predictions Std 18.6544 +trainer/V1 Predictions Max -0.124004 +trainer/V1 Predictions Min -88.2678 +trainer/VF Loss 0.0832645 +expl/num steps total 601000 +expl/num paths total 772 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0638358 +expl/Actions Std 0.810122 +expl/Actions Max 2.17417 +expl/Actions Min -2.14703 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 564532 +eval/num paths total 605 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.374137 +eval/Actions Std 0.572867 +eval/Actions Max 0.999366 +eval/Actions Min -0.999045 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27107e-05 +time/evaluation sampling (s) 5.68295 +time/exploration sampling (s) 7.93578 +time/logging (s) 0.0126734 +time/saving (s) 0.0356323 +time/training (s) 19.6507 +time/epoch (s) 33.3178 +time/total (s) 14264.6 +Epoch -400 +------------------------------ ---------------- +2022-05-15 22:00:44.903948 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -399 finished +------------------------------ ---------------- +epoch -399 +replay_buffer/size 999047 +trainer/num train calls 602000 +trainer/QF1 Loss 0.928236 +trainer/QF2 Loss 0.871051 +trainer/Policy Loss 16.1493 +trainer/Q1 Predictions Mean -73.5246 +trainer/Q1 Predictions Std 17.4518 +trainer/Q1 Predictions Max -0.806178 +trainer/Q1 Predictions Min -87.2026 +trainer/Q2 Predictions Mean -73.4283 +trainer/Q2 Predictions Std 17.4693 +trainer/Q2 Predictions Max -0.431524 +trainer/Q2 Predictions Min -87.0082 +trainer/Q Targets Mean -73.0009 +trainer/Q Targets Std 17.335 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9368 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0181495 +trainer/policy/mean Std 0.71628 +trainer/policy/mean Max 0.998485 +trainer/policy/mean Min -0.998252 +trainer/policy/std Mean 0.418896 +trainer/policy/std Std 0.0193701 +trainer/policy/std Max 0.440789 +trainer/policy/std Min 0.389972 +trainer/Advantage Weights Mean 4.96379 +trainer/Advantage Weights Std 19.9339 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.23543e-20 +trainer/Advantage Score Mean -0.401113 +trainer/Advantage Score Std 0.624884 +trainer/Advantage Score Max 1.12027 +trainer/Advantage Score Min -4.52473 +trainer/V1 Predictions Mean -72.8468 +trainer/V1 Predictions Std 17.3297 +trainer/V1 Predictions Max -0.423449 +trainer/V1 Predictions Min -86.7504 +trainer/VF Loss 0.0775069 +expl/num steps total 602000 +expl/num paths total 773 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.475143 +expl/Actions Std 0.778331 +expl/Actions Max 2.27208 +expl/Actions Min -2.54348 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 564995 +eval/num paths total 606 +eval/path length Mean 463 +eval/path length Std 0 +eval/path length Max 463 +eval/path length Min 463 +eval/Rewards Mean 0.00215983 +eval/Rewards Std 0.0464237 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0127906 +eval/Actions Std 0.726784 +eval/Actions Max 0.998364 +eval/Actions Min -0.998856 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.7372e-06 +time/evaluation sampling (s) 5.13448 +time/exploration sampling (s) 7.38397 +time/logging (s) 0.00799907 +time/saving (s) 0.0154087 +time/training (s) 19.4542 +time/epoch (s) 31.996 +time/total (s) 14296.6 +Epoch -399 +------------------------------ ---------------- +2022-05-15 22:01:16.043896 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -398 finished +------------------------------ ---------------- +epoch -398 +replay_buffer/size 999047 +trainer/num train calls 603000 +trainer/QF1 Loss 0.660087 +trainer/QF2 Loss 0.961167 +trainer/Policy Loss 23.5447 +trainer/Q1 Predictions Mean -71.8637 +trainer/Q1 Predictions Std 19.1734 +trainer/Q1 Predictions Max -2.60411 +trainer/Q1 Predictions Min -86.3995 +trainer/Q2 Predictions Mean -71.8226 +trainer/Q2 Predictions Std 19.1292 +trainer/Q2 Predictions Max -1.11952 +trainer/Q2 Predictions Min -86.2756 +trainer/Q Targets Mean -72.1354 +trainer/Q Targets Std 19.2124 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1983 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0138921 +trainer/policy/mean Std 0.73003 +trainer/policy/mean Max 0.999854 +trainer/policy/mean Min -0.999293 +trainer/policy/std Mean 0.418432 +trainer/policy/std Std 0.0189072 +trainer/policy/std Max 0.439278 +trainer/policy/std Min 0.389104 +trainer/Advantage Weights Mean 5.02724 +trainer/Advantage Weights Std 17.9095 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.39973e-20 +trainer/Advantage Score Mean -0.3533 +trainer/Advantage Score Std 0.737126 +trainer/Advantage Score Max 1.69675 +trainer/Advantage Score Min -4.51763 +trainer/V1 Predictions Mean -71.8257 +trainer/V1 Predictions Std 19.4572 +trainer/V1 Predictions Max -1.61982 +trainer/V1 Predictions Min -86.1307 +trainer/VF Loss 0.10019 +expl/num steps total 603000 +expl/num paths total 775 +expl/path length Mean 500 +expl/path length Std 46 +expl/path length Max 546 +expl/path length Min 454 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0344005 +expl/Actions Std 0.83337 +expl/Actions Max 2.49701 +expl/Actions Min -2.38829 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 565995 +eval/num paths total 607 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0359645 +eval/Actions Std 0.680012 +eval/Actions Max 0.999971 +eval/Actions Min -0.999777 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.88315e-06 +time/evaluation sampling (s) 5.26012 +time/exploration sampling (s) 6.57156 +time/logging (s) 0.00993189 +time/saving (s) 0.0147836 +time/training (s) 19.2734 +time/epoch (s) 31.1298 +time/total (s) 14327.7 +Epoch -398 +------------------------------ ---------------- +2022-05-15 22:01:48.410894 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -397 finished +------------------------------ ---------------- +epoch -397 +replay_buffer/size 999047 +trainer/num train calls 604000 +trainer/QF1 Loss 0.995335 +trainer/QF2 Loss 0.984769 +trainer/Policy Loss 8.18172 +trainer/Q1 Predictions Mean -73.4458 +trainer/Q1 Predictions Std 16.7506 +trainer/Q1 Predictions Max -2.27045 +trainer/Q1 Predictions Min -88.3979 +trainer/Q2 Predictions Mean -73.4626 +trainer/Q2 Predictions Std 16.7916 +trainer/Q2 Predictions Max -3.04062 +trainer/Q2 Predictions Min -88.0403 +trainer/Q Targets Mean -73.0082 +trainer/Q Targets Std 16.9376 +trainer/Q Targets Max -3.19742 +trainer/Q Targets Min -87.3703 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0318739 +trainer/policy/mean Std 0.719308 +trainer/policy/mean Max 0.999593 +trainer/policy/mean Min -0.998809 +trainer/policy/std Mean 0.417423 +trainer/policy/std Std 0.0206128 +trainer/policy/std Max 0.439125 +trainer/policy/std Min 0.385198 +trainer/Advantage Weights Mean 1.61258 +trainer/Advantage Weights Std 11.1102 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.82575e-23 +trainer/Advantage Score Mean -0.639155 +trainer/Advantage Score Std 0.672781 +trainer/Advantage Score Max 0.796539 +trainer/Advantage Score Min -5.07818 +trainer/V1 Predictions Mean -72.803 +trainer/V1 Predictions Std 16.9441 +trainer/V1 Predictions Max -1.32205 +trainer/V1 Predictions Min -87.3146 +trainer/VF Loss 0.0927914 +expl/num steps total 604000 +expl/num paths total 777 +expl/path length Mean 500 +expl/path length Std 36 +expl/path length Max 536 +expl/path length Min 464 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0296751 +expl/Actions Std 0.825885 +expl/Actions Max 2.33652 +expl/Actions Min -2.2868 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 566460 +eval/num paths total 608 +eval/path length Mean 465 +eval/path length Std 0 +eval/path length Max 465 +eval/path length Min 465 +eval/Rewards Mean 0.00215054 +eval/Rewards Std 0.046324 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0133347 +eval/Actions Std 0.739498 +eval/Actions Max 0.998957 +eval/Actions Min -0.99922 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0008e-05 +time/evaluation sampling (s) 5.33613 +time/exploration sampling (s) 7.14007 +time/logging (s) 0.00899394 +time/saving (s) 0.0162243 +time/training (s) 19.8477 +time/epoch (s) 32.3492 +time/total (s) 14360.1 +Epoch -397 +------------------------------ ---------------- +2022-05-15 22:02:20.476430 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -396 finished +------------------------------ ---------------- +epoch -396 +replay_buffer/size 999047 +trainer/num train calls 605000 +trainer/QF1 Loss 0.857897 +trainer/QF2 Loss 1.05104 +trainer/Policy Loss 10.4546 +trainer/Q1 Predictions Mean -72.8328 +trainer/Q1 Predictions Std 16.9529 +trainer/Q1 Predictions Max -3.81539 +trainer/Q1 Predictions Min -86.6377 +trainer/Q2 Predictions Mean -72.9367 +trainer/Q2 Predictions Std 16.8071 +trainer/Q2 Predictions Max -4.57974 +trainer/Q2 Predictions Min -86.664 +trainer/Q Targets Mean -72.7097 +trainer/Q Targets Std 17.2861 +trainer/Q Targets Max -1.42951 +trainer/Q Targets Min -86.6286 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0308628 +trainer/policy/mean Std 0.728813 +trainer/policy/mean Max 0.999115 +trainer/policy/mean Min -0.997629 +trainer/policy/std Mean 0.416715 +trainer/policy/std Std 0.0210787 +trainer/policy/std Max 0.44298 +trainer/policy/std Min 0.38216 +trainer/Advantage Weights Mean 2.80361 +trainer/Advantage Weights Std 12.1558 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.46027e-23 +trainer/Advantage Score Mean -0.496424 +trainer/Advantage Score Std 0.840001 +trainer/Advantage Score Max 2.00714 +trainer/Advantage Score Min -5.25808 +trainer/V1 Predictions Mean -72.4429 +trainer/V1 Predictions Std 17.2062 +trainer/V1 Predictions Max -2.96407 +trainer/V1 Predictions Min -86.5174 +trainer/VF Loss 0.116731 +expl/num steps total 605000 +expl/num paths total 779 +expl/path length Mean 500 +expl/path length Std 33 +expl/path length Max 533 +expl/path length Min 467 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0291647 +expl/Actions Std 0.837482 +expl/Actions Max 2.39882 +expl/Actions Min -2.44258 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 567460 +eval/num paths total 609 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0964299 +eval/Actions Std 0.750923 +eval/Actions Max 0.999872 +eval/Actions Min -0.999454 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.04601e-05 +time/evaluation sampling (s) 4.71338 +time/exploration sampling (s) 7.24383 +time/logging (s) 0.0125127 +time/saving (s) 0.0227619 +time/training (s) 20.0593 +time/epoch (s) 32.0518 +time/total (s) 14392.1 +Epoch -396 +------------------------------ ---------------- +2022-05-15 22:02:51.332285 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -395 finished +------------------------------ ---------------- +epoch -395 +replay_buffer/size 999047 +trainer/num train calls 606000 +trainer/QF1 Loss 1.15416 +trainer/QF2 Loss 1.04836 +trainer/Policy Loss 23.3202 +trainer/Q1 Predictions Mean -72.8537 +trainer/Q1 Predictions Std 18.6573 +trainer/Q1 Predictions Max -0.930948 +trainer/Q1 Predictions Min -86.9449 +trainer/Q2 Predictions Mean -72.9011 +trainer/Q2 Predictions Std 18.633 +trainer/Q2 Predictions Max -0.359707 +trainer/Q2 Predictions Min -86.2081 +trainer/Q Targets Mean -73.1178 +trainer/Q Targets Std 19.0833 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0162 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0225039 +trainer/policy/mean Std 0.731862 +trainer/policy/mean Max 0.9991 +trainer/policy/mean Min -0.999589 +trainer/policy/std Mean 0.415862 +trainer/policy/std Std 0.0214932 +trainer/policy/std Max 0.440935 +trainer/policy/std Min 0.381517 +trainer/Advantage Weights Mean 5.9526 +trainer/Advantage Weights Std 19.8221 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.73853e-21 +trainer/Advantage Score Mean -0.279052 +trainer/Advantage Score Std 0.658541 +trainer/Advantage Score Max 1.36609 +trainer/Advantage Score Min -4.78012 +trainer/V1 Predictions Mean -72.8499 +trainer/V1 Predictions Std 19.1245 +trainer/V1 Predictions Max 1.4107 +trainer/V1 Predictions Min -86.7821 +trainer/VF Loss 0.080911 +expl/num steps total 606000 +expl/num paths total 781 +expl/path length Mean 500 +expl/path length Std 485 +expl/path length Max 985 +expl/path length Min 15 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0548503 +expl/Actions Std 0.802674 +expl/Actions Max 2.45929 +expl/Actions Min -2.54056 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 568460 +eval/num paths total 610 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.144806 +eval/Actions Std 0.776451 +eval/Actions Max 0.999739 +eval/Actions Min -0.999462 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.93209e-06 +time/evaluation sampling (s) 5.09639 +time/exploration sampling (s) 6.46257 +time/logging (s) 0.0114853 +time/saving (s) 0.0154333 +time/training (s) 19.2504 +time/epoch (s) 30.8363 +time/total (s) 14423 +Epoch -395 +------------------------------ ---------------- +2022-05-15 22:03:22.839615 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -394 finished +------------------------------ ---------------- +epoch -394 +replay_buffer/size 999047 +trainer/num train calls 607000 +trainer/QF1 Loss 1.25137 +trainer/QF2 Loss 1.32271 +trainer/Policy Loss 29.299 +trainer/Q1 Predictions Mean -70.6142 +trainer/Q1 Predictions Std 21.5091 +trainer/Q1 Predictions Max -0.777506 +trainer/Q1 Predictions Min -88.038 +trainer/Q2 Predictions Mean -70.6494 +trainer/Q2 Predictions Std 21.4341 +trainer/Q2 Predictions Max 0.675467 +trainer/Q2 Predictions Min -87.9827 +trainer/Q Targets Mean -70.9349 +trainer/Q Targets Std 21.5779 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.6372 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.034475 +trainer/policy/mean Std 0.729848 +trainer/policy/mean Max 0.999701 +trainer/policy/mean Min -0.999603 +trainer/policy/std Mean 0.415038 +trainer/policy/std Std 0.020341 +trainer/policy/std Max 0.438974 +trainer/policy/std Min 0.38275 +trainer/Advantage Weights Mean 5.10388 +trainer/Advantage Weights Std 18.7467 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.74589e-15 +trainer/Advantage Score Mean -0.395842 +trainer/Advantage Score Std 0.65676 +trainer/Advantage Score Max 0.941814 +trainer/Advantage Score Min -3.39815 +trainer/V1 Predictions Mean -70.6705 +trainer/V1 Predictions Std 21.6581 +trainer/V1 Predictions Max -0.716443 +trainer/V1 Predictions Min -88.5507 +trainer/VF Loss 0.0731134 +expl/num steps total 607000 +expl/num paths total 782 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.102061 +expl/Actions Std 0.877168 +expl/Actions Max 2.57588 +expl/Actions Min -2.38328 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 569460 +eval/num paths total 611 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0222667 +eval/Actions Std 0.74306 +eval/Actions Max 0.999824 +eval/Actions Min -0.999822 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.16603e-06 +time/evaluation sampling (s) 4.94175 +time/exploration sampling (s) 6.95958 +time/logging (s) 0.0121797 +time/saving (s) 0.0152738 +time/training (s) 19.5634 +time/epoch (s) 31.4922 +time/total (s) 14454.5 +Epoch -394 +------------------------------ ---------------- +2022-05-15 22:03:55.197512 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -393 finished +------------------------------ ---------------- +epoch -393 +replay_buffer/size 999047 +trainer/num train calls 608000 +trainer/QF1 Loss 1.53688 +trainer/QF2 Loss 1.72543 +trainer/Policy Loss 65.2161 +trainer/Q1 Predictions Mean -71.3616 +trainer/Q1 Predictions Std 19.6424 +trainer/Q1 Predictions Max -0.207261 +trainer/Q1 Predictions Min -86.0187 +trainer/Q2 Predictions Mean -71.4193 +trainer/Q2 Predictions Std 19.5822 +trainer/Q2 Predictions Max -0.356749 +trainer/Q2 Predictions Min -86.6387 +trainer/Q Targets Mean -71.6904 +trainer/Q Targets Std 19.7545 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3696 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0132362 +trainer/policy/mean Std 0.738735 +trainer/policy/mean Max 0.998988 +trainer/policy/mean Min -0.999186 +trainer/policy/std Mean 0.415113 +trainer/policy/std Std 0.0208828 +trainer/policy/std Max 0.438346 +trainer/policy/std Min 0.381522 +trainer/Advantage Weights Mean 9.84502 +trainer/Advantage Weights Std 24.1675 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.02603e-14 +trainer/Advantage Score Mean -0.217744 +trainer/Advantage Score Std 0.596232 +trainer/Advantage Score Max 2.75535 +trainer/Advantage Score Min -3.15301 +trainer/V1 Predictions Mean -71.4871 +trainer/V1 Predictions Std 19.747 +trainer/V1 Predictions Max -0.713563 +trainer/V1 Predictions Min -86.37 +trainer/VF Loss 0.0912338 +expl/num steps total 608000 +expl/num paths total 783 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.270424 +expl/Actions Std 0.764521 +expl/Actions Max 2.33433 +expl/Actions Min -2.1509 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 570460 +eval/num paths total 612 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.244062 +eval/Actions Std 0.652611 +eval/Actions Max 0.999363 +eval/Actions Min -0.999714 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.21377e-06 +time/evaluation sampling (s) 5.01946 +time/exploration sampling (s) 7.97715 +time/logging (s) 0.00828603 +time/saving (s) 0.0155167 +time/training (s) 19.3187 +time/epoch (s) 32.3391 +time/total (s) 14486.8 +Epoch -393 +------------------------------ ---------------- +2022-05-15 22:04:26.741281 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -392 finished +------------------------------ ---------------- +epoch -392 +replay_buffer/size 999047 +trainer/num train calls 609000 +trainer/QF1 Loss 6.11071 +trainer/QF2 Loss 6.15337 +trainer/Policy Loss 16.7502 +trainer/Q1 Predictions Mean -72.8976 +trainer/Q1 Predictions Std 17.9457 +trainer/Q1 Predictions Max -0.642351 +trainer/Q1 Predictions Min -85.7366 +trainer/Q2 Predictions Mean -73.032 +trainer/Q2 Predictions Std 18.0234 +trainer/Q2 Predictions Max -0.920527 +trainer/Q2 Predictions Min -85.7546 +trainer/Q Targets Mean -72.864 +trainer/Q Targets Std 18.0826 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9134 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00109566 +trainer/policy/mean Std 0.726155 +trainer/policy/mean Max 0.999772 +trainer/policy/mean Min -0.998928 +trainer/policy/std Mean 0.415315 +trainer/policy/std Std 0.0209217 +trainer/policy/std Max 0.439599 +trainer/policy/std Min 0.382367 +trainer/Advantage Weights Mean 4.7727 +trainer/Advantage Weights Std 18.9368 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.72781e-13 +trainer/Advantage Score Mean -0.404006 +trainer/Advantage Score Std 0.606805 +trainer/Advantage Score Max 2.09524 +trainer/Advantage Score Min -2.93867 +trainer/V1 Predictions Mean -72.7602 +trainer/V1 Predictions Std 18.0322 +trainer/V1 Predictions Max -0.317221 +trainer/V1 Predictions Min -85.6717 +trainer/VF Loss 0.0845609 +expl/num steps total 609000 +expl/num paths total 784 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0707042 +expl/Actions Std 0.837563 +expl/Actions Max 2.5947 +expl/Actions Min -2.62451 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 571202 +eval/num paths total 613 +eval/path length Mean 742 +eval/path length Std 0 +eval/path length Max 742 +eval/path length Min 742 +eval/Rewards Mean 0.00134771 +eval/Rewards Std 0.0366864 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0518559 +eval/Actions Std 0.720944 +eval/Actions Max 0.999448 +eval/Actions Min -0.999624 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.20611e-05 +time/evaluation sampling (s) 5.41812 +time/exploration sampling (s) 7.00453 +time/logging (s) 0.0109231 +time/saving (s) 0.0172359 +time/training (s) 19.0827 +time/epoch (s) 31.5335 +time/total (s) 14518.3 +Epoch -392 +------------------------------ ---------------- +2022-05-15 22:04:59.329768 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -391 finished +------------------------------ ---------------- +epoch -391 +replay_buffer/size 999047 +trainer/num train calls 610000 +trainer/QF1 Loss 5.6001 +trainer/QF2 Loss 5.63213 +trainer/Policy Loss 20.1811 +trainer/Q1 Predictions Mean -71.8256 +trainer/Q1 Predictions Std 18.7691 +trainer/Q1 Predictions Max -1.28537 +trainer/Q1 Predictions Min -86.279 +trainer/Q2 Predictions Mean -71.7828 +trainer/Q2 Predictions Std 18.6484 +trainer/Q2 Predictions Max -0.401862 +trainer/Q2 Predictions Min -86.1237 +trainer/Q Targets Mean -71.5431 +trainer/Q Targets Std 18.8549 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8788 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0257592 +trainer/policy/mean Std 0.730612 +trainer/policy/mean Max 0.999118 +trainer/policy/mean Min -0.999222 +trainer/policy/std Mean 0.415527 +trainer/policy/std Std 0.0210895 +trainer/policy/std Max 0.438262 +trainer/policy/std Min 0.380954 +trainer/Advantage Weights Mean 5.02686 +trainer/Advantage Weights Std 19.9529 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.51274e-15 +trainer/Advantage Score Mean -0.360904 +trainer/Advantage Score Std 0.618966 +trainer/Advantage Score Max 2.01452 +trainer/Advantage Score Min -3.36174 +trainer/V1 Predictions Mean -71.4894 +trainer/V1 Predictions Std 18.7447 +trainer/V1 Predictions Max -0.581301 +trainer/V1 Predictions Min -85.7449 +trainer/VF Loss 0.0906423 +expl/num steps total 610000 +expl/num paths total 786 +expl/path length Mean 500 +expl/path length Std 387 +expl/path length Max 887 +expl/path length Min 113 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0451292 +expl/Actions Std 0.825379 +expl/Actions Max 2.52707 +expl/Actions Min -2.24181 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 572202 +eval/num paths total 614 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0104823 +eval/Actions Std 0.649363 +eval/Actions Max 0.999679 +eval/Actions Min -0.999473 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.06208e-05 +time/evaluation sampling (s) 5.72505 +time/exploration sampling (s) 6.87361 +time/logging (s) 0.00913593 +time/saving (s) 0.0132284 +time/training (s) 19.9491 +time/epoch (s) 32.5701 +time/total (s) 14550.9 +Epoch -391 +------------------------------ ---------------- +2022-05-15 22:05:31.169084 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -390 finished +------------------------------ ---------------- +epoch -390 +replay_buffer/size 999047 +trainer/num train calls 611000 +trainer/QF1 Loss 0.660886 +trainer/QF2 Loss 0.807787 +trainer/Policy Loss 14.6847 +trainer/Q1 Predictions Mean -72.0583 +trainer/Q1 Predictions Std 19.3393 +trainer/Q1 Predictions Max -0.56071 +trainer/Q1 Predictions Min -87.3433 +trainer/Q2 Predictions Mean -72.0856 +trainer/Q2 Predictions Std 19.3397 +trainer/Q2 Predictions Max -0.0958084 +trainer/Q2 Predictions Min -87.0003 +trainer/Q Targets Mean -72.406 +trainer/Q Targets Std 19.3123 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.95 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0157987 +trainer/policy/mean Std 0.735356 +trainer/policy/mean Max 0.999641 +trainer/policy/mean Min -0.999481 +trainer/policy/std Mean 0.415163 +trainer/policy/std Std 0.0207174 +trainer/policy/std Max 0.437534 +trainer/policy/std Min 0.383978 +trainer/Advantage Weights Mean 4.05165 +trainer/Advantage Weights Std 14.2 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.43751e-14 +trainer/Advantage Score Mean -0.295929 +trainer/Advantage Score Std 0.588992 +trainer/Advantage Score Max 0.844513 +trainer/Advantage Score Min -3.05429 +trainer/V1 Predictions Mean -72.1404 +trainer/V1 Predictions Std 19.4273 +trainer/V1 Predictions Max -0.789053 +trainer/V1 Predictions Min -87.2514 +trainer/VF Loss 0.0568103 +expl/num steps total 611000 +expl/num paths total 788 +expl/path length Mean 500 +expl/path length Std 79 +expl/path length Max 579 +expl/path length Min 421 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0379282 +expl/Actions Std 0.840322 +expl/Actions Max 2.34275 +expl/Actions Min -2.50475 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 573202 +eval/num paths total 615 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0425703 +eval/Actions Std 0.725283 +eval/Actions Max 0.99994 +eval/Actions Min -0.999187 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.96487e-06 +time/evaluation sampling (s) 5.19924 +time/exploration sampling (s) 7.18364 +time/logging (s) 0.0125365 +time/saving (s) 0.0176742 +time/training (s) 19.4174 +time/epoch (s) 31.8305 +time/total (s) 14582.8 +Epoch -390 +------------------------------ ---------------- +2022-05-15 22:06:03.518309 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -389 finished +------------------------------ ---------------- +epoch -389 +replay_buffer/size 999047 +trainer/num train calls 612000 +trainer/QF1 Loss 1.07283 +trainer/QF2 Loss 0.99606 +trainer/Policy Loss 33.2582 +trainer/Q1 Predictions Mean -71.0751 +trainer/Q1 Predictions Std 19.3604 +trainer/Q1 Predictions Max -0.741868 +trainer/Q1 Predictions Min -86.7752 +trainer/Q2 Predictions Mean -70.9557 +trainer/Q2 Predictions Std 19.299 +trainer/Q2 Predictions Max -0.957871 +trainer/Q2 Predictions Min -86.9342 +trainer/Q Targets Mean -71.4492 +trainer/Q Targets Std 19.3273 +trainer/Q Targets Max -1.56556 +trainer/Q Targets Min -86.9738 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00363233 +trainer/policy/mean Std 0.720538 +trainer/policy/mean Max 0.999407 +trainer/policy/mean Min -0.999836 +trainer/policy/std Mean 0.416198 +trainer/policy/std Std 0.019537 +trainer/policy/std Max 0.435507 +trainer/policy/std Min 0.384114 +trainer/Advantage Weights Mean 7.41514 +trainer/Advantage Weights Std 22.8504 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.92232e-18 +trainer/Advantage Score Mean -0.331367 +trainer/Advantage Score Std 0.762077 +trainer/Advantage Score Max 2.07392 +trainer/Advantage Score Min -3.98528 +trainer/V1 Predictions Mean -71.0814 +trainer/V1 Predictions Std 19.5572 +trainer/V1 Predictions Max -0.780758 +trainer/V1 Predictions Min -86.9167 +trainer/VF Loss 0.116497 +expl/num steps total 612000 +expl/num paths total 790 +expl/path length Mean 500 +expl/path length Std 428 +expl/path length Max 928 +expl/path length Min 72 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0493609 +expl/Actions Std 0.821086 +expl/Actions Max 2.47978 +expl/Actions Min -2.21381 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 573854 +eval/num paths total 616 +eval/path length Mean 652 +eval/path length Std 0 +eval/path length Max 652 +eval/path length Min 652 +eval/Rewards Mean 0.00153374 +eval/Rewards Std 0.039133 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0116271 +eval/Actions Std 0.722971 +eval/Actions Max 0.999913 +eval/Actions Min -0.999722 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.16471e-05 +time/evaluation sampling (s) 4.79321 +time/exploration sampling (s) 7.80931 +time/logging (s) 0.0106953 +time/saving (s) 0.0181426 +time/training (s) 19.6986 +time/epoch (s) 32.33 +time/total (s) 14615.1 +Epoch -389 +------------------------------ ---------------- +2022-05-15 22:06:35.477278 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -388 finished +------------------------------ ---------------- +epoch -388 +replay_buffer/size 999047 +trainer/num train calls 613000 +trainer/QF1 Loss 0.508426 +trainer/QF2 Loss 0.522772 +trainer/Policy Loss 38.9654 +trainer/Q1 Predictions Mean -74.4118 +trainer/Q1 Predictions Std 15.5168 +trainer/Q1 Predictions Max -1.0247 +trainer/Q1 Predictions Min -86.67 +trainer/Q2 Predictions Mean -74.429 +trainer/Q2 Predictions Std 15.543 +trainer/Q2 Predictions Max -1.92759 +trainer/Q2 Predictions Min -86.9413 +trainer/Q Targets Mean -74.5733 +trainer/Q Targets Std 15.4412 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8405 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.035018 +trainer/policy/mean Std 0.720955 +trainer/policy/mean Max 0.999409 +trainer/policy/mean Min -0.999205 +trainer/policy/std Mean 0.416211 +trainer/policy/std Std 0.0201497 +trainer/policy/std Max 0.435444 +trainer/policy/std Min 0.387339 +trainer/Advantage Weights Mean 7.89654 +trainer/Advantage Weights Std 22.0942 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.53872e-17 +trainer/Advantage Score Mean -0.0977827 +trainer/Advantage Score Std 0.53222 +trainer/Advantage Score Max 3.92003 +trainer/Advantage Score Min -3.72662 +trainer/V1 Predictions Mean -74.3215 +trainer/V1 Predictions Std 15.5263 +trainer/V1 Predictions Max -1.42735 +trainer/V1 Predictions Min -86.5273 +trainer/VF Loss 0.110624 +expl/num steps total 613000 +expl/num paths total 792 +expl/path length Mean 500 +expl/path length Std 298 +expl/path length Max 798 +expl/path length Min 202 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00849771 +expl/Actions Std 0.826739 +expl/Actions Max 2.43129 +expl/Actions Min -2.25631 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 574597 +eval/num paths total 617 +eval/path length Mean 743 +eval/path length Std 0 +eval/path length Max 743 +eval/path length Min 743 +eval/Rewards Mean 0.0013459 +eval/Rewards Std 0.0366617 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0509613 +eval/Actions Std 0.74349 +eval/Actions Max 0.999947 +eval/Actions Min -0.999722 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.14329e-05 +time/evaluation sampling (s) 5.24159 +time/exploration sampling (s) 6.70908 +time/logging (s) 0.00956869 +time/saving (s) 0.0129392 +time/training (s) 19.9667 +time/epoch (s) 31.9399 +time/total (s) 14647 +Epoch -388 +------------------------------ ---------------- +2022-05-15 22:07:07.710693 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -387 finished +------------------------------ ---------------- +epoch -387 +replay_buffer/size 999047 +trainer/num train calls 614000 +trainer/QF1 Loss 0.430857 +trainer/QF2 Loss 0.471172 +trainer/Policy Loss 16.9343 +trainer/Q1 Predictions Mean -73.2136 +trainer/Q1 Predictions Std 17.7078 +trainer/Q1 Predictions Max 1.01133 +trainer/Q1 Predictions Min -86.353 +trainer/Q2 Predictions Mean -73.3452 +trainer/Q2 Predictions Std 17.7448 +trainer/Q2 Predictions Max 0.40176 +trainer/Q2 Predictions Min -86.2481 +trainer/Q Targets Mean -73.2023 +trainer/Q Targets Std 17.655 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1176 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -7.73417e-06 +trainer/policy/mean Std 0.730435 +trainer/policy/mean Max 0.998813 +trainer/policy/mean Min -0.999606 +trainer/policy/std Mean 0.416725 +trainer/policy/std Std 0.0200304 +trainer/policy/std Max 0.437447 +trainer/policy/std Min 0.388498 +trainer/Advantage Weights Mean 3.57055 +trainer/Advantage Weights Std 15.4557 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.27329e-14 +trainer/Advantage Score Mean -0.341479 +trainer/Advantage Score Std 0.582986 +trainer/Advantage Score Max 3.35373 +trainer/Advantage Score Min -3.19946 +trainer/V1 Predictions Mean -72.9664 +trainer/V1 Predictions Std 17.751 +trainer/V1 Predictions Max -0.146525 +trainer/V1 Predictions Min -86.1689 +trainer/VF Loss 0.0987929 +expl/num steps total 614000 +expl/num paths total 793 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.199573 +expl/Actions Std 0.799274 +expl/Actions Max 2.32724 +expl/Actions Min -2.2042 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 575095 +eval/num paths total 618 +eval/path length Mean 498 +eval/path length Std 0 +eval/path length Max 498 +eval/path length Min 498 +eval/Rewards Mean 0.00200803 +eval/Rewards Std 0.0447661 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0207375 +eval/Actions Std 0.747384 +eval/Actions Max 0.999905 +eval/Actions Min -0.999691 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0096e-05 +time/evaluation sampling (s) 5.41279 +time/exploration sampling (s) 7.31745 +time/logging (s) 0.00865672 +time/saving (s) 0.0149 +time/training (s) 19.4583 +time/epoch (s) 32.2121 +time/total (s) 14679.3 +Epoch -387 +------------------------------ ---------------- +2022-05-15 22:07:38.450723 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -386 finished +------------------------------ ---------------- +epoch -386 +replay_buffer/size 999047 +trainer/num train calls 615000 +trainer/QF1 Loss 0.908773 +trainer/QF2 Loss 1.0386 +trainer/Policy Loss 11.4461 +trainer/Q1 Predictions Mean -72.9594 +trainer/Q1 Predictions Std 17.6311 +trainer/Q1 Predictions Max -0.604011 +trainer/Q1 Predictions Min -86.2884 +trainer/Q2 Predictions Mean -72.963 +trainer/Q2 Predictions Std 17.6503 +trainer/Q2 Predictions Max -0.860366 +trainer/Q2 Predictions Min -86.2866 +trainer/Q Targets Mean -72.9347 +trainer/Q Targets Std 17.3897 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1553 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00950775 +trainer/policy/mean Std 0.725157 +trainer/policy/mean Max 0.999911 +trainer/policy/mean Min -0.999592 +trainer/policy/std Mean 0.416566 +trainer/policy/std Std 0.0210541 +trainer/policy/std Max 0.437162 +trainer/policy/std Min 0.384058 +trainer/Advantage Weights Mean 1.93428 +trainer/Advantage Weights Std 11.3275 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.72032e-13 +trainer/Advantage Score Mean -0.445602 +trainer/Advantage Score Std 0.46361 +trainer/Advantage Score Max 1.48962 +trainer/Advantage Score Min -2.83817 +trainer/V1 Predictions Mean -72.6282 +trainer/V1 Predictions Std 17.5532 +trainer/V1 Predictions Max -0.0837638 +trainer/V1 Predictions Min -85.8774 +trainer/VF Loss 0.0566611 +expl/num steps total 615000 +expl/num paths total 795 +expl/path length Mean 500 +expl/path length Std 487 +expl/path length Max 987 +expl/path length Min 13 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0373612 +expl/Actions Std 0.821651 +expl/Actions Max 2.41597 +expl/Actions Min -2.37894 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 576095 +eval/num paths total 619 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0109167 +eval/Actions Std 0.700648 +eval/Actions Max 0.999696 +eval/Actions Min -0.99982 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.02424e-06 +time/evaluation sampling (s) 5.07661 +time/exploration sampling (s) 6.51983 +time/logging (s) 0.0122569 +time/saving (s) 0.0184104 +time/training (s) 19.1025 +time/epoch (s) 30.7296 +time/total (s) 14710 +Epoch -386 +------------------------------ ---------------- +2022-05-15 22:08:09.629177 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -385 finished +------------------------------ ---------------- +epoch -385 +replay_buffer/size 999047 +trainer/num train calls 616000 +trainer/QF1 Loss 0.538715 +trainer/QF2 Loss 0.459912 +trainer/Policy Loss 26.9678 +trainer/Q1 Predictions Mean -72.3327 +trainer/Q1 Predictions Std 18.3409 +trainer/Q1 Predictions Max -1.38754 +trainer/Q1 Predictions Min -89.0636 +trainer/Q2 Predictions Mean -72.2513 +trainer/Q2 Predictions Std 18.4019 +trainer/Q2 Predictions Max -1.71003 +trainer/Q2 Predictions Min -88.8594 +trainer/Q Targets Mean -72.2402 +trainer/Q Targets Std 18.4413 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4791 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00113496 +trainer/policy/mean Std 0.721157 +trainer/policy/mean Max 0.998846 +trainer/policy/mean Min -0.996269 +trainer/policy/std Mean 0.416399 +trainer/policy/std Std 0.0211588 +trainer/policy/std Max 0.437737 +trainer/policy/std Min 0.385417 +trainer/Advantage Weights Mean 4.86106 +trainer/Advantage Weights Std 17.7934 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.30195e-14 +trainer/Advantage Score Mean -0.266427 +trainer/Advantage Score Std 0.468129 +trainer/Advantage Score Max 1.39737 +trainer/Advantage Score Min -3.14024 +trainer/V1 Predictions Mean -72.0159 +trainer/V1 Predictions Std 18.4889 +trainer/V1 Predictions Max -1.42539 +trainer/V1 Predictions Min -88.4021 +trainer/VF Loss 0.0505189 +expl/num steps total 616000 +expl/num paths total 797 +expl/path length Mean 500 +expl/path length Std 313 +expl/path length Max 813 +expl/path length Min 187 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0643549 +expl/Actions Std 0.816126 +expl/Actions Max 2.37249 +expl/Actions Min -2.17977 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 576609 +eval/num paths total 620 +eval/path length Mean 514 +eval/path length Std 0 +eval/path length Max 514 +eval/path length Min 514 +eval/Rewards Mean 0.00194553 +eval/Rewards Std 0.0440652 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0170094 +eval/Actions Std 0.741132 +eval/Actions Max 0.999752 +eval/Actions Min -0.999691 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.91062e-06 +time/evaluation sampling (s) 4.79917 +time/exploration sampling (s) 6.94022 +time/logging (s) 0.0101029 +time/saving (s) 0.0182474 +time/training (s) 19.3902 +time/epoch (s) 31.158 +time/total (s) 14741.2 +Epoch -385 +------------------------------ ---------------- +2022-05-15 22:08:41.487262 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -384 finished +------------------------------ ---------------- +epoch -384 +replay_buffer/size 999047 +trainer/num train calls 617000 +trainer/QF1 Loss 0.996093 +trainer/QF2 Loss 0.846817 +trainer/Policy Loss 8.69433 +trainer/Q1 Predictions Mean -70.8469 +trainer/Q1 Predictions Std 20.1526 +trainer/Q1 Predictions Max -1.06148 +trainer/Q1 Predictions Min -85.8721 +trainer/Q2 Predictions Mean -70.7353 +trainer/Q2 Predictions Std 20.1833 +trainer/Q2 Predictions Max -1.13858 +trainer/Q2 Predictions Min -85.9699 +trainer/Q Targets Mean -70.7184 +trainer/Q Targets Std 20.1916 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7449 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0113231 +trainer/policy/mean Std 0.727149 +trainer/policy/mean Max 0.99854 +trainer/policy/mean Min -0.998916 +trainer/policy/std Mean 0.414388 +trainer/policy/std Std 0.0202883 +trainer/policy/std Max 0.435227 +trainer/policy/std Min 0.384299 +trainer/Advantage Weights Mean 2.27999 +trainer/Advantage Weights Std 12.9731 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.81042e-21 +trainer/Advantage Score Mean -0.531563 +trainer/Advantage Score Std 0.647539 +trainer/Advantage Score Max 0.733528 +trainer/Advantage Score Min -4.62988 +trainer/V1 Predictions Mean -70.3766 +trainer/V1 Predictions Std 20.4952 +trainer/V1 Predictions Max -0.1352 +trainer/V1 Predictions Min -85.5229 +trainer/VF Loss 0.0771621 +expl/num steps total 617000 +expl/num paths total 798 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0448588 +expl/Actions Std 0.801303 +expl/Actions Max 2.38403 +expl/Actions Min -2.34489 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 577609 +eval/num paths total 621 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0478051 +eval/Actions Std 0.736583 +eval/Actions Max 0.999894 +eval/Actions Min -0.999911 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.19437e-05 +time/evaluation sampling (s) 5.46109 +time/exploration sampling (s) 6.77778 +time/logging (s) 0.0124898 +time/saving (s) 0.0228504 +time/training (s) 19.5677 +time/epoch (s) 31.8419 +time/total (s) 14773 +Epoch -384 +------------------------------ ---------------- +2022-05-15 22:09:14.177430 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -383 finished +------------------------------ ---------------- +epoch -383 +replay_buffer/size 999047 +trainer/num train calls 618000 +trainer/QF1 Loss 0.41326 +trainer/QF2 Loss 0.435076 +trainer/Policy Loss 9.81837 +trainer/Q1 Predictions Mean -72.5351 +trainer/Q1 Predictions Std 17.0852 +trainer/Q1 Predictions Max -0.0980748 +trainer/Q1 Predictions Min -86.0364 +trainer/Q2 Predictions Mean -72.562 +trainer/Q2 Predictions Std 17.0617 +trainer/Q2 Predictions Max -0.0469134 +trainer/Q2 Predictions Min -85.9246 +trainer/Q Targets Mean -72.5742 +trainer/Q Targets Std 17.3252 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3221 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0199521 +trainer/policy/mean Std 0.722972 +trainer/policy/mean Max 0.999592 +trainer/policy/mean Min -0.999511 +trainer/policy/std Mean 0.415206 +trainer/policy/std Std 0.0196397 +trainer/policy/std Max 0.435356 +trainer/policy/std Min 0.385863 +trainer/Advantage Weights Mean 3.05153 +trainer/Advantage Weights Std 13.5574 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.16816e-23 +trainer/Advantage Score Mean -0.432349 +trainer/Advantage Score Std 0.685427 +trainer/Advantage Score Max 1.1168 +trainer/Advantage Score Min -5.1532 +trainer/V1 Predictions Mean -72.3027 +trainer/V1 Predictions Std 17.3615 +trainer/V1 Predictions Max 0.64671 +trainer/V1 Predictions Min -86.1214 +trainer/VF Loss 0.0785721 +expl/num steps total 618000 +expl/num paths total 799 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0205802 +expl/Actions Std 0.834894 +expl/Actions Max 2.54221 +expl/Actions Min -2.16637 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 578609 +eval/num paths total 622 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0946929 +eval/Actions Std 0.731398 +eval/Actions Max 0.999841 +eval/Actions Min -0.999617 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.59189e-06 +time/evaluation sampling (s) 5.35811 +time/exploration sampling (s) 7.44503 +time/logging (s) 0.0126473 +time/saving (s) 0.018692 +time/training (s) 19.8389 +time/epoch (s) 32.6734 +time/total (s) 14805.7 +Epoch -383 +------------------------------ ---------------- +2022-05-15 22:09:46.911681 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -382 finished +------------------------------ ---------------- +epoch -382 +replay_buffer/size 999047 +trainer/num train calls 619000 +trainer/QF1 Loss 0.763782 +trainer/QF2 Loss 0.696119 +trainer/Policy Loss 17.532 +trainer/Q1 Predictions Mean -73.7814 +trainer/Q1 Predictions Std 15.9756 +trainer/Q1 Predictions Max -0.518459 +trainer/Q1 Predictions Min -87.4411 +trainer/Q2 Predictions Mean -73.6838 +trainer/Q2 Predictions Std 15.9383 +trainer/Q2 Predictions Max -0.0674498 +trainer/Q2 Predictions Min -87.1946 +trainer/Q Targets Mean -73.6964 +trainer/Q Targets Std 15.7442 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8606 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0263335 +trainer/policy/mean Std 0.71175 +trainer/policy/mean Max 0.999596 +trainer/policy/mean Min -0.997748 +trainer/policy/std Mean 0.413841 +trainer/policy/std Std 0.0209279 +trainer/policy/std Max 0.43384 +trainer/policy/std Min 0.383605 +trainer/Advantage Weights Mean 5.41169 +trainer/Advantage Weights Std 20.7432 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.57683e-12 +trainer/Advantage Score Mean -0.449791 +trainer/Advantage Score Std 0.584978 +trainer/Advantage Score Max 1.50553 +trainer/Advantage Score Min -2.63565 +trainer/V1 Predictions Mean -73.4763 +trainer/V1 Predictions Std 15.7179 +trainer/V1 Predictions Max 0.826449 +trainer/V1 Predictions Min -87.0174 +trainer/VF Loss 0.09703 +expl/num steps total 619000 +expl/num paths total 801 +expl/path length Mean 500 +expl/path length Std 60 +expl/path length Max 560 +expl/path length Min 440 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.010705 +expl/Actions Std 0.831218 +expl/Actions Max 2.18945 +expl/Actions Min -2.13548 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 579609 +eval/num paths total 623 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0270089 +eval/Actions Std 0.700804 +eval/Actions Max 0.999779 +eval/Actions Min -0.999398 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.15992e-05 +time/evaluation sampling (s) 5.6801 +time/exploration sampling (s) 6.94892 +time/logging (s) 0.0124273 +time/saving (s) 0.018247 +time/training (s) 20.0558 +time/epoch (s) 32.7154 +time/total (s) 14838.4 +Epoch -382 +------------------------------ ---------------- +2022-05-15 22:10:18.568494 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -381 finished +------------------------------ ---------------- +epoch -381 +replay_buffer/size 999047 +trainer/num train calls 620000 +trainer/QF1 Loss 0.722348 +trainer/QF2 Loss 0.847363 +trainer/Policy Loss 27.7055 +trainer/Q1 Predictions Mean -71.9165 +trainer/Q1 Predictions Std 18.6505 +trainer/Q1 Predictions Max -0.876987 +trainer/Q1 Predictions Min -87.0299 +trainer/Q2 Predictions Mean -71.9012 +trainer/Q2 Predictions Std 18.6661 +trainer/Q2 Predictions Max -0.632986 +trainer/Q2 Predictions Min -87.5369 +trainer/Q Targets Mean -71.9564 +trainer/Q Targets Std 18.5063 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8743 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0295424 +trainer/policy/mean Std 0.737793 +trainer/policy/mean Max 0.999926 +trainer/policy/mean Min -0.997704 +trainer/policy/std Mean 0.413349 +trainer/policy/std Std 0.0211194 +trainer/policy/std Max 0.434142 +trainer/policy/std Min 0.380087 +trainer/Advantage Weights Mean 6.88561 +trainer/Advantage Weights Std 22.3431 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.46674e-15 +trainer/Advantage Score Mean -0.298684 +trainer/Advantage Score Std 0.507732 +trainer/Advantage Score Max 1.1026 +trainer/Advantage Score Min -3.41557 +trainer/V1 Predictions Mean -71.7526 +trainer/V1 Predictions Std 18.7145 +trainer/V1 Predictions Max -0.985219 +trainer/V1 Predictions Min -87.2524 +trainer/VF Loss 0.0572766 +expl/num steps total 620000 +expl/num paths total 803 +expl/path length Mean 500 +expl/path length Std 338 +expl/path length Max 838 +expl/path length Min 162 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0194587 +expl/Actions Std 0.839673 +expl/Actions Max 2.31364 +expl/Actions Min -2.36162 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 580279 +eval/num paths total 624 +eval/path length Mean 670 +eval/path length Std 0 +eval/path length Max 670 +eval/path length Min 670 +eval/Rewards Mean 0.00149254 +eval/Rewards Std 0.0386045 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0516005 +eval/Actions Std 0.718153 +eval/Actions Max 0.999925 +eval/Actions Min -0.999395 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.36229e-05 +time/evaluation sampling (s) 5.39091 +time/exploration sampling (s) 6.98231 +time/logging (s) 0.0121557 +time/saving (s) 0.0203435 +time/training (s) 19.2325 +time/epoch (s) 31.6382 +time/total (s) 14870.1 +Epoch -381 +------------------------------ ---------------- +2022-05-15 22:10:51.219776 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -380 finished +------------------------------ ---------------- +epoch -380 +replay_buffer/size 999047 +trainer/num train calls 621000 +trainer/QF1 Loss 0.930498 +trainer/QF2 Loss 0.884312 +trainer/Policy Loss 10.038 +trainer/Q1 Predictions Mean -71.7939 +trainer/Q1 Predictions Std 17.4837 +trainer/Q1 Predictions Max -5.46952 +trainer/Q1 Predictions Min -88.5736 +trainer/Q2 Predictions Mean -71.8948 +trainer/Q2 Predictions Std 17.3904 +trainer/Q2 Predictions Max -7.55504 +trainer/Q2 Predictions Min -88.872 +trainer/Q Targets Mean -71.6315 +trainer/Q Targets Std 17.25 +trainer/Q Targets Max -6.25535 +trainer/Q Targets Min -88.1705 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0435801 +trainer/policy/mean Std 0.719923 +trainer/policy/mean Max 0.997413 +trainer/policy/mean Min -0.999273 +trainer/policy/std Mean 0.415867 +trainer/policy/std Std 0.0215096 +trainer/policy/std Max 0.437214 +trainer/policy/std Min 0.38212 +trainer/Advantage Weights Mean 4.31356 +trainer/Advantage Weights Std 17.7051 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.83857e-19 +trainer/Advantage Score Mean -0.540182 +trainer/Advantage Score Std 0.748955 +trainer/Advantage Score Max 2.29456 +trainer/Advantage Score Min -4.14628 +trainer/V1 Predictions Mean -71.3018 +trainer/V1 Predictions Std 17.5443 +trainer/V1 Predictions Max -3.40617 +trainer/V1 Predictions Min -88.3951 +trainer/VF Loss 0.115985 +expl/num steps total 621000 +expl/num paths total 805 +expl/path length Mean 500 +expl/path length Std 311 +expl/path length Max 811 +expl/path length Min 189 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0190821 +expl/Actions Std 0.825533 +expl/Actions Max 2.17264 +expl/Actions Min -2.21991 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 581279 +eval/num paths total 625 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0918701 +eval/Actions Std 0.617838 +eval/Actions Max 0.999737 +eval/Actions Min -0.999727 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.367e-05 +time/evaluation sampling (s) 5.72574 +time/exploration sampling (s) 7.01904 +time/logging (s) 0.0106337 +time/saving (s) 0.0172002 +time/training (s) 19.8575 +time/epoch (s) 32.6301 +time/total (s) 14902.7 +Epoch -380 +------------------------------ ---------------- +2022-05-15 22:11:23.712887 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -379 finished +------------------------------ ---------------- +epoch -379 +replay_buffer/size 999047 +trainer/num train calls 622000 +trainer/QF1 Loss 1.43557 +trainer/QF2 Loss 1.32868 +trainer/Policy Loss 12.3462 +trainer/Q1 Predictions Mean -68.6562 +trainer/Q1 Predictions Std 21.8194 +trainer/Q1 Predictions Max -0.426963 +trainer/Q1 Predictions Min -85.6974 +trainer/Q2 Predictions Mean -68.6782 +trainer/Q2 Predictions Std 21.8535 +trainer/Q2 Predictions Max -0.347055 +trainer/Q2 Predictions Min -85.7994 +trainer/Q Targets Mean -68.8153 +trainer/Q Targets Std 22.0565 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1427 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0256915 +trainer/policy/mean Std 0.733241 +trainer/policy/mean Max 0.998874 +trainer/policy/mean Min -0.998861 +trainer/policy/std Mean 0.417277 +trainer/policy/std Std 0.0217094 +trainer/policy/std Max 0.437833 +trainer/policy/std Min 0.381236 +trainer/Advantage Weights Mean 3.5157 +trainer/Advantage Weights Std 16.5468 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.09804e-24 +trainer/Advantage Score Mean -0.478208 +trainer/Advantage Score Std 0.673786 +trainer/Advantage Score Max 2.4779 +trainer/Advantage Score Min -5.36332 +trainer/V1 Predictions Mean -68.6707 +trainer/V1 Predictions Std 21.907 +trainer/V1 Predictions Max -0.127581 +trainer/V1 Predictions Min -85.9757 +trainer/VF Loss 0.10964 +expl/num steps total 622000 +expl/num paths total 806 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.201233 +expl/Actions Std 0.886476 +expl/Actions Max 2.62717 +expl/Actions Min -2.34833 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 581761 +eval/num paths total 626 +eval/path length Mean 482 +eval/path length Std 0 +eval/path length Max 482 +eval/path length Min 482 +eval/Rewards Mean 0.00207469 +eval/Rewards Std 0.0455015 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0106403 +eval/Actions Std 0.738052 +eval/Actions Max 0.999592 +eval/Actions Min -0.999361 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.20029e-05 +time/evaluation sampling (s) 5.47701 +time/exploration sampling (s) 6.89974 +time/logging (s) 0.00994038 +time/saving (s) 0.0178204 +time/training (s) 20.0747 +time/epoch (s) 32.4793 +time/total (s) 14935.2 +Epoch -379 +------------------------------ ---------------- +2022-05-15 22:11:54.856132 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -378 finished +------------------------------ --------------- +epoch -378 +replay_buffer/size 999047 +trainer/num train calls 623000 +trainer/QF1 Loss 1.32254 +trainer/QF2 Loss 1.40944 +trainer/Policy Loss 31.5187 +trainer/Q1 Predictions Mean -71.8354 +trainer/Q1 Predictions Std 17.4547 +trainer/Q1 Predictions Max -2.20302 +trainer/Q1 Predictions Min -85.0475 +trainer/Q2 Predictions Mean -71.6857 +trainer/Q2 Predictions Std 17.4314 +trainer/Q2 Predictions Max -2.14931 +trainer/Q2 Predictions Min -85.2032 +trainer/Q Targets Mean -72.2084 +trainer/Q Targets Std 17.6149 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.6379 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0237012 +trainer/policy/mean Std 0.723399 +trainer/policy/mean Max 0.997994 +trainer/policy/mean Min -0.99817 +trainer/policy/std Mean 0.417313 +trainer/policy/std Std 0.0214618 +trainer/policy/std Max 0.439095 +trainer/policy/std Min 0.382528 +trainer/Advantage Weights Mean 7.5112 +trainer/Advantage Weights Std 22.1796 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.2685e-25 +trainer/Advantage Score Mean -0.428234 +trainer/Advantage Score Std 0.841319 +trainer/Advantage Score Max 1.45171 +trainer/Advantage Score Min -5.61134 +trainer/V1 Predictions Mean -71.9178 +trainer/V1 Predictions Std 17.8806 +trainer/V1 Predictions Max -1.62845 +trainer/V1 Predictions Min -85.5256 +trainer/VF Loss 0.117625 +expl/num steps total 623000 +expl/num paths total 808 +expl/path length Mean 500 +expl/path length Std 206 +expl/path length Max 706 +expl/path length Min 294 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00768303 +expl/Actions Std 0.837598 +expl/Actions Max 2.39989 +expl/Actions Min -2.51255 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 582488 +eval/num paths total 627 +eval/path length Mean 727 +eval/path length Std 0 +eval/path length Max 727 +eval/path length Min 727 +eval/Rewards Mean 0.00137552 +eval/Rewards Std 0.0370624 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0452458 +eval/Actions Std 0.730587 +eval/Actions Max 0.999819 +eval/Actions Min -0.999653 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.1873e-05 +time/evaluation sampling (s) 5.40641 +time/exploration sampling (s) 6.44464 +time/logging (s) 0.00891137 +time/saving (s) 0.0136734 +time/training (s) 19.2502 +time/epoch (s) 31.1238 +time/total (s) 14966.3 +Epoch -378 +------------------------------ --------------- +2022-05-15 22:12:25.880459 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -377 finished +------------------------------ ---------------- +epoch -377 +replay_buffer/size 999047 +trainer/num train calls 624000 +trainer/QF1 Loss 1.36159 +trainer/QF2 Loss 1.4493 +trainer/Policy Loss 20.0597 +trainer/Q1 Predictions Mean -71.0719 +trainer/Q1 Predictions Std 19.8196 +trainer/Q1 Predictions Max -1.47205 +trainer/Q1 Predictions Min -86.0601 +trainer/Q2 Predictions Mean -71.0173 +trainer/Q2 Predictions Std 19.7954 +trainer/Q2 Predictions Max -1.7187 +trainer/Q2 Predictions Min -85.89 +trainer/Q Targets Mean -71.3331 +trainer/Q Targets Std 19.8427 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9752 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0123982 +trainer/policy/mean Std 0.726945 +trainer/policy/mean Max 0.99983 +trainer/policy/mean Min -0.997916 +trainer/policy/std Mean 0.413942 +trainer/policy/std Std 0.0210114 +trainer/policy/std Max 0.437598 +trainer/policy/std Min 0.379148 +trainer/Advantage Weights Mean 4.41424 +trainer/Advantage Weights Std 16.0106 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.90095e-19 +trainer/Advantage Score Mean -0.399568 +trainer/Advantage Score Std 0.718915 +trainer/Advantage Score Max 0.989746 +trainer/Advantage Score Min -4.26841 +trainer/V1 Predictions Mean -70.9938 +trainer/V1 Predictions Std 20.077 +trainer/V1 Predictions Max 0.530237 +trainer/V1 Predictions Min -85.7701 +trainer/VF Loss 0.0836501 +expl/num steps total 624000 +expl/num paths total 809 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0867455 +expl/Actions Std 0.81508 +expl/Actions Max 2.51738 +expl/Actions Min -2.41866 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 583488 +eval/num paths total 628 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0273065 +eval/Actions Std 0.722592 +eval/Actions Max 0.999795 +eval/Actions Min -0.999816 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.37209e-06 +time/evaluation sampling (s) 4.88171 +time/exploration sampling (s) 6.88733 +time/logging (s) 0.0112026 +time/saving (s) 0.0177233 +time/training (s) 19.2105 +time/epoch (s) 31.0085 +time/total (s) 14997.3 +Epoch -377 +------------------------------ ---------------- +2022-05-15 22:12:58.104681 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -376 finished +------------------------------ ---------------- +epoch -376 +replay_buffer/size 999047 +trainer/num train calls 625000 +trainer/QF1 Loss 0.624718 +trainer/QF2 Loss 0.606689 +trainer/Policy Loss 10.1757 +trainer/Q1 Predictions Mean -72.9955 +trainer/Q1 Predictions Std 18.1449 +trainer/Q1 Predictions Max -0.53345 +trainer/Q1 Predictions Min -87.1427 +trainer/Q2 Predictions Mean -73.0097 +trainer/Q2 Predictions Std 18.1714 +trainer/Q2 Predictions Max -1.37903 +trainer/Q2 Predictions Min -87.3565 +trainer/Q Targets Mean -72.6874 +trainer/Q Targets Std 18.066 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4219 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00756593 +trainer/policy/mean Std 0.717685 +trainer/policy/mean Max 0.999152 +trainer/policy/mean Min -0.997283 +trainer/policy/std Mean 0.416853 +trainer/policy/std Std 0.0204667 +trainer/policy/std Max 0.439751 +trainer/policy/std Min 0.382013 +trainer/Advantage Weights Mean 2.71787 +trainer/Advantage Weights Std 14.2554 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.1576e-27 +trainer/Advantage Score Mean -0.536972 +trainer/Advantage Score Std 0.687334 +trainer/Advantage Score Max 1.58632 +trainer/Advantage Score Min -6.20235 +trainer/V1 Predictions Mean -72.4214 +trainer/V1 Predictions Std 18.3214 +trainer/V1 Predictions Max -0.848152 +trainer/V1 Predictions Min -86.4251 +trainer/VF Loss 0.0897853 +expl/num steps total 625000 +expl/num paths total 810 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0808337 +expl/Actions Std 0.891381 +expl/Actions Max 2.13576 +expl/Actions Min -2.28171 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 584488 +eval/num paths total 629 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00685858 +eval/Actions Std 0.788691 +eval/Actions Max 0.999781 +eval/Actions Min -0.99963 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.19489e-05 +time/evaluation sampling (s) 5.36938 +time/exploration sampling (s) 6.68071 +time/logging (s) 0.0118703 +time/saving (s) 0.0177965 +time/training (s) 20.1303 +time/epoch (s) 32.2101 +time/total (s) 15029.5 +Epoch -376 +------------------------------ ---------------- +2022-05-15 22:13:30.920320 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -375 finished +------------------------------ ---------------- +epoch -375 +replay_buffer/size 999047 +trainer/num train calls 626000 +trainer/QF1 Loss 0.773338 +trainer/QF2 Loss 0.943388 +trainer/Policy Loss 23.7423 +trainer/Q1 Predictions Mean -72.1696 +trainer/Q1 Predictions Std 18.8217 +trainer/Q1 Predictions Max -0.92356 +trainer/Q1 Predictions Min -86.1881 +trainer/Q2 Predictions Mean -72.0426 +trainer/Q2 Predictions Std 18.7838 +trainer/Q2 Predictions Max -0.272414 +trainer/Q2 Predictions Min -85.9852 +trainer/Q Targets Mean -72.4731 +trainer/Q Targets Std 18.5761 +trainer/Q Targets Max -1.26997 +trainer/Q Targets Min -86.6478 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000826747 +trainer/policy/mean Std 0.723608 +trainer/policy/mean Max 0.999229 +trainer/policy/mean Min -0.998403 +trainer/policy/std Mean 0.416979 +trainer/policy/std Std 0.0212973 +trainer/policy/std Max 0.44053 +trainer/policy/std Min 0.38136 +trainer/Advantage Weights Mean 5.48789 +trainer/Advantage Weights Std 19.669 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.57895e-23 +trainer/Advantage Score Mean -0.288705 +trainer/Advantage Score Std 0.611515 +trainer/Advantage Score Max 2.35367 +trainer/Advantage Score Min -5.1438 +trainer/V1 Predictions Mean -72.1387 +trainer/V1 Predictions Std 18.815 +trainer/V1 Predictions Max -0.254209 +trainer/V1 Predictions Min -86.403 +trainer/VF Loss 0.0839742 +expl/num steps total 626000 +expl/num paths total 811 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0178028 +expl/Actions Std 0.868749 +expl/Actions Max 2.78553 +expl/Actions Min -2.13901 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 585449 +eval/num paths total 630 +eval/path length Mean 961 +eval/path length Std 0 +eval/path length Max 961 +eval/path length Min 961 +eval/Rewards Mean 0.00104058 +eval/Rewards Std 0.0322413 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0493016 +eval/Actions Std 0.741517 +eval/Actions Max 0.99978 +eval/Actions Min -0.999705 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.19591e-05 +time/evaluation sampling (s) 5.88062 +time/exploration sampling (s) 7.0291 +time/logging (s) 0.0122121 +time/saving (s) 0.018274 +time/training (s) 19.858 +time/epoch (s) 32.7982 +time/total (s) 15062.4 +Epoch -375 +------------------------------ ---------------- +2022-05-15 22:14:03.073739 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -374 finished +------------------------------ ---------------- +epoch -374 +replay_buffer/size 999047 +trainer/num train calls 627000 +trainer/QF1 Loss 0.849842 +trainer/QF2 Loss 0.761924 +trainer/Policy Loss 21.7863 +trainer/Q1 Predictions Mean -70.9018 +trainer/Q1 Predictions Std 20.802 +trainer/Q1 Predictions Max 0.627588 +trainer/Q1 Predictions Min -85.9948 +trainer/Q2 Predictions Mean -70.9383 +trainer/Q2 Predictions Std 20.7515 +trainer/Q2 Predictions Max -0.40661 +trainer/Q2 Predictions Min -86.1447 +trainer/Q Targets Mean -70.9179 +trainer/Q Targets Std 20.7584 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0834 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0181995 +trainer/policy/mean Std 0.713545 +trainer/policy/mean Max 0.999664 +trainer/policy/mean Min -0.999267 +trainer/policy/std Mean 0.415273 +trainer/policy/std Std 0.0202649 +trainer/policy/std Max 0.435902 +trainer/policy/std Min 0.38211 +trainer/Advantage Weights Mean 4.68694 +trainer/Advantage Weights Std 17.5679 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.06148e-20 +trainer/Advantage Score Mean -0.417215 +trainer/Advantage Score Std 0.70992 +trainer/Advantage Score Max 2.41658 +trainer/Advantage Score Min -4.46502 +trainer/V1 Predictions Mean -70.6879 +trainer/V1 Predictions Std 20.9269 +trainer/V1 Predictions Max 1.15952 +trainer/V1 Predictions Min -85.6902 +trainer/VF Loss 0.0987296 +expl/num steps total 627000 +expl/num paths total 812 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0975832 +expl/Actions Std 0.80942 +expl/Actions Max 2.23106 +expl/Actions Min -2.48896 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 585948 +eval/num paths total 631 +eval/path length Mean 499 +eval/path length Std 0 +eval/path length Max 499 +eval/path length Min 499 +eval/Rewards Mean 0.00200401 +eval/Rewards Std 0.0447213 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0234748 +eval/Actions Std 0.741967 +eval/Actions Max 0.999737 +eval/Actions Min -0.999672 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0232e-05 +time/evaluation sampling (s) 5.14284 +time/exploration sampling (s) 7.50511 +time/logging (s) 0.0104256 +time/saving (s) 0.0190914 +time/training (s) 19.4555 +time/epoch (s) 32.133 +time/total (s) 15094.5 +Epoch -374 +------------------------------ ---------------- +2022-05-15 22:14:34.703730 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -373 finished +------------------------------ ---------------- +epoch -373 +replay_buffer/size 999047 +trainer/num train calls 628000 +trainer/QF1 Loss 0.864333 +trainer/QF2 Loss 0.926833 +trainer/Policy Loss 23.1437 +trainer/Q1 Predictions Mean -72.2814 +trainer/Q1 Predictions Std 18.5683 +trainer/Q1 Predictions Max 1.74704 +trainer/Q1 Predictions Min -86.7827 +trainer/Q2 Predictions Mean -72.2403 +trainer/Q2 Predictions Std 18.4016 +trainer/Q2 Predictions Max -0.379423 +trainer/Q2 Predictions Min -86.4455 +trainer/Q Targets Mean -72.0006 +trainer/Q Targets Std 18.5979 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3149 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0114695 +trainer/policy/mean Std 0.728717 +trainer/policy/mean Max 0.998663 +trainer/policy/mean Min -0.999381 +trainer/policy/std Mean 0.414578 +trainer/policy/std Std 0.0204324 +trainer/policy/std Max 0.439158 +trainer/policy/std Min 0.3821 +trainer/Advantage Weights Mean 5.45692 +trainer/Advantage Weights Std 20.0254 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.03678e-19 +trainer/Advantage Score Mean -0.466949 +trainer/Advantage Score Std 0.804198 +trainer/Advantage Score Max 1.9582 +trainer/Advantage Score Min -4.3713 +trainer/V1 Predictions Mean -71.6344 +trainer/V1 Predictions Std 18.8304 +trainer/V1 Predictions Max 3.19562 +trainer/V1 Predictions Min -86.4315 +trainer/VF Loss 0.118483 +expl/num steps total 628000 +expl/num paths total 813 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0379124 +expl/Actions Std 0.881375 +expl/Actions Max 2.57658 +expl/Actions Min -2.34329 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 586551 +eval/num paths total 632 +eval/path length Mean 603 +eval/path length Std 0 +eval/path length Max 603 +eval/path length Min 603 +eval/Rewards Mean 0.00165837 +eval/Rewards Std 0.0406894 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0409154 +eval/Actions Std 0.726688 +eval/Actions Max 0.99987 +eval/Actions Min -0.999623 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.09596e-06 +time/evaluation sampling (s) 5.17795 +time/exploration sampling (s) 6.92133 +time/logging (s) 0.00719423 +time/saving (s) 0.0163054 +time/training (s) 19.4858 +time/epoch (s) 31.6086 +time/total (s) 15126.1 +Epoch -373 +------------------------------ ---------------- +2022-05-15 22:15:07.412165 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -372 finished +------------------------------ ---------------- +epoch -372 +replay_buffer/size 999047 +trainer/num train calls 629000 +trainer/QF1 Loss 0.508604 +trainer/QF2 Loss 0.593402 +trainer/Policy Loss 16.0115 +trainer/Q1 Predictions Mean -73.4927 +trainer/Q1 Predictions Std 16.1968 +trainer/Q1 Predictions Max -1.49625 +trainer/Q1 Predictions Min -88.0935 +trainer/Q2 Predictions Mean -73.5354 +trainer/Q2 Predictions Std 16.1458 +trainer/Q2 Predictions Max -2.69389 +trainer/Q2 Predictions Min -87.8778 +trainer/Q Targets Mean -73.3717 +trainer/Q Targets Std 16.1983 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9851 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0170477 +trainer/policy/mean Std 0.717702 +trainer/policy/mean Max 0.998987 +trainer/policy/mean Min -0.999318 +trainer/policy/std Mean 0.415379 +trainer/policy/std Std 0.0219513 +trainer/policy/std Max 0.441475 +trainer/policy/std Min 0.379827 +trainer/Advantage Weights Mean 2.83513 +trainer/Advantage Weights Std 15.1732 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.15465e-14 +trainer/Advantage Score Mean -0.62014 +trainer/Advantage Score Std 0.529826 +trainer/Advantage Score Max 1.19585 +trainer/Advantage Score Min -3.0419 +trainer/V1 Predictions Mean -73.1536 +trainer/V1 Predictions Std 16.2787 +trainer/V1 Predictions Max -3.33784 +trainer/V1 Predictions Min -87.6236 +trainer/VF Loss 0.0806351 +expl/num steps total 629000 +expl/num paths total 814 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0304387 +expl/Actions Std 0.840923 +expl/Actions Max 2.17134 +expl/Actions Min -2.2114 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 587551 +eval/num paths total 633 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0562129 +eval/Actions Std 0.737549 +eval/Actions Max 0.999889 +eval/Actions Min -0.999764 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.12909e-05 +time/evaluation sampling (s) 5.7719 +time/exploration sampling (s) 6.95733 +time/logging (s) 0.0074809 +time/saving (s) 0.0171006 +time/training (s) 19.9441 +time/epoch (s) 32.698 +time/total (s) 15158.8 +Epoch -372 +------------------------------ ---------------- +2022-05-15 22:15:39.907336 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -371 finished +------------------------------ ---------------- +epoch -371 +replay_buffer/size 999047 +trainer/num train calls 630000 +trainer/QF1 Loss 1.43188 +trainer/QF2 Loss 1.47643 +trainer/Policy Loss 72.9218 +trainer/Q1 Predictions Mean -70.1121 +trainer/Q1 Predictions Std 18.8338 +trainer/Q1 Predictions Max -0.849808 +trainer/Q1 Predictions Min -87.5385 +trainer/Q2 Predictions Mean -69.9839 +trainer/Q2 Predictions Std 18.8254 +trainer/Q2 Predictions Max 0.370732 +trainer/Q2 Predictions Min -87.5646 +trainer/Q Targets Mean -70.638 +trainer/Q Targets Std 19.2431 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4321 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00765919 +trainer/policy/mean Std 0.710899 +trainer/policy/mean Max 0.999837 +trainer/policy/mean Min -0.999185 +trainer/policy/std Mean 0.414123 +trainer/policy/std Std 0.0210248 +trainer/policy/std Max 0.437596 +trainer/policy/std Min 0.379986 +trainer/Advantage Weights Mean 15.9827 +trainer/Advantage Weights Std 30.5678 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.03205e-17 +trainer/Advantage Score Mean -0.182567 +trainer/Advantage Score Std 0.787842 +trainer/Advantage Score Max 1.49741 +trainer/Advantage Score Min -3.77497 +trainer/V1 Predictions Mean -70.4472 +trainer/V1 Predictions Std 19.1724 +trainer/V1 Predictions Max -0.491741 +trainer/V1 Predictions Min -88.3882 +trainer/VF Loss 0.138713 +expl/num steps total 630000 +expl/num paths total 815 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0528538 +expl/Actions Std 0.81512 +expl/Actions Max 2.50486 +expl/Actions Min -2.31452 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 588551 +eval/num paths total 634 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0953585 +eval/Actions Std 0.670421 +eval/Actions Max 0.999879 +eval/Actions Min -0.999684 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.23402e-06 +time/evaluation sampling (s) 5.14327 +time/exploration sampling (s) 7.12605 +time/logging (s) 0.00929214 +time/saving (s) 0.014013 +time/training (s) 20.1925 +time/epoch (s) 32.4852 +time/total (s) 15191.3 +Epoch -371 +------------------------------ ---------------- +2022-05-15 22:16:10.804064 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -370 finished +------------------------------ ---------------- +epoch -370 +replay_buffer/size 999047 +trainer/num train calls 631000 +trainer/QF1 Loss 0.617572 +trainer/QF2 Loss 0.610057 +trainer/Policy Loss 15.5487 +trainer/Q1 Predictions Mean -70.9136 +trainer/Q1 Predictions Std 19.4822 +trainer/Q1 Predictions Max -0.320462 +trainer/Q1 Predictions Min -87.8446 +trainer/Q2 Predictions Mean -70.9004 +trainer/Q2 Predictions Std 19.4309 +trainer/Q2 Predictions Max -0.642987 +trainer/Q2 Predictions Min -87.809 +trainer/Q Targets Mean -71.0466 +trainer/Q Targets Std 19.463 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.7814 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0100453 +trainer/policy/mean Std 0.733196 +trainer/policy/mean Max 0.997985 +trainer/policy/mean Min -0.998142 +trainer/policy/std Mean 0.416708 +trainer/policy/std Std 0.0194437 +trainer/policy/std Max 0.442019 +trainer/policy/std Min 0.382483 +trainer/Advantage Weights Mean 3.11706 +trainer/Advantage Weights Std 14.1951 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.63174e-11 +trainer/Advantage Score Mean -0.378545 +trainer/Advantage Score Std 0.51071 +trainer/Advantage Score Max 0.835479 +trainer/Advantage Score Min -2.48388 +trainer/V1 Predictions Mean -70.7544 +trainer/V1 Predictions Std 19.5951 +trainer/V1 Predictions Max -0.235776 +trainer/V1 Predictions Min -88.656 +trainer/VF Loss 0.049043 +expl/num steps total 631000 +expl/num paths total 817 +expl/path length Mean 500 +expl/path length Std 350 +expl/path length Max 850 +expl/path length Min 150 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.025753 +expl/Actions Std 0.81297 +expl/Actions Max 2.27614 +expl/Actions Min -2.50775 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 589265 +eval/num paths total 635 +eval/path length Mean 714 +eval/path length Std 0 +eval/path length Max 714 +eval/path length Min 714 +eval/Rewards Mean 0.00140056 +eval/Rewards Std 0.0373978 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0407754 +eval/Actions Std 0.731095 +eval/Actions Max 0.999873 +eval/Actions Min -0.999901 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.23489e-05 +time/evaluation sampling (s) 5.58592 +time/exploration sampling (s) 5.83629 +time/logging (s) 0.00957461 +time/saving (s) 0.0179552 +time/training (s) 19.4355 +time/epoch (s) 30.8853 +time/total (s) 15222.2 +Epoch -370 +------------------------------ ---------------- +2022-05-15 22:16:41.767779 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -369 finished +------------------------------ ---------------- +epoch -369 +replay_buffer/size 999047 +trainer/num train calls 632000 +trainer/QF1 Loss 0.567349 +trainer/QF2 Loss 0.459824 +trainer/Policy Loss 44.7125 +trainer/Q1 Predictions Mean -71.4363 +trainer/Q1 Predictions Std 19.3037 +trainer/Q1 Predictions Max -0.276199 +trainer/Q1 Predictions Min -86.9334 +trainer/Q2 Predictions Mean -71.5206 +trainer/Q2 Predictions Std 19.2043 +trainer/Q2 Predictions Max -0.246887 +trainer/Q2 Predictions Min -86.6156 +trainer/Q Targets Mean -71.6856 +trainer/Q Targets Std 19.2355 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7167 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00730568 +trainer/policy/mean Std 0.719512 +trainer/policy/mean Max 0.999349 +trainer/policy/mean Min -0.999519 +trainer/policy/std Mean 0.416344 +trainer/policy/std Std 0.018884 +trainer/policy/std Max 0.437676 +trainer/policy/std Min 0.383973 +trainer/Advantage Weights Mean 8.01788 +trainer/Advantage Weights Std 24.1283 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.28049e-17 +trainer/Advantage Score Mean -0.322825 +trainer/Advantage Score Std 0.67801 +trainer/Advantage Score Max 2.6634 +trainer/Advantage Score Min -3.83196 +trainer/V1 Predictions Mean -71.3598 +trainer/V1 Predictions Std 19.3278 +trainer/V1 Predictions Max -0.471551 +trainer/V1 Predictions Min -86.5832 +trainer/VF Loss 0.119892 +expl/num steps total 632000 +expl/num paths total 819 +expl/path length Mean 500 +expl/path length Std 240 +expl/path length Max 740 +expl/path length Min 260 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00820794 +expl/Actions Std 0.83404 +expl/Actions Max 2.40244 +expl/Actions Min -2.18234 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 589951 +eval/num paths total 636 +eval/path length Mean 686 +eval/path length Std 0 +eval/path length Max 686 +eval/path length Min 686 +eval/Rewards Mean 0.00145773 +eval/Rewards Std 0.0381523 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0440654 +eval/Actions Std 0.731271 +eval/Actions Max 0.999971 +eval/Actions Min -0.999735 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.91226e-06 +time/evaluation sampling (s) 5.01147 +time/exploration sampling (s) 6.29586 +time/logging (s) 0.0103496 +time/saving (s) 0.0173087 +time/training (s) 19.6124 +time/epoch (s) 30.9473 +time/total (s) 15253.1 +Epoch -369 +------------------------------ ---------------- +2022-05-15 22:17:13.397699 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -368 finished +------------------------------ ---------------- +epoch -368 +replay_buffer/size 999047 +trainer/num train calls 633000 +trainer/QF1 Loss 0.766345 +trainer/QF2 Loss 0.649434 +trainer/Policy Loss 31.0315 +trainer/Q1 Predictions Mean -70.3245 +trainer/Q1 Predictions Std 20.7714 +trainer/Q1 Predictions Max -0.816449 +trainer/Q1 Predictions Min -86.6682 +trainer/Q2 Predictions Mean -70.2639 +trainer/Q2 Predictions Std 20.8035 +trainer/Q2 Predictions Max -0.388574 +trainer/Q2 Predictions Min -86.8782 +trainer/Q Targets Mean -70.2996 +trainer/Q Targets Std 20.871 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3881 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0161484 +trainer/policy/mean Std 0.730841 +trainer/policy/mean Max 0.999853 +trainer/policy/mean Min -0.998109 +trainer/policy/std Mean 0.415537 +trainer/policy/std Std 0.0194764 +trainer/policy/std Max 0.434956 +trainer/policy/std Min 0.38219 +trainer/Advantage Weights Mean 6.13176 +trainer/Advantage Weights Std 21.0234 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.10791e-23 +trainer/Advantage Score Mean -0.438477 +trainer/Advantage Score Std 0.718985 +trainer/Advantage Score Max 2.30212 +trainer/Advantage Score Min -5.2857 +trainer/V1 Predictions Mean -69.9815 +trainer/V1 Predictions Std 21.1145 +trainer/V1 Predictions Max 0.421865 +trainer/V1 Predictions Min -86.2589 +trainer/VF Loss 0.114956 +expl/num steps total 633000 +expl/num paths total 820 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.295097 +expl/Actions Std 0.779714 +expl/Actions Max 2.08491 +expl/Actions Min -2.46385 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 590951 +eval/num paths total 637 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.021514 +eval/Actions Std 0.746556 +eval/Actions Max 0.99987 +eval/Actions Min -0.999752 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02497e-05 +time/evaluation sampling (s) 5.19932 +time/exploration sampling (s) 6.53535 +time/logging (s) 0.0126145 +time/saving (s) 0.0151667 +time/training (s) 19.8556 +time/epoch (s) 31.6181 +time/total (s) 15284.8 +Epoch -368 +------------------------------ ---------------- +2022-05-15 22:17:45.498318 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -367 finished +------------------------------ ---------------- +epoch -367 +replay_buffer/size 999047 +trainer/num train calls 634000 +trainer/QF1 Loss 1.12176 +trainer/QF2 Loss 1.13281 +trainer/Policy Loss 20.5547 +trainer/Q1 Predictions Mean -70.4791 +trainer/Q1 Predictions Std 19.515 +trainer/Q1 Predictions Max -0.609545 +trainer/Q1 Predictions Min -86.9991 +trainer/Q2 Predictions Mean -70.5374 +trainer/Q2 Predictions Std 19.509 +trainer/Q2 Predictions Max -0.335185 +trainer/Q2 Predictions Min -87.2321 +trainer/Q Targets Mean -70.8637 +trainer/Q Targets Std 19.7414 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.0332 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00448651 +trainer/policy/mean Std 0.712986 +trainer/policy/mean Max 0.999058 +trainer/policy/mean Min -0.998757 +trainer/policy/std Mean 0.415154 +trainer/policy/std Std 0.0189963 +trainer/policy/std Max 0.433628 +trainer/policy/std Min 0.384277 +trainer/Advantage Weights Mean 4.2574 +trainer/Advantage Weights Std 17.671 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.48763e-18 +trainer/Advantage Score Mean -0.445337 +trainer/Advantage Score Std 0.732004 +trainer/Advantage Score Max 2.66677 +trainer/Advantage Score Min -4.10493 +trainer/V1 Predictions Mean -70.5307 +trainer/V1 Predictions Std 19.8891 +trainer/V1 Predictions Max -0.15408 +trainer/V1 Predictions Min -87.9024 +trainer/VF Loss 0.106284 +expl/num steps total 634000 +expl/num paths total 821 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0611478 +expl/Actions Std 0.826895 +expl/Actions Max 2.28687 +expl/Actions Min -2.27713 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 591951 +eval/num paths total 638 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0502352 +eval/Actions Std 0.765445 +eval/Actions Max 0.999965 +eval/Actions Min -0.999648 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.28178e-05 +time/evaluation sampling (s) 5.60294 +time/exploration sampling (s) 6.83992 +time/logging (s) 0.0125655 +time/saving (s) 0.0183525 +time/training (s) 19.6122 +time/epoch (s) 32.086 +time/total (s) 15316.9 +Epoch -367 +------------------------------ ---------------- +2022-05-15 22:18:18.340792 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -366 finished +------------------------------ ---------------- +epoch -366 +replay_buffer/size 999047 +trainer/num train calls 635000 +trainer/QF1 Loss 0.868221 +trainer/QF2 Loss 0.73153 +trainer/Policy Loss 24.3379 +trainer/Q1 Predictions Mean -71.562 +trainer/Q1 Predictions Std 20.06 +trainer/Q1 Predictions Max -0.835961 +trainer/Q1 Predictions Min -88.9112 +trainer/Q2 Predictions Mean -71.6428 +trainer/Q2 Predictions Std 19.9531 +trainer/Q2 Predictions Max -1.33517 +trainer/Q2 Predictions Min -88.684 +trainer/Q Targets Mean -71.4147 +trainer/Q Targets Std 20.271 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.9676 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00964812 +trainer/policy/mean Std 0.716652 +trainer/policy/mean Max 0.999802 +trainer/policy/mean Min -0.997617 +trainer/policy/std Mean 0.414786 +trainer/policy/std Std 0.0198878 +trainer/policy/std Max 0.435728 +trainer/policy/std Min 0.38506 +trainer/Advantage Weights Mean 5.00295 +trainer/Advantage Weights Std 19.205 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.00136e-24 +trainer/Advantage Score Mean -0.444208 +trainer/Advantage Score Std 0.736295 +trainer/Advantage Score Max 2.04813 +trainer/Advantage Score Min -5.31824 +trainer/V1 Predictions Mean -71.1172 +trainer/V1 Predictions Std 20.4061 +trainer/V1 Predictions Max 0.128511 +trainer/V1 Predictions Min -88.7794 +trainer/VF Loss 0.105394 +expl/num steps total 635000 +expl/num paths total 823 +expl/path length Mean 500 +expl/path length Std 231 +expl/path length Max 731 +expl/path length Min 269 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.014813 +expl/Actions Std 0.828897 +expl/Actions Max 2.25436 +expl/Actions Min -2.19188 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 592487 +eval/num paths total 639 +eval/path length Mean 536 +eval/path length Std 0 +eval/path length Max 536 +eval/path length Min 536 +eval/Rewards Mean 0.00186567 +eval/Rewards Std 0.0431531 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.031226 +eval/Actions Std 0.744318 +eval/Actions Max 0.999551 +eval/Actions Min -0.999698 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.06739e-05 +time/evaluation sampling (s) 5.75529 +time/exploration sampling (s) 7.40745 +time/logging (s) 0.0140372 +time/saving (s) 0.0156868 +time/training (s) 19.633 +time/epoch (s) 32.8255 +time/total (s) 15349.7 +Epoch -366 +------------------------------ ---------------- +2022-05-15 22:18:50.113836 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -365 finished +------------------------------ ---------------- +epoch -365 +replay_buffer/size 999047 +trainer/num train calls 636000 +trainer/QF1 Loss 5.00983 +trainer/QF2 Loss 5.0634 +trainer/Policy Loss 25.4129 +trainer/Q1 Predictions Mean -71.7753 +trainer/Q1 Predictions Std 18.1782 +trainer/Q1 Predictions Max -0.840655 +trainer/Q1 Predictions Min -87.42 +trainer/Q2 Predictions Mean -71.8104 +trainer/Q2 Predictions Std 18.2337 +trainer/Q2 Predictions Max -1.05782 +trainer/Q2 Predictions Min -87.1933 +trainer/Q Targets Mean -71.594 +trainer/Q Targets Std 18.453 +trainer/Q Targets Max -0.98918 +trainer/Q Targets Min -86.3724 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00966059 +trainer/policy/mean Std 0.725136 +trainer/policy/mean Max 0.999166 +trainer/policy/mean Min -0.999405 +trainer/policy/std Mean 0.416827 +trainer/policy/std Std 0.0204578 +trainer/policy/std Max 0.438971 +trainer/policy/std Min 0.381289 +trainer/Advantage Weights Mean 5.96356 +trainer/Advantage Weights Std 19.9392 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.14031e-19 +trainer/Advantage Score Mean -0.370939 +trainer/Advantage Score Std 0.722344 +trainer/Advantage Score Max 0.891232 +trainer/Advantage Score Min -4.29882 +trainer/V1 Predictions Mean -71.442 +trainer/V1 Predictions Std 18.4119 +trainer/V1 Predictions Max -0.876959 +trainer/V1 Predictions Min -86.7164 +trainer/VF Loss 0.0843109 +expl/num steps total 636000 +expl/num paths total 824 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.163407 +expl/Actions Std 0.815715 +expl/Actions Max 2.15112 +expl/Actions Min -2.42122 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 593487 +eval/num paths total 640 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0262285 +eval/Actions Std 0.714818 +eval/Actions Max 0.999894 +eval/Actions Min -0.999909 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0204e-05 +time/evaluation sampling (s) 5.285 +time/exploration sampling (s) 7.14409 +time/logging (s) 0.0115813 +time/saving (s) 0.0140321 +time/training (s) 19.2979 +time/epoch (s) 31.7526 +time/total (s) 15381.5 +Epoch -365 +------------------------------ ---------------- +2022-05-15 22:19:21.707159 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -364 finished +------------------------------ ---------------- +epoch -364 +replay_buffer/size 999047 +trainer/num train calls 637000 +trainer/QF1 Loss 9.80574 +trainer/QF2 Loss 9.83878 +trainer/Policy Loss 34.8307 +trainer/Q1 Predictions Mean -71.059 +trainer/Q1 Predictions Std 17.8215 +trainer/Q1 Predictions Max -2.93493 +trainer/Q1 Predictions Min -87.1444 +trainer/Q2 Predictions Mean -71.1306 +trainer/Q2 Predictions Std 17.7871 +trainer/Q2 Predictions Max -2.98782 +trainer/Q2 Predictions Min -87.2723 +trainer/Q Targets Mean -71.0504 +trainer/Q Targets Std 18.1183 +trainer/Q Targets Max -5.25784 +trainer/Q Targets Min -87.5087 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00739396 +trainer/policy/mean Std 0.724468 +trainer/policy/mean Max 0.999272 +trainer/policy/mean Min -0.999383 +trainer/policy/std Mean 0.417647 +trainer/policy/std Std 0.0215828 +trainer/policy/std Max 0.440285 +trainer/policy/std Min 0.381101 +trainer/Advantage Weights Mean 7.03141 +trainer/Advantage Weights Std 21.4145 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.1045e-17 +trainer/Advantage Score Mean -0.312946 +trainer/Advantage Score Std 0.626321 +trainer/Advantage Score Max 1.15961 +trainer/Advantage Score Min -3.83999 +trainer/V1 Predictions Mean -70.9652 +trainer/V1 Predictions Std 18.0113 +trainer/V1 Predictions Max -2.2057 +trainer/V1 Predictions Min -87.3437 +trainer/VF Loss 0.0736453 +expl/num steps total 637000 +expl/num paths total 826 +expl/path length Mean 500 +expl/path length Std 450 +expl/path length Max 950 +expl/path length Min 50 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0318023 +expl/Actions Std 0.813158 +expl/Actions Max 2.31566 +expl/Actions Min -2.71286 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 594487 +eval/num paths total 641 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00567293 +eval/Actions Std 0.632944 +eval/Actions Max 0.99994 +eval/Actions Min -0.998977 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.26017e-05 +time/evaluation sampling (s) 5.34095 +time/exploration sampling (s) 6.64189 +time/logging (s) 0.01718 +time/saving (s) 0.0198503 +time/training (s) 19.5621 +time/epoch (s) 31.582 +time/total (s) 15413 +Epoch -364 +------------------------------ ---------------- +2022-05-15 22:19:53.671779 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -363 finished +------------------------------ ---------------- +epoch -363 +replay_buffer/size 999047 +trainer/num train calls 638000 +trainer/QF1 Loss 0.947199 +trainer/QF2 Loss 0.761006 +trainer/Policy Loss 15.8746 +trainer/Q1 Predictions Mean -71.1372 +trainer/Q1 Predictions Std 17.8804 +trainer/Q1 Predictions Max -1.30014 +trainer/Q1 Predictions Min -86.4311 +trainer/Q2 Predictions Mean -71.1749 +trainer/Q2 Predictions Std 17.8431 +trainer/Q2 Predictions Max -1.11784 +trainer/Q2 Predictions Min -86.0651 +trainer/Q Targets Mean -71.2978 +trainer/Q Targets Std 17.6581 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0067 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00146754 +trainer/policy/mean Std 0.73142 +trainer/policy/mean Max 0.998692 +trainer/policy/mean Min -0.999478 +trainer/policy/std Mean 0.41723 +trainer/policy/std Std 0.020528 +trainer/policy/std Max 0.439012 +trainer/policy/std Min 0.384588 +trainer/Advantage Weights Mean 4.9866 +trainer/Advantage Weights Std 19.8431 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.185e-15 +trainer/Advantage Score Mean -0.501898 +trainer/Advantage Score Std 0.60672 +trainer/Advantage Score Max 2.69885 +trainer/Advantage Score Min -3.4369 +trainer/V1 Predictions Mean -71.1273 +trainer/V1 Predictions Std 17.5914 +trainer/V1 Predictions Max -2.07338 +trainer/V1 Predictions Min -86.1006 +trainer/VF Loss 0.101426 +expl/num steps total 638000 +expl/num paths total 828 +expl/path length Mean 500 +expl/path length Std 168 +expl/path length Max 668 +expl/path length Min 332 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0458399 +expl/Actions Std 0.823016 +expl/Actions Max 2.40683 +expl/Actions Min -2.16143 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 595084 +eval/num paths total 642 +eval/path length Mean 597 +eval/path length Std 0 +eval/path length Max 597 +eval/path length Min 597 +eval/Rewards Mean 0.00167504 +eval/Rewards Std 0.040893 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0592147 +eval/Actions Std 0.729645 +eval/Actions Max 0.998831 +eval/Actions Min -0.999214 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.22641e-05 +time/evaluation sampling (s) 5.67458 +time/exploration sampling (s) 6.36929 +time/logging (s) 0.0106543 +time/saving (s) 0.0188244 +time/training (s) 19.8657 +time/epoch (s) 31.9391 +time/total (s) 15445 +Epoch -363 +------------------------------ ---------------- +2022-05-15 22:20:26.591525 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -362 finished +------------------------------ ---------------- +epoch -362 +replay_buffer/size 999047 +trainer/num train calls 639000 +trainer/QF1 Loss 0.728723 +trainer/QF2 Loss 0.767297 +trainer/Policy Loss 21.6309 +trainer/Q1 Predictions Mean -72.6681 +trainer/Q1 Predictions Std 17.8857 +trainer/Q1 Predictions Max -0.857753 +trainer/Q1 Predictions Min -86.7343 +trainer/Q2 Predictions Mean -72.7057 +trainer/Q2 Predictions Std 17.8343 +trainer/Q2 Predictions Max -1.67845 +trainer/Q2 Predictions Min -86.7716 +trainer/Q Targets Mean -72.6327 +trainer/Q Targets Std 17.7487 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9422 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00786196 +trainer/policy/mean Std 0.720463 +trainer/policy/mean Max 0.999258 +trainer/policy/mean Min -0.999646 +trainer/policy/std Mean 0.417061 +trainer/policy/std Std 0.0212834 +trainer/policy/std Max 0.439571 +trainer/policy/std Min 0.383366 +trainer/Advantage Weights Mean 4.93013 +trainer/Advantage Weights Std 18.9123 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.84046e-14 +trainer/Advantage Score Mean -0.434407 +trainer/Advantage Score Std 0.569564 +trainer/Advantage Score Max 1.44149 +trainer/Advantage Score Min -3.04714 +trainer/V1 Predictions Mean -72.3995 +trainer/V1 Predictions Std 17.7878 +trainer/V1 Predictions Max -0.487082 +trainer/V1 Predictions Min -86.7527 +trainer/VF Loss 0.0792765 +expl/num steps total 639000 +expl/num paths total 829 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.331005 +expl/Actions Std 0.865704 +expl/Actions Max 2.32394 +expl/Actions Min -2.23541 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 596084 +eval/num paths total 643 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0987477 +eval/Actions Std 0.696003 +eval/Actions Max 0.999921 +eval/Actions Min -0.999778 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.29091e-05 +time/evaluation sampling (s) 5.84512 +time/exploration sampling (s) 7.61569 +time/logging (s) 0.0129909 +time/saving (s) 0.0154505 +time/training (s) 19.4141 +time/epoch (s) 32.9034 +time/total (s) 15477.9 +Epoch -362 +------------------------------ ---------------- +2022-05-15 22:20:57.605567 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -361 finished +------------------------------ ---------------- +epoch -361 +replay_buffer/size 999047 +trainer/num train calls 640000 +trainer/QF1 Loss 0.782732 +trainer/QF2 Loss 0.837984 +trainer/Policy Loss 20.7951 +trainer/Q1 Predictions Mean -71.8735 +trainer/Q1 Predictions Std 17.769 +trainer/Q1 Predictions Max -2.00492 +trainer/Q1 Predictions Min -87.2571 +trainer/Q2 Predictions Mean -71.8693 +trainer/Q2 Predictions Std 17.749 +trainer/Q2 Predictions Max -1.41195 +trainer/Q2 Predictions Min -86.8654 +trainer/Q Targets Mean -71.8535 +trainer/Q Targets Std 18.1524 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2122 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.013901 +trainer/policy/mean Std 0.716363 +trainer/policy/mean Max 0.999403 +trainer/policy/mean Min -0.999737 +trainer/policy/std Mean 0.41461 +trainer/policy/std Std 0.0212933 +trainer/policy/std Max 0.436313 +trainer/policy/std Min 0.377106 +trainer/Advantage Weights Mean 3.45247 +trainer/Advantage Weights Std 14.5962 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.04322e-17 +trainer/Advantage Score Mean -0.437705 +trainer/Advantage Score Std 0.641444 +trainer/Advantage Score Max 1.73037 +trainer/Advantage Score Min -3.75259 +trainer/V1 Predictions Mean -71.5782 +trainer/V1 Predictions Std 18.0048 +trainer/V1 Predictions Max -1.68479 +trainer/V1 Predictions Min -87.0237 +trainer/VF Loss 0.0834885 +expl/num steps total 640000 +expl/num paths total 830 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.217164 +expl/Actions Std 0.805373 +expl/Actions Max 2.15473 +expl/Actions Min -2.33378 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 597084 +eval/num paths total 644 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0732269 +eval/Actions Std 0.718412 +eval/Actions Max 0.999574 +eval/Actions Min -0.999513 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.852e-06 +time/evaluation sampling (s) 5.56088 +time/exploration sampling (s) 6.44296 +time/logging (s) 0.00888903 +time/saving (s) 0.0152125 +time/training (s) 18.9636 +time/epoch (s) 30.9916 +time/total (s) 15508.9 +Epoch -361 +------------------------------ ---------------- +2022-05-15 22:21:29.146582 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -360 finished +------------------------------ --------------- +epoch -360 +replay_buffer/size 999047 +trainer/num train calls 641000 +trainer/QF1 Loss 0.650979 +trainer/QF2 Loss 0.719181 +trainer/Policy Loss 47.4218 +trainer/Q1 Predictions Mean -72.4934 +trainer/Q1 Predictions Std 16.305 +trainer/Q1 Predictions Max -2.04051 +trainer/Q1 Predictions Min -86.0307 +trainer/Q2 Predictions Mean -72.6058 +trainer/Q2 Predictions Std 16.3623 +trainer/Q2 Predictions Max -1.12512 +trainer/Q2 Predictions Min -86.1782 +trainer/Q Targets Mean -72.7395 +trainer/Q Targets Std 16.4196 +trainer/Q Targets Max -3.96712 +trainer/Q Targets Min -86.1558 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.017917 +trainer/policy/mean Std 0.72268 +trainer/policy/mean Max 0.999334 +trainer/policy/mean Min -0.999069 +trainer/policy/std Mean 0.415524 +trainer/policy/std Std 0.02036 +trainer/policy/std Max 0.436841 +trainer/policy/std Min 0.380662 +trainer/Advantage Weights Mean 9.39443 +trainer/Advantage Weights Std 24.3311 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.4396e-21 +trainer/Advantage Score Mean -0.298918 +trainer/Advantage Score Std 0.750291 +trainer/Advantage Score Max 1.82757 +trainer/Advantage Score Min -4.62214 +trainer/V1 Predictions Mean -72.4779 +trainer/V1 Predictions Std 16.5713 +trainer/V1 Predictions Max -3.75291 +trainer/V1 Predictions Min -86.0334 +trainer/VF Loss 0.113314 +expl/num steps total 641000 +expl/num paths total 832 +expl/path length Mean 500 +expl/path length Std 468 +expl/path length Max 968 +expl/path length Min 32 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0281292 +expl/Actions Std 0.819571 +expl/Actions Max 2.2485 +expl/Actions Min -2.43894 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 598084 +eval/num paths total 645 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0411246 +eval/Actions Std 0.691325 +eval/Actions Max 0.998038 +eval/Actions Min -0.999228 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.8608e-06 +time/evaluation sampling (s) 5.32219 +time/exploration sampling (s) 6.50398 +time/logging (s) 0.012642 +time/saving (s) 0.0182777 +time/training (s) 19.6747 +time/epoch (s) 31.5318 +time/total (s) 15540.4 +Epoch -360 +------------------------------ --------------- +2022-05-15 22:22:00.633209 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -359 finished +------------------------------ ---------------- +epoch -359 +replay_buffer/size 999047 +trainer/num train calls 642000 +trainer/QF1 Loss 0.825079 +trainer/QF2 Loss 0.968809 +trainer/Policy Loss 13.0391 +trainer/Q1 Predictions Mean -71.8273 +trainer/Q1 Predictions Std 17.5185 +trainer/Q1 Predictions Max -3.84545 +trainer/Q1 Predictions Min -89.4544 +trainer/Q2 Predictions Mean -72.0525 +trainer/Q2 Predictions Std 17.544 +trainer/Q2 Predictions Max -3.84819 +trainer/Q2 Predictions Min -90.0262 +trainer/Q Targets Mean -71.7093 +trainer/Q Targets Std 17.8885 +trainer/Q Targets Max -0.780624 +trainer/Q Targets Min -88.8975 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0191688 +trainer/policy/mean Std 0.723472 +trainer/policy/mean Max 0.998745 +trainer/policy/mean Min -0.999236 +trainer/policy/std Mean 0.415288 +trainer/policy/std Std 0.0209117 +trainer/policy/std Max 0.437998 +trainer/policy/std Min 0.382601 +trainer/Advantage Weights Mean 4.24829 +trainer/Advantage Weights Std 16.9769 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.59995e-17 +trainer/Advantage Score Mean -0.358477 +trainer/Advantage Score Std 0.661359 +trainer/Advantage Score Max 1.92315 +trainer/Advantage Score Min -3.74212 +trainer/V1 Predictions Mean -71.5056 +trainer/V1 Predictions Std 17.8934 +trainer/V1 Predictions Max -0.551331 +trainer/V1 Predictions Min -88.9528 +trainer/VF Loss 0.0881014 +expl/num steps total 642000 +expl/num paths total 834 +expl/path length Mean 500 +expl/path length Std 285 +expl/path length Max 785 +expl/path length Min 215 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0336477 +expl/Actions Std 0.830495 +expl/Actions Max 2.37367 +expl/Actions Min -2.37732 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 598570 +eval/num paths total 646 +eval/path length Mean 486 +eval/path length Std 0 +eval/path length Max 486 +eval/path length Min 486 +eval/Rewards Mean 0.00205761 +eval/Rewards Std 0.0453142 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0191992 +eval/Actions Std 0.736757 +eval/Actions Max 0.99983 +eval/Actions Min -0.999149 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.9318e-06 +time/evaluation sampling (s) 5.1939 +time/exploration sampling (s) 6.15488 +time/logging (s) 0.00912542 +time/saving (s) 0.0202178 +time/training (s) 20.0867 +time/epoch (s) 31.4648 +time/total (s) 15571.9 +Epoch -359 +------------------------------ ---------------- +2022-05-15 22:22:32.196694 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -358 finished +------------------------------ ---------------- +epoch -358 +replay_buffer/size 999047 +trainer/num train calls 643000 +trainer/QF1 Loss 1.00824 +trainer/QF2 Loss 0.965692 +trainer/Policy Loss 8.08294 +trainer/Q1 Predictions Mean -71.1473 +trainer/Q1 Predictions Std 20.1584 +trainer/Q1 Predictions Max -1.13655 +trainer/Q1 Predictions Min -86.4678 +trainer/Q2 Predictions Mean -71.156 +trainer/Q2 Predictions Std 20.174 +trainer/Q2 Predictions Max -2.51458 +trainer/Q2 Predictions Min -86.6092 +trainer/Q Targets Mean -70.7681 +trainer/Q Targets Std 20.3517 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5185 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0129329 +trainer/policy/mean Std 0.724174 +trainer/policy/mean Max 0.999682 +trainer/policy/mean Min -0.999489 +trainer/policy/std Mean 0.415926 +trainer/policy/std Std 0.0214647 +trainer/policy/std Max 0.43877 +trainer/policy/std Min 0.379727 +trainer/Advantage Weights Mean 2.03957 +trainer/Advantage Weights Std 11.8137 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.66252e-13 +trainer/Advantage Score Mean -0.599514 +trainer/Advantage Score Std 0.483563 +trainer/Advantage Score Max 1.16801 +trainer/Advantage Score Min -2.83941 +trainer/V1 Predictions Mean -70.5172 +trainer/V1 Predictions Std 20.4149 +trainer/V1 Predictions Max -1.61459 +trainer/V1 Predictions Min -86.4755 +trainer/VF Loss 0.0677813 +expl/num steps total 643000 +expl/num paths total 836 +expl/path length Mean 500 +expl/path length Std 303 +expl/path length Max 803 +expl/path length Min 197 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0487605 +expl/Actions Std 0.829781 +expl/Actions Max 2.17281 +expl/Actions Min -2.44452 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 599518 +eval/num paths total 648 +eval/path length Mean 474 +eval/path length Std 81 +eval/path length Max 555 +eval/path length Min 393 +eval/Rewards Mean 0.0021097 +eval/Rewards Std 0.045883 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0242887 +eval/Actions Std 0.739393 +eval/Actions Max 0.9998 +eval/Actions Min -0.999688 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 1.24159e-05 +time/evaluation sampling (s) 4.88936 +time/exploration sampling (s) 6.80928 +time/logging (s) 0.00954753 +time/saving (s) 0.0182979 +time/training (s) 19.8221 +time/epoch (s) 31.5486 +time/total (s) 15603.5 +Epoch -358 +------------------------------ ---------------- +2022-05-15 22:23:04.301183 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -357 finished +------------------------------ ---------------- +epoch -357 +replay_buffer/size 999047 +trainer/num train calls 644000 +trainer/QF1 Loss 6.06252 +trainer/QF2 Loss 6.0364 +trainer/Policy Loss 15.768 +trainer/Q1 Predictions Mean -72.4435 +trainer/Q1 Predictions Std 18.1557 +trainer/Q1 Predictions Max -0.0395502 +trainer/Q1 Predictions Min -88.5283 +trainer/Q2 Predictions Mean -72.6176 +trainer/Q2 Predictions Std 18.0544 +trainer/Q2 Predictions Max -0.462068 +trainer/Q2 Predictions Min -88.2994 +trainer/Q Targets Mean -72.1267 +trainer/Q Targets Std 18.19 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.2971 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00224267 +trainer/policy/mean Std 0.736706 +trainer/policy/mean Max 0.999727 +trainer/policy/mean Min -0.998098 +trainer/policy/std Mean 0.416719 +trainer/policy/std Std 0.0195874 +trainer/policy/std Max 0.436744 +trainer/policy/std Min 0.385848 +trainer/Advantage Weights Mean 2.97126 +trainer/Advantage Weights Std 13.5806 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.19516e-30 +trainer/Advantage Score Mean -0.537491 +trainer/Advantage Score Std 0.739599 +trainer/Advantage Score Max 0.75647 +trainer/Advantage Score Min -6.79159 +trainer/V1 Predictions Mean -71.9653 +trainer/V1 Predictions Std 18.2123 +trainer/V1 Predictions Max 0.508832 +trainer/V1 Predictions Min -87.9611 +trainer/VF Loss 0.0931031 +expl/num steps total 644000 +expl/num paths total 837 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.185623 +expl/Actions Std 0.86106 +expl/Actions Max 2.31714 +expl/Actions Min -2.41405 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 600518 +eval/num paths total 649 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.26203 +eval/Actions Std 0.682576 +eval/Actions Max 0.999643 +eval/Actions Min -0.999753 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.18511e-05 +time/evaluation sampling (s) 5.04979 +time/exploration sampling (s) 7.3376 +time/logging (s) 0.0116288 +time/saving (s) 0.0155336 +time/training (s) 19.6812 +time/epoch (s) 32.0957 +time/total (s) 15635.6 +Epoch -357 +------------------------------ ---------------- +2022-05-15 22:23:36.912179 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -356 finished +------------------------------ ---------------- +epoch -356 +replay_buffer/size 999047 +trainer/num train calls 645000 +trainer/QF1 Loss 0.544962 +trainer/QF2 Loss 0.513029 +trainer/Policy Loss 3.72741 +trainer/Q1 Predictions Mean -71.7131 +trainer/Q1 Predictions Std 18.4642 +trainer/Q1 Predictions Max 0.787089 +trainer/Q1 Predictions Min -86.2478 +trainer/Q2 Predictions Mean -71.6807 +trainer/Q2 Predictions Std 18.4833 +trainer/Q2 Predictions Max -0.363953 +trainer/Q2 Predictions Min -85.9057 +trainer/Q Targets Mean -71.3726 +trainer/Q Targets Std 18.51 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7525 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00483976 +trainer/policy/mean Std 0.720876 +trainer/policy/mean Max 0.999334 +trainer/policy/mean Min -0.999003 +trainer/policy/std Mean 0.416436 +trainer/policy/std Std 0.0199454 +trainer/policy/std Max 0.438625 +trainer/policy/std Min 0.384302 +trainer/Advantage Weights Mean 1.55644 +trainer/Advantage Weights Std 10.8743 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.82527e-14 +trainer/Advantage Score Mean -0.536708 +trainer/Advantage Score Std 0.562777 +trainer/Advantage Score Max 1.39385 +trainer/Advantage Score Min -3.01788 +trainer/V1 Predictions Mean -71.1325 +trainer/V1 Predictions Std 18.6356 +trainer/V1 Predictions Max 1.08104 +trainer/V1 Predictions Min -85.5261 +trainer/VF Loss 0.0762972 +expl/num steps total 645000 +expl/num paths total 838 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.177045 +expl/Actions Std 0.786708 +expl/Actions Max 2.1876 +expl/Actions Min -2.47334 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 601216 +eval/num paths total 650 +eval/path length Mean 698 +eval/path length Std 0 +eval/path length Max 698 +eval/path length Min 698 +eval/Rewards Mean 0.00143266 +eval/Rewards Std 0.0378234 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0134482 +eval/Actions Std 0.731082 +eval/Actions Max 0.999519 +eval/Actions Min -0.999335 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.30651e-05 +time/evaluation sampling (s) 5.77726 +time/exploration sampling (s) 6.96333 +time/logging (s) 0.0108261 +time/saving (s) 0.0180452 +time/training (s) 19.8247 +time/epoch (s) 32.5942 +time/total (s) 15668.2 +Epoch -356 +------------------------------ ---------------- +2022-05-15 22:24:08.883024 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -355 finished +------------------------------ ---------------- +epoch -355 +replay_buffer/size 999047 +trainer/num train calls 646000 +trainer/QF1 Loss 0.555495 +trainer/QF2 Loss 0.54624 +trainer/Policy Loss 9.29188 +trainer/Q1 Predictions Mean -71.5286 +trainer/Q1 Predictions Std 19.3879 +trainer/Q1 Predictions Max -1.36813 +trainer/Q1 Predictions Min -88.607 +trainer/Q2 Predictions Mean -71.4882 +trainer/Q2 Predictions Std 19.4063 +trainer/Q2 Predictions Max -0.941849 +trainer/Q2 Predictions Min -88.3679 +trainer/Q Targets Mean -71.677 +trainer/Q Targets Std 19.4519 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4433 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00857703 +trainer/policy/mean Std 0.702939 +trainer/policy/mean Max 0.999336 +trainer/policy/mean Min -0.999494 +trainer/policy/std Mean 0.416415 +trainer/policy/std Std 0.0208979 +trainer/policy/std Max 0.437143 +trainer/policy/std Min 0.38506 +trainer/Advantage Weights Mean 2.40231 +trainer/Advantage Weights Std 11.4315 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.20282e-32 +trainer/Advantage Score Mean -0.383484 +trainer/Advantage Score Std 0.677884 +trainer/Advantage Score Max 0.969379 +trainer/Advantage Score Min -7.18577 +trainer/V1 Predictions Mean -71.4048 +trainer/V1 Predictions Std 19.5377 +trainer/V1 Predictions Max -1.08649 +trainer/V1 Predictions Min -88.4744 +trainer/VF Loss 0.0725546 +expl/num steps total 646000 +expl/num paths total 840 +expl/path length Mean 500 +expl/path length Std 414 +expl/path length Max 914 +expl/path length Min 86 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0339356 +expl/Actions Std 0.810649 +expl/Actions Max 2.28608 +expl/Actions Min -2.18604 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 601907 +eval/num paths total 651 +eval/path length Mean 691 +eval/path length Std 0 +eval/path length Max 691 +eval/path length Min 691 +eval/Rewards Mean 0.00144718 +eval/Rewards Std 0.0380143 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0446132 +eval/Actions Std 0.726698 +eval/Actions Max 0.999511 +eval/Actions Min -0.999925 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.21729e-05 +time/evaluation sampling (s) 5.2763 +time/exploration sampling (s) 7.14714 +time/logging (s) 0.0109929 +time/saving (s) 0.0170237 +time/training (s) 19.5007 +time/epoch (s) 31.9521 +time/total (s) 15700.1 +Epoch -355 +------------------------------ ---------------- +2022-05-15 22:24:41.361285 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -354 finished +------------------------------ ---------------- +epoch -354 +replay_buffer/size 999047 +trainer/num train calls 647000 +trainer/QF1 Loss 1.03495 +trainer/QF2 Loss 1.2938 +trainer/Policy Loss 21.2219 +trainer/Q1 Predictions Mean -70.8722 +trainer/Q1 Predictions Std 20.0457 +trainer/Q1 Predictions Max -0.451821 +trainer/Q1 Predictions Min -87.1313 +trainer/Q2 Predictions Mean -70.9616 +trainer/Q2 Predictions Std 19.9196 +trainer/Q2 Predictions Max 0.319332 +trainer/Q2 Predictions Min -87.1824 +trainer/Q Targets Mean -70.4588 +trainer/Q Targets Std 20.3573 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6201 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00439179 +trainer/policy/mean Std 0.729432 +trainer/policy/mean Max 0.999876 +trainer/policy/mean Min -0.998425 +trainer/policy/std Mean 0.416558 +trainer/policy/std Std 0.0207322 +trainer/policy/std Max 0.43768 +trainer/policy/std Min 0.383319 +trainer/Advantage Weights Mean 3.72292 +trainer/Advantage Weights Std 15.0041 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.69737e-24 +trainer/Advantage Score Mean -0.475929 +trainer/Advantage Score Std 0.819284 +trainer/Advantage Score Max 1.03455 +trainer/Advantage Score Min -5.4733 +trainer/V1 Predictions Mean -70.1758 +trainer/V1 Predictions Std 20.5059 +trainer/V1 Predictions Max 0.302743 +trainer/V1 Predictions Min -86.3857 +trainer/VF Loss 0.102898 +expl/num steps total 647000 +expl/num paths total 842 +expl/path length Mean 500 +expl/path length Std 46 +expl/path length Max 546 +expl/path length Min 454 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0101678 +expl/Actions Std 0.851889 +expl/Actions Max 2.55236 +expl/Actions Min -2.35998 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 602899 +eval/num paths total 653 +eval/path length Mean 496 +eval/path length Std 39 +eval/path length Max 535 +eval/path length Min 457 +eval/Rewards Mean 0.00201613 +eval/Rewards Std 0.044856 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.02895 +eval/Actions Std 0.736611 +eval/Actions Max 0.999476 +eval/Actions Min -0.999837 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 1.30069e-05 +time/evaluation sampling (s) 5.28818 +time/exploration sampling (s) 6.82714 +time/logging (s) 0.0128108 +time/saving (s) 0.0177 +time/training (s) 20.3158 +time/epoch (s) 32.4616 +time/total (s) 15732.6 +Epoch -354 +------------------------------ ---------------- +2022-05-15 22:25:12.706373 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -353 finished +------------------------------ ---------------- +epoch -353 +replay_buffer/size 999047 +trainer/num train calls 648000 +trainer/QF1 Loss 0.703927 +trainer/QF2 Loss 0.624403 +trainer/Policy Loss 17.8686 +trainer/Q1 Predictions Mean -73.3899 +trainer/Q1 Predictions Std 18.0539 +trainer/Q1 Predictions Max 0.637507 +trainer/Q1 Predictions Min -87.0126 +trainer/Q2 Predictions Mean -73.3666 +trainer/Q2 Predictions Std 18.064 +trainer/Q2 Predictions Max -0.385293 +trainer/Q2 Predictions Min -86.7473 +trainer/Q Targets Mean -73.3922 +trainer/Q Targets Std 18.1124 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0281 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000405083 +trainer/policy/mean Std 0.724539 +trainer/policy/mean Max 0.999309 +trainer/policy/mean Min -0.999729 +trainer/policy/std Mean 0.418374 +trainer/policy/std Std 0.0213722 +trainer/policy/std Max 0.439909 +trainer/policy/std Min 0.381694 +trainer/Advantage Weights Mean 4.13204 +trainer/Advantage Weights Std 15.0905 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.22487e-21 +trainer/Advantage Score Mean -0.283756 +trainer/Advantage Score Std 0.602266 +trainer/Advantage Score Max 0.645859 +trainer/Advantage Score Min -4.69133 +trainer/V1 Predictions Mean -73.2184 +trainer/V1 Predictions Std 18.0535 +trainer/V1 Predictions Max 1.15337 +trainer/V1 Predictions Min -86.8722 +trainer/VF Loss 0.0557918 +expl/num steps total 648000 +expl/num paths total 844 +expl/path length Mean 500 +expl/path length Std 90 +expl/path length Max 590 +expl/path length Min 410 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0268675 +expl/Actions Std 0.822858 +expl/Actions Max 2.28609 +expl/Actions Min -2.23959 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 603584 +eval/num paths total 654 +eval/path length Mean 685 +eval/path length Std 0 +eval/path length Max 685 +eval/path length Min 685 +eval/Rewards Mean 0.00145985 +eval/Rewards Std 0.0381801 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00525453 +eval/Actions Std 0.742379 +eval/Actions Max 0.999472 +eval/Actions Min -0.999838 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.06501e-05 +time/evaluation sampling (s) 5.20365 +time/exploration sampling (s) 6.67355 +time/logging (s) 0.0101672 +time/saving (s) 0.0158981 +time/training (s) 19.4205 +time/epoch (s) 31.3238 +time/total (s) 15763.9 +Epoch -353 +------------------------------ ---------------- +2022-05-15 22:25:44.641990 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -352 finished +------------------------------ ---------------- +epoch -352 +replay_buffer/size 999047 +trainer/num train calls 649000 +trainer/QF1 Loss 0.894227 +trainer/QF2 Loss 0.759813 +trainer/Policy Loss 35.0992 +trainer/Q1 Predictions Mean -71.4842 +trainer/Q1 Predictions Std 17.869 +trainer/Q1 Predictions Max -3.72838 +trainer/Q1 Predictions Min -85.97 +trainer/Q2 Predictions Mean -71.5867 +trainer/Q2 Predictions Std 17.9451 +trainer/Q2 Predictions Max -3.12381 +trainer/Q2 Predictions Min -86.1864 +trainer/Q Targets Mean -71.8693 +trainer/Q Targets Std 18.0117 +trainer/Q Targets Max -2.67054 +trainer/Q Targets Min -86.8462 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00342327 +trainer/policy/mean Std 0.71517 +trainer/policy/mean Max 0.99806 +trainer/policy/mean Min -0.998615 +trainer/policy/std Mean 0.416959 +trainer/policy/std Std 0.0210531 +trainer/policy/std Max 0.440871 +trainer/policy/std Min 0.384152 +trainer/Advantage Weights Mean 6.76661 +trainer/Advantage Weights Std 20.7247 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.80563e-19 +trainer/Advantage Score Mean -0.357484 +trainer/Advantage Score Std 0.693641 +trainer/Advantage Score Max 1.34297 +trainer/Advantage Score Min -4.31582 +trainer/V1 Predictions Mean -71.5215 +trainer/V1 Predictions Std 18.1566 +trainer/V1 Predictions Max -3.48761 +trainer/V1 Predictions Min -86.5117 +trainer/VF Loss 0.0920675 +expl/num steps total 649000 +expl/num paths total 845 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0161829 +expl/Actions Std 0.794204 +expl/Actions Max 2.38383 +expl/Actions Min -2.41733 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 604584 +eval/num paths total 655 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.223806 +eval/Actions Std 0.703142 +eval/Actions Max 0.999586 +eval/Actions Min -0.999723 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.89898e-06 +time/evaluation sampling (s) 5.28202 +time/exploration sampling (s) 6.6477 +time/logging (s) 0.0115422 +time/saving (s) 0.0163289 +time/training (s) 19.9609 +time/epoch (s) 31.9185 +time/total (s) 15795.8 +Epoch -352 +------------------------------ ---------------- +2022-05-15 22:26:16.269478 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -351 finished +------------------------------ ---------------- +epoch -351 +replay_buffer/size 999047 +trainer/num train calls 650000 +trainer/QF1 Loss 1.25034 +trainer/QF2 Loss 1.45165 +trainer/Policy Loss 11.9532 +trainer/Q1 Predictions Mean -72.5568 +trainer/Q1 Predictions Std 18.4722 +trainer/Q1 Predictions Max -4.50974 +trainer/Q1 Predictions Min -86.3287 +trainer/Q2 Predictions Mean -72.5233 +trainer/Q2 Predictions Std 18.5643 +trainer/Q2 Predictions Max -3.73524 +trainer/Q2 Predictions Min -87.0363 +trainer/Q Targets Mean -72.2666 +trainer/Q Targets Std 18.1298 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7151 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.016811 +trainer/policy/mean Std 0.719436 +trainer/policy/mean Max 0.998814 +trainer/policy/mean Min -0.998845 +trainer/policy/std Mean 0.415985 +trainer/policy/std Std 0.0205047 +trainer/policy/std Max 0.43895 +trainer/policy/std Min 0.383345 +trainer/Advantage Weights Mean 3.00216 +trainer/Advantage Weights Std 16.3096 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.7836e-18 +trainer/Advantage Score Mean -0.513971 +trainer/Advantage Score Std 0.569158 +trainer/Advantage Score Max 1.06866 +trainer/Advantage Score Min -3.96915 +trainer/V1 Predictions Mean -71.9579 +trainer/V1 Predictions Std 18.4163 +trainer/V1 Predictions Max -2.1527 +trainer/V1 Predictions Min -85.9652 +trainer/VF Loss 0.0739398 +expl/num steps total 650000 +expl/num paths total 847 +expl/path length Mean 500 +expl/path length Std 255 +expl/path length Max 755 +expl/path length Min 245 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0250739 +expl/Actions Std 0.82831 +expl/Actions Max 2.33099 +expl/Actions Min -2.41435 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 605584 +eval/num paths total 656 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0355123 +eval/Actions Std 0.740287 +eval/Actions Max 0.999075 +eval/Actions Min -0.999375 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.09258e-05 +time/evaluation sampling (s) 5.19627 +time/exploration sampling (s) 6.63096 +time/logging (s) 0.0107334 +time/saving (s) 0.0163044 +time/training (s) 19.7565 +time/epoch (s) 31.6108 +time/total (s) 15827.5 +Epoch -351 +------------------------------ ---------------- +2022-05-15 22:26:48.789962 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -350 finished +------------------------------ ---------------- +epoch -350 +replay_buffer/size 999047 +trainer/num train calls 651000 +trainer/QF1 Loss 1.28711 +trainer/QF2 Loss 1.11209 +trainer/Policy Loss 30.8001 +trainer/Q1 Predictions Mean -72.0686 +trainer/Q1 Predictions Std 17.362 +trainer/Q1 Predictions Max -1.4161 +trainer/Q1 Predictions Min -88.6389 +trainer/Q2 Predictions Mean -72.0468 +trainer/Q2 Predictions Std 17.4292 +trainer/Q2 Predictions Max -0.565098 +trainer/Q2 Predictions Min -88.4586 +trainer/Q Targets Mean -72.037 +trainer/Q Targets Std 17.9402 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.1238 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0069387 +trainer/policy/mean Std 0.726174 +trainer/policy/mean Max 0.999437 +trainer/policy/mean Min -0.999704 +trainer/policy/std Mean 0.414958 +trainer/policy/std Std 0.0197628 +trainer/policy/std Max 0.434988 +trainer/policy/std Min 0.379804 +trainer/Advantage Weights Mean 5.26701 +trainer/Advantage Weights Std 18.996 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.82694e-21 +trainer/Advantage Score Mean -0.422771 +trainer/Advantage Score Std 0.807352 +trainer/Advantage Score Max 4.03775 +trainer/Advantage Score Min -4.64334 +trainer/V1 Predictions Mean -71.8263 +trainer/V1 Predictions Std 17.877 +trainer/V1 Predictions Max 0.62663 +trainer/V1 Predictions Min -88.9709 +trainer/VF Loss 0.153137 +expl/num steps total 651000 +expl/num paths total 849 +expl/path length Mean 500 +expl/path length Std 446 +expl/path length Max 946 +expl/path length Min 54 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0177449 +expl/Actions Std 0.82601 +expl/Actions Max 2.22283 +expl/Actions Min -2.3459 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 606375 +eval/num paths total 657 +eval/path length Mean 791 +eval/path length Std 0 +eval/path length Max 791 +eval/path length Min 791 +eval/Rewards Mean 0.00126422 +eval/Rewards Std 0.0355334 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.028598 +eval/Actions Std 0.719317 +eval/Actions Max 0.999821 +eval/Actions Min -0.999644 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.01961e-05 +time/evaluation sampling (s) 5.00433 +time/exploration sampling (s) 7.60969 +time/logging (s) 0.00994386 +time/saving (s) 0.0237632 +time/training (s) 19.8608 +time/epoch (s) 32.5085 +time/total (s) 15860 +Epoch -350 +------------------------------ ---------------- +2022-05-15 22:27:20.631122 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -349 finished +------------------------------ ---------------- +epoch -349 +replay_buffer/size 999047 +trainer/num train calls 652000 +trainer/QF1 Loss 2.17883 +trainer/QF2 Loss 2.12879 +trainer/Policy Loss 9.45295 +trainer/Q1 Predictions Mean -67.5791 +trainer/Q1 Predictions Std 22.8237 +trainer/Q1 Predictions Max -0.876522 +trainer/Q1 Predictions Min -87.9577 +trainer/Q2 Predictions Mean -67.5129 +trainer/Q2 Predictions Std 22.763 +trainer/Q2 Predictions Max -0.238977 +trainer/Q2 Predictions Min -87.6399 +trainer/Q Targets Mean -67.3186 +trainer/Q Targets Std 23.1144 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6617 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00314686 +trainer/policy/mean Std 0.724128 +trainer/policy/mean Max 0.99898 +trainer/policy/mean Min -0.998706 +trainer/policy/std Mean 0.416571 +trainer/policy/std Std 0.0199138 +trainer/policy/std Max 0.43774 +trainer/policy/std Min 0.386296 +trainer/Advantage Weights Mean 2.4908 +trainer/Advantage Weights Std 13.9983 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.23496e-20 +trainer/Advantage Score Mean -0.599217 +trainer/Advantage Score Std 0.741582 +trainer/Advantage Score Max 0.786111 +trainer/Advantage Score Min -4.58407 +trainer/V1 Predictions Mean -67.1057 +trainer/V1 Predictions Std 23.0832 +trainer/V1 Predictions Max -0.16525 +trainer/V1 Predictions Min -87.0405 +trainer/VF Loss 0.0990324 +expl/num steps total 652000 +expl/num paths total 851 +expl/path length Mean 500 +expl/path length Std 139 +expl/path length Max 639 +expl/path length Min 361 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0165842 +expl/Actions Std 0.826376 +expl/Actions Max 2.39256 +expl/Actions Min -2.41959 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 606853 +eval/num paths total 658 +eval/path length Mean 478 +eval/path length Std 0 +eval/path length Max 478 +eval/path length Min 478 +eval/Rewards Mean 0.00209205 +eval/Rewards Std 0.0456911 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0577365 +eval/Actions Std 0.732033 +eval/Actions Max 0.999017 +eval/Actions Min -0.999628 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.21756e-05 +time/evaluation sampling (s) 5.41059 +time/exploration sampling (s) 6.72439 +time/logging (s) 0.0103087 +time/saving (s) 0.0187289 +time/training (s) 19.666 +time/epoch (s) 31.83 +time/total (s) 15891.8 +Epoch -349 +------------------------------ ---------------- +2022-05-15 22:27:52.272625 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -348 finished +------------------------------ ---------------- +epoch -348 +replay_buffer/size 999047 +trainer/num train calls 653000 +trainer/QF1 Loss 0.81095 +trainer/QF2 Loss 1.01157 +trainer/Policy Loss 28.3652 +trainer/Q1 Predictions Mean -71.0931 +trainer/Q1 Predictions Std 18.7106 +trainer/Q1 Predictions Max -0.387828 +trainer/Q1 Predictions Min -86.0644 +trainer/Q2 Predictions Mean -71.0141 +trainer/Q2 Predictions Std 18.64 +trainer/Q2 Predictions Max -0.366002 +trainer/Q2 Predictions Min -85.8363 +trainer/Q Targets Mean -71.038 +trainer/Q Targets Std 18.834 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0559 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0282931 +trainer/policy/mean Std 0.724532 +trainer/policy/mean Max 0.999051 +trainer/policy/mean Min -0.998958 +trainer/policy/std Mean 0.41516 +trainer/policy/std Std 0.0206363 +trainer/policy/std Max 0.44045 +trainer/policy/std Min 0.38735 +trainer/Advantage Weights Mean 3.69374 +trainer/Advantage Weights Std 17.4388 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.60534e-15 +trainer/Advantage Score Mean -0.492621 +trainer/Advantage Score Std 0.595223 +trainer/Advantage Score Max 1.41787 +trainer/Advantage Score Min -3.28151 +trainer/V1 Predictions Mean -70.7487 +trainer/V1 Predictions Std 18.8752 +trainer/V1 Predictions Max 0.797883 +trainer/V1 Predictions Min -86.021 +trainer/VF Loss 0.0772544 +expl/num steps total 653000 +expl/num paths total 852 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00898209 +expl/Actions Std 0.828678 +expl/Actions Max 2.17459 +expl/Actions Min -2.21559 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 607853 +eval/num paths total 659 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.141208 +eval/Actions Std 0.72544 +eval/Actions Max 0.999424 +eval/Actions Min -0.99952 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.03298e-05 +time/evaluation sampling (s) 5.06182 +time/exploration sampling (s) 7.11278 +time/logging (s) 0.012618 +time/saving (s) 0.0235305 +time/training (s) 19.4141 +time/epoch (s) 31.6248 +time/total (s) 15923.4 +Epoch -348 +------------------------------ ---------------- +2022-05-15 22:28:24.898214 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -347 finished +------------------------------ ---------------- +epoch -347 +replay_buffer/size 999047 +trainer/num train calls 654000 +trainer/QF1 Loss 0.806799 +trainer/QF2 Loss 0.665041 +trainer/Policy Loss 10.4666 +trainer/Q1 Predictions Mean -72.4119 +trainer/Q1 Predictions Std 16.3737 +trainer/Q1 Predictions Max -1.46982 +trainer/Q1 Predictions Min -86.4824 +trainer/Q2 Predictions Mean -72.3275 +trainer/Q2 Predictions Std 16.3634 +trainer/Q2 Predictions Max -0.222477 +trainer/Q2 Predictions Min -86.3132 +trainer/Q Targets Mean -72.1467 +trainer/Q Targets Std 16.2926 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9897 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00709001 +trainer/policy/mean Std 0.732445 +trainer/policy/mean Max 0.997707 +trainer/policy/mean Min -0.998227 +trainer/policy/std Mean 0.413525 +trainer/policy/std Std 0.019674 +trainer/policy/std Max 0.43612 +trainer/policy/std Min 0.386665 +trainer/Advantage Weights Mean 2.27105 +trainer/Advantage Weights Std 13.9664 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.36244e-15 +trainer/Advantage Score Mean -0.602357 +trainer/Advantage Score Std 0.656121 +trainer/Advantage Score Max 1.51234 +trainer/Advantage Score Min -3.33261 +trainer/V1 Predictions Mean -71.8326 +trainer/V1 Predictions Std 16.4641 +trainer/V1 Predictions Max -0.290172 +trainer/V1 Predictions Min -86.1056 +trainer/VF Loss 0.096244 +expl/num steps total 654000 +expl/num paths total 853 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.195536 +expl/Actions Std 0.795567 +expl/Actions Max 2.53582 +expl/Actions Min -2.34212 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 608657 +eval/num paths total 660 +eval/path length Mean 804 +eval/path length Std 0 +eval/path length Max 804 +eval/path length Min 804 +eval/Rewards Mean 0.00124378 +eval/Rewards Std 0.0352453 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0129156 +eval/Actions Std 0.735555 +eval/Actions Max 0.999432 +eval/Actions Min -0.99997 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.23121e-05 +time/evaluation sampling (s) 5.47486 +time/exploration sampling (s) 7.34319 +time/logging (s) 0.0116851 +time/saving (s) 0.0190625 +time/training (s) 19.7567 +time/epoch (s) 32.6055 +time/total (s) 15956.1 +Epoch -347 +------------------------------ ---------------- +2022-05-15 22:28:56.515033 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -346 finished +------------------------------ ---------------- +epoch -346 +replay_buffer/size 999047 +trainer/num train calls 655000 +trainer/QF1 Loss 0.680015 +trainer/QF2 Loss 0.668095 +trainer/Policy Loss 9.24839 +trainer/Q1 Predictions Mean -71.9513 +trainer/Q1 Predictions Std 19.4734 +trainer/Q1 Predictions Max -0.473925 +trainer/Q1 Predictions Min -87.4669 +trainer/Q2 Predictions Mean -71.9681 +trainer/Q2 Predictions Std 19.3818 +trainer/Q2 Predictions Max -0.569928 +trainer/Q2 Predictions Min -87.4405 +trainer/Q Targets Mean -71.9627 +trainer/Q Targets Std 19.514 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3573 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00664296 +trainer/policy/mean Std 0.719426 +trainer/policy/mean Max 0.999832 +trainer/policy/mean Min -0.999945 +trainer/policy/std Mean 0.415456 +trainer/policy/std Std 0.0200515 +trainer/policy/std Max 0.435393 +trainer/policy/std Min 0.384819 +trainer/Advantage Weights Mean 4.11656 +trainer/Advantage Weights Std 17.2409 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.46778e-19 +trainer/Advantage Score Mean -0.400044 +trainer/Advantage Score Std 0.659475 +trainer/Advantage Score Max 1.22604 +trainer/Advantage Score Min -4.33654 +trainer/V1 Predictions Mean -71.6932 +trainer/V1 Predictions Std 19.6078 +trainer/V1 Predictions Max -0.661983 +trainer/V1 Predictions Min -87.4724 +trainer/VF Loss 0.0820401 +expl/num steps total 655000 +expl/num paths total 855 +expl/path length Mean 500 +expl/path length Std 1 +expl/path length Max 501 +expl/path length Min 499 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0119628 +expl/Actions Std 0.842518 +expl/Actions Max 2.43723 +expl/Actions Min -2.44228 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 609148 +eval/num paths total 661 +eval/path length Mean 491 +eval/path length Std 0 +eval/path length Max 491 +eval/path length Min 491 +eval/Rewards Mean 0.00203666 +eval/Rewards Std 0.0450834 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.01563 +eval/Actions Std 0.736735 +eval/Actions Max 0.999938 +eval/Actions Min -0.999878 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.35233e-05 +time/evaluation sampling (s) 5.5486 +time/exploration sampling (s) 6.54264 +time/logging (s) 0.00981196 +time/saving (s) 0.0153702 +time/training (s) 19.4792 +time/epoch (s) 31.5956 +time/total (s) 15987.7 +Epoch -346 +------------------------------ ---------------- +2022-05-15 22:29:28.159838 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -345 finished +------------------------------ ---------------- +epoch -345 +replay_buffer/size 999047 +trainer/num train calls 656000 +trainer/QF1 Loss 0.552239 +trainer/QF2 Loss 0.540696 +trainer/Policy Loss 7.18082 +trainer/Q1 Predictions Mean -70.6017 +trainer/Q1 Predictions Std 19.6608 +trainer/Q1 Predictions Max 0.11706 +trainer/Q1 Predictions Min -86.0428 +trainer/Q2 Predictions Mean -70.5727 +trainer/Q2 Predictions Std 19.7649 +trainer/Q2 Predictions Max -0.366248 +trainer/Q2 Predictions Min -86.2096 +trainer/Q Targets Mean -70.5712 +trainer/Q Targets Std 19.7826 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5317 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00260648 +trainer/policy/mean Std 0.732658 +trainer/policy/mean Max 0.998592 +trainer/policy/mean Min -0.999605 +trainer/policy/std Mean 0.415116 +trainer/policy/std Std 0.021038 +trainer/policy/std Max 0.436454 +trainer/policy/std Min 0.384447 +trainer/Advantage Weights Mean 1.85422 +trainer/Advantage Weights Std 11.693 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.04249e-18 +trainer/Advantage Score Mean -0.550278 +trainer/Advantage Score Std 0.621859 +trainer/Advantage Score Max 2.17862 +trainer/Advantage Score Min -4.14049 +trainer/V1 Predictions Mean -70.2985 +trainer/V1 Predictions Std 19.9628 +trainer/V1 Predictions Max 0.950254 +trainer/V1 Predictions Min -86.5514 +trainer/VF Loss 0.0882722 +expl/num steps total 656000 +expl/num paths total 856 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0347239 +expl/Actions Std 0.835853 +expl/Actions Max 2.28537 +expl/Actions Min -2.23754 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 610148 +eval/num paths total 662 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0296127 +eval/Actions Std 0.792574 +eval/Actions Max 0.999894 +eval/Actions Min -0.999251 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2551e-05 +time/evaluation sampling (s) 5.36769 +time/exploration sampling (s) 6.82637 +time/logging (s) 0.0116017 +time/saving (s) 0.0170653 +time/training (s) 19.4059 +time/epoch (s) 31.6286 +time/total (s) 16019.3 +Epoch -345 +------------------------------ ---------------- +2022-05-15 22:30:00.474193 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -344 finished +------------------------------ ---------------- +epoch -344 +replay_buffer/size 999047 +trainer/num train calls 657000 +trainer/QF1 Loss 0.980332 +trainer/QF2 Loss 0.882361 +trainer/Policy Loss 17.2998 +trainer/Q1 Predictions Mean -69.6171 +trainer/Q1 Predictions Std 21.0168 +trainer/Q1 Predictions Max 0.234873 +trainer/Q1 Predictions Min -85.4184 +trainer/Q2 Predictions Mean -69.5686 +trainer/Q2 Predictions Std 21.0035 +trainer/Q2 Predictions Max -1.12002 +trainer/Q2 Predictions Min -85.6667 +trainer/Q Targets Mean -69.5403 +trainer/Q Targets Std 21.0552 +trainer/Q Targets Max 0.0290847 +trainer/Q Targets Min -85.4109 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0215022 +trainer/policy/mean Std 0.731528 +trainer/policy/mean Max 0.998888 +trainer/policy/mean Min -0.998391 +trainer/policy/std Mean 0.415761 +trainer/policy/std Std 0.0213376 +trainer/policy/std Max 0.438555 +trainer/policy/std Min 0.380754 +trainer/Advantage Weights Mean 3.9551 +trainer/Advantage Weights Std 15.7217 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.4909e-16 +trainer/Advantage Score Mean -0.371276 +trainer/Advantage Score Std 0.645199 +trainer/Advantage Score Max 2.32445 +trainer/Advantage Score Min -3.55912 +trainer/V1 Predictions Mean -69.2618 +trainer/V1 Predictions Std 21.2014 +trainer/V1 Predictions Max 0.516664 +trainer/V1 Predictions Min -85.2811 +trainer/VF Loss 0.0934177 +expl/num steps total 657000 +expl/num paths total 857 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.132838 +expl/Actions Std 0.867413 +expl/Actions Max 2.45731 +expl/Actions Min -2.33601 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 610787 +eval/num paths total 663 +eval/path length Mean 639 +eval/path length Std 0 +eval/path length Max 639 +eval/path length Min 639 +eval/Rewards Mean 0.00156495 +eval/Rewards Std 0.0395284 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0121402 +eval/Actions Std 0.729601 +eval/Actions Max 0.999803 +eval/Actions Min -0.999676 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.47973e-05 +time/evaluation sampling (s) 4.93287 +time/exploration sampling (s) 7.19632 +time/logging (s) 0.0109717 +time/saving (s) 0.0190625 +time/training (s) 20.1373 +time/epoch (s) 32.2965 +time/total (s) 16051.6 +Epoch -344 +------------------------------ ---------------- +2022-05-15 22:30:32.391252 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -343 finished +------------------------------ ---------------- +epoch -343 +replay_buffer/size 999047 +trainer/num train calls 658000 +trainer/QF1 Loss 0.812028 +trainer/QF2 Loss 0.829093 +trainer/Policy Loss 20.1681 +trainer/Q1 Predictions Mean -70.4531 +trainer/Q1 Predictions Std 20.6589 +trainer/Q1 Predictions Max -0.507424 +trainer/Q1 Predictions Min -86.263 +trainer/Q2 Predictions Mean -70.4381 +trainer/Q2 Predictions Std 20.7726 +trainer/Q2 Predictions Max 0.434894 +trainer/Q2 Predictions Min -86.3538 +trainer/Q Targets Mean -70.5649 +trainer/Q Targets Std 20.7542 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.823 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0130521 +trainer/policy/mean Std 0.741717 +trainer/policy/mean Max 0.999661 +trainer/policy/mean Min -0.998874 +trainer/policy/std Mean 0.415194 +trainer/policy/std Std 0.0203668 +trainer/policy/std Max 0.439018 +trainer/policy/std Min 0.381138 +trainer/Advantage Weights Mean 5.894 +trainer/Advantage Weights Std 19.311 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.24699e-18 +trainer/Advantage Score Mean -0.35261 +trainer/Advantage Score Std 0.692863 +trainer/Advantage Score Max 1.15361 +trainer/Advantage Score Min -4.06369 +trainer/V1 Predictions Mean -70.3835 +trainer/V1 Predictions Std 20.7342 +trainer/V1 Predictions Max 0.751565 +trainer/V1 Predictions Min -86.7244 +trainer/VF Loss 0.0834414 +expl/num steps total 658000 +expl/num paths total 859 +expl/path length Mean 500 +expl/path length Std 51 +expl/path length Max 551 +expl/path length Min 449 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0223114 +expl/Actions Std 0.83703 +expl/Actions Max 2.31629 +expl/Actions Min -2.37718 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 611725 +eval/num paths total 664 +eval/path length Mean 938 +eval/path length Std 0 +eval/path length Max 938 +eval/path length Min 938 +eval/Rewards Mean 0.0010661 +eval/Rewards Std 0.0326337 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0279591 +eval/Actions Std 0.72533 +eval/Actions Max 0.999786 +eval/Actions Min -0.999398 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.88808e-06 +time/evaluation sampling (s) 5.62983 +time/exploration sampling (s) 6.76529 +time/logging (s) 0.0104981 +time/saving (s) 0.0163156 +time/training (s) 19.4755 +time/epoch (s) 31.8974 +time/total (s) 16083.5 +Epoch -343 +------------------------------ ---------------- +2022-05-15 22:31:03.405392 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -342 finished +------------------------------ ---------------- +epoch -342 +replay_buffer/size 999047 +trainer/num train calls 659000 +trainer/QF1 Loss 0.940642 +trainer/QF2 Loss 0.581427 +trainer/Policy Loss 4.09124 +trainer/Q1 Predictions Mean -71.5178 +trainer/Q1 Predictions Std 18.8052 +trainer/Q1 Predictions Max -3.78279 +trainer/Q1 Predictions Min -88.732 +trainer/Q2 Predictions Mean -71.5092 +trainer/Q2 Predictions Std 18.9454 +trainer/Q2 Predictions Max -3.61144 +trainer/Q2 Predictions Min -88.8478 +trainer/Q Targets Mean -71.2727 +trainer/Q Targets Std 19.052 +trainer/Q Targets Max -4.69195 +trainer/Q Targets Min -88.3583 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0289174 +trainer/policy/mean Std 0.717186 +trainer/policy/mean Max 0.997904 +trainer/policy/mean Min -0.999211 +trainer/policy/std Mean 0.414018 +trainer/policy/std Std 0.0210968 +trainer/policy/std Max 0.440353 +trainer/policy/std Min 0.379314 +trainer/Advantage Weights Mean 1.29561 +trainer/Advantage Weights Std 9.3801 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.16716e-22 +trainer/Advantage Score Mean -0.597659 +trainer/Advantage Score Std 0.685143 +trainer/Advantage Score Max 0.514885 +trainer/Advantage Score Min -4.90146 +trainer/V1 Predictions Mean -71.0031 +trainer/V1 Predictions Std 19.1451 +trainer/V1 Predictions Max -3.17058 +trainer/V1 Predictions Min -88.3071 +trainer/VF Loss 0.0855487 +expl/num steps total 659000 +expl/num paths total 860 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00277408 +expl/Actions Std 0.853901 +expl/Actions Max 2.30835 +expl/Actions Min -2.3149 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 612343 +eval/num paths total 665 +eval/path length Mean 618 +eval/path length Std 0 +eval/path length Max 618 +eval/path length Min 618 +eval/Rewards Mean 0.00161812 +eval/Rewards Std 0.0401933 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0361127 +eval/Actions Std 0.727698 +eval/Actions Max 0.99979 +eval/Actions Min -0.999847 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.21999e-05 +time/evaluation sampling (s) 5.50752 +time/exploration sampling (s) 6.1487 +time/logging (s) 0.00985043 +time/saving (s) 0.0158146 +time/training (s) 19.3146 +time/epoch (s) 30.9965 +time/total (s) 16114.5 +Epoch -342 +------------------------------ ---------------- +2022-05-15 22:31:35.185359 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -341 finished +------------------------------ ---------------- +epoch -341 +replay_buffer/size 999047 +trainer/num train calls 660000 +trainer/QF1 Loss 0.610699 +trainer/QF2 Loss 0.78036 +trainer/Policy Loss 13.8126 +trainer/Q1 Predictions Mean -72.5822 +trainer/Q1 Predictions Std 17.9698 +trainer/Q1 Predictions Max 0.712613 +trainer/Q1 Predictions Min -86.3533 +trainer/Q2 Predictions Mean -72.566 +trainer/Q2 Predictions Std 18.0724 +trainer/Q2 Predictions Max 0.53608 +trainer/Q2 Predictions Min -86.349 +trainer/Q Targets Mean -72.6774 +trainer/Q Targets Std 17.8334 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.171 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0107432 +trainer/policy/mean Std 0.723645 +trainer/policy/mean Max 0.999762 +trainer/policy/mean Min -0.998961 +trainer/policy/std Mean 0.413489 +trainer/policy/std Std 0.0200888 +trainer/policy/std Max 0.438227 +trainer/policy/std Min 0.379243 +trainer/Advantage Weights Mean 3.70786 +trainer/Advantage Weights Std 16.5178 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.20817e-18 +trainer/Advantage Score Mean -0.416875 +trainer/Advantage Score Std 0.574358 +trainer/Advantage Score Max 1.9944 +trainer/Advantage Score Min -4.02808 +trainer/V1 Predictions Mean -72.3383 +trainer/V1 Predictions Std 18.1781 +trainer/V1 Predictions Max 1.0871 +trainer/V1 Predictions Min -86.1952 +trainer/VF Loss 0.0781669 +expl/num steps total 660000 +expl/num paths total 861 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0661682 +expl/Actions Std 0.835515 +expl/Actions Max 2.20224 +expl/Actions Min -2.57547 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 613343 +eval/num paths total 666 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.104058 +eval/Actions Std 0.727734 +eval/Actions Max 0.999377 +eval/Actions Min -0.999486 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.12872e-05 +time/evaluation sampling (s) 5.24289 +time/exploration sampling (s) 6.80668 +time/logging (s) 0.0121476 +time/saving (s) 0.0180532 +time/training (s) 19.6875 +time/epoch (s) 31.7672 +time/total (s) 16146.3 +Epoch -341 +------------------------------ ---------------- +2022-05-15 22:32:07.653646 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -340 finished +------------------------------ ---------------- +epoch -340 +replay_buffer/size 999047 +trainer/num train calls 661000 +trainer/QF1 Loss 0.866921 +trainer/QF2 Loss 0.870189 +trainer/Policy Loss 20.8687 +trainer/Q1 Predictions Mean -70.8977 +trainer/Q1 Predictions Std 18.6675 +trainer/Q1 Predictions Max -0.645351 +trainer/Q1 Predictions Min -89.3355 +trainer/Q2 Predictions Mean -70.9173 +trainer/Q2 Predictions Std 18.7175 +trainer/Q2 Predictions Max -0.366616 +trainer/Q2 Predictions Min -89.417 +trainer/Q Targets Mean -71.1493 +trainer/Q Targets Std 18.5805 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.2143 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0154162 +trainer/policy/mean Std 0.726378 +trainer/policy/mean Max 0.998425 +trainer/policy/mean Min -0.998981 +trainer/policy/std Mean 0.412941 +trainer/policy/std Std 0.019515 +trainer/policy/std Max 0.437289 +trainer/policy/std Min 0.382749 +trainer/Advantage Weights Mean 4.44707 +trainer/Advantage Weights Std 17.2134 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.72716e-19 +trainer/Advantage Score Mean -0.453507 +trainer/Advantage Score Std 0.630947 +trainer/Advantage Score Max 1.57549 +trainer/Advantage Score Min -4.27459 +trainer/V1 Predictions Mean -70.8342 +trainer/V1 Predictions Std 18.7954 +trainer/V1 Predictions Max -0.349893 +trainer/V1 Predictions Min -89.7104 +trainer/VF Loss 0.0848214 +expl/num steps total 661000 +expl/num paths total 862 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0165517 +expl/Actions Std 0.876616 +expl/Actions Max 2.21284 +expl/Actions Min -2.32812 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 614059 +eval/num paths total 667 +eval/path length Mean 716 +eval/path length Std 0 +eval/path length Max 716 +eval/path length Min 716 +eval/Rewards Mean 0.00139665 +eval/Rewards Std 0.0373456 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.00341948 +eval/Actions Std 0.712008 +eval/Actions Max 0.999489 +eval/Actions Min -0.999158 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.09249e-05 +time/evaluation sampling (s) 5.32822 +time/exploration sampling (s) 6.58614 +time/logging (s) 0.0106129 +time/saving (s) 0.0154234 +time/training (s) 20.5071 +time/epoch (s) 32.4475 +time/total (s) 16178.7 +Epoch -340 +------------------------------ ---------------- +2022-05-15 22:32:39.262454 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -339 finished +------------------------------ ---------------- +epoch -339 +replay_buffer/size 999047 +trainer/num train calls 662000 +trainer/QF1 Loss 0.626798 +trainer/QF2 Loss 0.648428 +trainer/Policy Loss 18.7522 +trainer/Q1 Predictions Mean -72.6515 +trainer/Q1 Predictions Std 17.441 +trainer/Q1 Predictions Max -0.482203 +trainer/Q1 Predictions Min -86.7001 +trainer/Q2 Predictions Mean -72.675 +trainer/Q2 Predictions Std 17.5495 +trainer/Q2 Predictions Max 0.239399 +trainer/Q2 Predictions Min -86.3185 +trainer/Q Targets Mean -72.9502 +trainer/Q Targets Std 17.3019 +trainer/Q Targets Max -0.96681 +trainer/Q Targets Min -86.7249 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0318945 +trainer/policy/mean Std 0.724544 +trainer/policy/mean Max 0.999764 +trainer/policy/mean Min -0.999884 +trainer/policy/std Mean 0.412346 +trainer/policy/std Std 0.0207894 +trainer/policy/std Max 0.436522 +trainer/policy/std Min 0.377783 +trainer/Advantage Weights Mean 3.83551 +trainer/Advantage Weights Std 16.8661 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.70055e-19 +trainer/Advantage Score Mean -0.367457 +trainer/Advantage Score Std 0.554149 +trainer/Advantage Score Max 1.56823 +trainer/Advantage Score Min -4.27557 +trainer/V1 Predictions Mean -72.6902 +trainer/V1 Predictions Std 17.4559 +trainer/V1 Predictions Max -0.158119 +trainer/V1 Predictions Min -86.5944 +trainer/VF Loss 0.0647354 +expl/num steps total 662000 +expl/num paths total 864 +expl/path length Mean 500 +expl/path length Std 212 +expl/path length Max 712 +expl/path length Min 288 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.014184 +expl/Actions Std 0.822617 +expl/Actions Max 2.19246 +expl/Actions Min -2.22714 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 614608 +eval/num paths total 668 +eval/path length Mean 549 +eval/path length Std 0 +eval/path length Max 549 +eval/path length Min 549 +eval/Rewards Mean 0.00182149 +eval/Rewards Std 0.0426401 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0173436 +eval/Actions Std 0.73706 +eval/Actions Max 0.999854 +eval/Actions Min -0.999186 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.03288e-05 +time/evaluation sampling (s) 4.97405 +time/exploration sampling (s) 6.83088 +time/logging (s) 0.00704704 +time/saving (s) 0.0119521 +time/training (s) 19.7634 +time/epoch (s) 31.5874 +time/total (s) 16210.3 +Epoch -339 +------------------------------ ---------------- +2022-05-15 22:33:11.740933 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -338 finished +------------------------------ ---------------- +epoch -338 +replay_buffer/size 999047 +trainer/num train calls 663000 +trainer/QF1 Loss 0.585991 +trainer/QF2 Loss 0.676036 +trainer/Policy Loss 7.92109 +trainer/Q1 Predictions Mean -72.2245 +trainer/Q1 Predictions Std 18.1348 +trainer/Q1 Predictions Max 0.289262 +trainer/Q1 Predictions Min -86.6458 +trainer/Q2 Predictions Mean -72.275 +trainer/Q2 Predictions Std 18.0098 +trainer/Q2 Predictions Max -1.13936 +trainer/Q2 Predictions Min -86.6709 +trainer/Q Targets Mean -71.9893 +trainer/Q Targets Std 18.0666 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5025 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0117596 +trainer/policy/mean Std 0.719815 +trainer/policy/mean Max 0.999593 +trainer/policy/mean Min -0.999368 +trainer/policy/std Mean 0.413456 +trainer/policy/std Std 0.0199148 +trainer/policy/std Max 0.436623 +trainer/policy/std Min 0.382311 +trainer/Advantage Weights Mean 2.08901 +trainer/Advantage Weights Std 12.4393 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.28636e-17 +trainer/Advantage Score Mean -0.510756 +trainer/Advantage Score Std 0.602526 +trainer/Advantage Score Max 1.16726 +trainer/Advantage Score Min -3.73056 +trainer/V1 Predictions Mean -71.7457 +trainer/V1 Predictions Std 18.2066 +trainer/V1 Predictions Max -0.0600855 +trainer/V1 Predictions Min -86.4532 +trainer/VF Loss 0.0727444 +expl/num steps total 663000 +expl/num paths total 866 +expl/path length Mean 500 +expl/path length Std 426 +expl/path length Max 926 +expl/path length Min 74 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0421266 +expl/Actions Std 0.826939 +expl/Actions Max 2.19014 +expl/Actions Min -2.3053 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 615608 +eval/num paths total 669 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00123776 +eval/Actions Std 0.73642 +eval/Actions Max 0.999872 +eval/Actions Min -0.999298 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.54908e-06 +time/evaluation sampling (s) 5.49023 +time/exploration sampling (s) 6.79116 +time/logging (s) 0.0115974 +time/saving (s) 0.0155864 +time/training (s) 20.1641 +time/epoch (s) 32.4727 +time/total (s) 16242.8 +Epoch -338 +------------------------------ ---------------- +2022-05-15 22:33:42.658281 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -337 finished +------------------------------ ---------------- +epoch -337 +replay_buffer/size 999047 +trainer/num train calls 664000 +trainer/QF1 Loss 1.15421 +trainer/QF2 Loss 1.03172 +trainer/Policy Loss 9.46585 +trainer/Q1 Predictions Mean -72.5918 +trainer/Q1 Predictions Std 16.3045 +trainer/Q1 Predictions Max -1.31828 +trainer/Q1 Predictions Min -86.9387 +trainer/Q2 Predictions Mean -72.5941 +trainer/Q2 Predictions Std 16.2649 +trainer/Q2 Predictions Max -1.63142 +trainer/Q2 Predictions Min -87.0709 +trainer/Q Targets Mean -72.5056 +trainer/Q Targets Std 16.2206 +trainer/Q Targets Max -1.60702 +trainer/Q Targets Min -86.3032 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0199684 +trainer/policy/mean Std 0.724932 +trainer/policy/mean Max 0.99887 +trainer/policy/mean Min -0.999348 +trainer/policy/std Mean 0.414767 +trainer/policy/std Std 0.0191793 +trainer/policy/std Max 0.434148 +trainer/policy/std Min 0.385643 +trainer/Advantage Weights Mean 2.32142 +trainer/Advantage Weights Std 13.5212 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.50456e-38 +trainer/Advantage Score Mean -0.580531 +trainer/Advantage Score Std 0.792085 +trainer/Advantage Score Max 0.981968 +trainer/Advantage Score Min -8.57927 +trainer/V1 Predictions Mean -72.1756 +trainer/V1 Predictions Std 16.5447 +trainer/V1 Predictions Max -0.723544 +trainer/V1 Predictions Min -86.1823 +trainer/VF Loss 0.106762 +expl/num steps total 664000 +expl/num paths total 868 +expl/path length Mean 500 +expl/path length Std 327 +expl/path length Max 827 +expl/path length Min 173 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.021474 +expl/Actions Std 0.823425 +expl/Actions Max 2.417 +expl/Actions Min -2.43239 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 616327 +eval/num paths total 670 +eval/path length Mean 719 +eval/path length Std 0 +eval/path length Max 719 +eval/path length Min 719 +eval/Rewards Mean 0.00139082 +eval/Rewards Std 0.0372678 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00940854 +eval/Actions Std 0.726589 +eval/Actions Max 0.999777 +eval/Actions Min -0.999705 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.15451e-05 +time/evaluation sampling (s) 5.10091 +time/exploration sampling (s) 6.00209 +time/logging (s) 0.0122324 +time/saving (s) 0.0182798 +time/training (s) 19.7661 +time/epoch (s) 30.8997 +time/total (s) 16273.7 +Epoch -337 +------------------------------ ---------------- +2022-05-15 22:34:15.225532 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -336 finished +------------------------------ ---------------- +epoch -336 +replay_buffer/size 999047 +trainer/num train calls 665000 +trainer/QF1 Loss 1.21523 +trainer/QF2 Loss 0.928916 +trainer/Policy Loss 3.06756 +trainer/Q1 Predictions Mean -70.9627 +trainer/Q1 Predictions Std 17.9594 +trainer/Q1 Predictions Max -3.42727 +trainer/Q1 Predictions Min -86.2428 +trainer/Q2 Predictions Mean -70.9553 +trainer/Q2 Predictions Std 17.9277 +trainer/Q2 Predictions Max -3.28845 +trainer/Q2 Predictions Min -86.2214 +trainer/Q Targets Mean -70.5828 +trainer/Q Targets Std 18.0027 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.04 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00906092 +trainer/policy/mean Std 0.719894 +trainer/policy/mean Max 0.999921 +trainer/policy/mean Min -0.997986 +trainer/policy/std Mean 0.414897 +trainer/policy/std Std 0.0199769 +trainer/policy/std Max 0.437857 +trainer/policy/std Min 0.382496 +trainer/Advantage Weights Mean 0.98493 +trainer/Advantage Weights Std 8.89279 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.48947e-17 +trainer/Advantage Score Mean -0.679078 +trainer/Advantage Score Std 0.59847 +trainer/Advantage Score Max 0.599731 +trainer/Advantage Score Min -3.76422 +trainer/V1 Predictions Mean -70.2048 +trainer/V1 Predictions Std 18.2584 +trainer/V1 Predictions Max -2.51436 +trainer/V1 Predictions Min -86.1576 +trainer/VF Loss 0.0845514 +expl/num steps total 665000 +expl/num paths total 870 +expl/path length Mean 500 +expl/path length Std 162 +expl/path length Max 662 +expl/path length Min 338 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0307487 +expl/Actions Std 0.842299 +expl/Actions Max 2.54609 +expl/Actions Min -2.52027 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 617040 +eval/num paths total 671 +eval/path length Mean 713 +eval/path length Std 0 +eval/path length Max 713 +eval/path length Min 713 +eval/Rewards Mean 0.00140252 +eval/Rewards Std 0.037424 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0217338 +eval/Actions Std 0.734945 +eval/Actions Max 0.999934 +eval/Actions Min -0.999853 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2347e-05 +time/evaluation sampling (s) 5.75775 +time/exploration sampling (s) 7.34744 +time/logging (s) 0.0103851 +time/saving (s) 0.0155643 +time/training (s) 19.4143 +time/epoch (s) 32.5455 +time/total (s) 16306.3 +Epoch -336 +------------------------------ ---------------- +2022-05-15 22:34:46.924523 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -335 finished +------------------------------ ---------------- +epoch -335 +replay_buffer/size 999047 +trainer/num train calls 666000 +trainer/QF1 Loss 1.22418 +trainer/QF2 Loss 1.02003 +trainer/Policy Loss 14.9294 +trainer/Q1 Predictions Mean -70.9275 +trainer/Q1 Predictions Std 20.294 +trainer/Q1 Predictions Max -0.00177371 +trainer/Q1 Predictions Min -86.3744 +trainer/Q2 Predictions Mean -70.9546 +trainer/Q2 Predictions Std 20.1153 +trainer/Q2 Predictions Max -0.408256 +trainer/Q2 Predictions Min -85.9837 +trainer/Q Targets Mean -70.5721 +trainer/Q Targets Std 20.1236 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1745 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0116138 +trainer/policy/mean Std 0.723246 +trainer/policy/mean Max 0.999725 +trainer/policy/mean Min -0.99813 +trainer/policy/std Mean 0.413972 +trainer/policy/std Std 0.0202291 +trainer/policy/std Max 0.438029 +trainer/policy/std Min 0.382892 +trainer/Advantage Weights Mean 3.77371 +trainer/Advantage Weights Std 17.3966 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.08474e-16 +trainer/Advantage Score Mean -0.548998 +trainer/Advantage Score Std 0.596598 +trainer/Advantage Score Max 0.928691 +trainer/Advantage Score Min -3.676 +trainer/V1 Predictions Mean -70.2854 +trainer/V1 Predictions Std 20.3557 +trainer/V1 Predictions Max 0.415656 +trainer/V1 Predictions Min -86.5843 +trainer/VF Loss 0.0806543 +expl/num steps total 666000 +expl/num paths total 871 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0301299 +expl/Actions Std 0.821392 +expl/Actions Max 2.32783 +expl/Actions Min -2.21414 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 617480 +eval/num paths total 672 +eval/path length Mean 440 +eval/path length Std 0 +eval/path length Max 440 +eval/path length Min 440 +eval/Rewards Mean 0.00227273 +eval/Rewards Std 0.0476189 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00227472 +eval/Actions Std 0.756961 +eval/Actions Max 0.999374 +eval/Actions Min -0.998979 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2971e-05 +time/evaluation sampling (s) 4.66077 +time/exploration sampling (s) 6.51732 +time/logging (s) 0.00983337 +time/saving (s) 0.0177151 +time/training (s) 20.4768 +time/epoch (s) 31.6825 +time/total (s) 16338 +Epoch -335 +------------------------------ ---------------- +2022-05-15 22:35:18.822794 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -334 finished +------------------------------ ---------------- +epoch -334 +replay_buffer/size 999047 +trainer/num train calls 667000 +trainer/QF1 Loss 0.66351 +trainer/QF2 Loss 0.740619 +trainer/Policy Loss 25.4277 +trainer/Q1 Predictions Mean -70.6853 +trainer/Q1 Predictions Std 20.3474 +trainer/Q1 Predictions Max -0.951076 +trainer/Q1 Predictions Min -86.2879 +trainer/Q2 Predictions Mean -70.5291 +trainer/Q2 Predictions Std 20.3285 +trainer/Q2 Predictions Max -0.376452 +trainer/Q2 Predictions Min -86.2506 +trainer/Q Targets Mean -70.7532 +trainer/Q Targets Std 20.5184 +trainer/Q Targets Max -0.500695 +trainer/Q Targets Min -86.5469 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0126396 +trainer/policy/mean Std 0.72675 +trainer/policy/mean Max 0.998705 +trainer/policy/mean Min -0.99939 +trainer/policy/std Mean 0.412478 +trainer/policy/std Std 0.020159 +trainer/policy/std Max 0.436945 +trainer/policy/std Min 0.382547 +trainer/Advantage Weights Mean 5.53707 +trainer/Advantage Weights Std 21.1054 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.66118e-17 +trainer/Advantage Score Mean -0.377461 +trainer/Advantage Score Std 0.672514 +trainer/Advantage Score Max 2.30644 +trainer/Advantage Score Min -3.81652 +trainer/V1 Predictions Mean -70.4935 +trainer/V1 Predictions Std 20.6299 +trainer/V1 Predictions Max 0.234869 +trainer/V1 Predictions Min -86.2692 +trainer/VF Loss 0.0977019 +expl/num steps total 667000 +expl/num paths total 873 +expl/path length Mean 500 +expl/path length Std 306 +expl/path length Max 806 +expl/path length Min 194 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0412178 +expl/Actions Std 0.81969 +expl/Actions Max 2.2134 +expl/Actions Min -2.28505 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 618480 +eval/num paths total 673 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00301811 +eval/Actions Std 0.723259 +eval/Actions Max 0.99982 +eval/Actions Min -0.999778 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0618e-05 +time/evaluation sampling (s) 5.03195 +time/exploration sampling (s) 6.81186 +time/logging (s) 0.00900838 +time/saving (s) 0.0146626 +time/training (s) 20.0113 +time/epoch (s) 31.8788 +time/total (s) 16369.8 +Epoch -334 +------------------------------ ---------------- +2022-05-15 22:35:50.708456 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -333 finished +------------------------------ ---------------- +epoch -333 +replay_buffer/size 999047 +trainer/num train calls 668000 +trainer/QF1 Loss 1.23397 +trainer/QF2 Loss 1.09461 +trainer/Policy Loss 9.37379 +trainer/Q1 Predictions Mean -70.2254 +trainer/Q1 Predictions Std 18.9261 +trainer/Q1 Predictions Max 0.117182 +trainer/Q1 Predictions Min -85.7512 +trainer/Q2 Predictions Mean -70.2223 +trainer/Q2 Predictions Std 18.9132 +trainer/Q2 Predictions Max -0.376503 +trainer/Q2 Predictions Min -85.7281 +trainer/Q Targets Mean -70.4957 +trainer/Q Targets Std 19.079 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7245 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00786792 +trainer/policy/mean Std 0.724343 +trainer/policy/mean Max 0.9995 +trainer/policy/mean Min -0.999631 +trainer/policy/std Mean 0.4154 +trainer/policy/std Std 0.0201443 +trainer/policy/std Max 0.434936 +trainer/policy/std Min 0.383118 +trainer/Advantage Weights Mean 2.47401 +trainer/Advantage Weights Std 10.9665 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.90759e-13 +trainer/Advantage Score Mean -0.399794 +trainer/Advantage Score Std 0.573983 +trainer/Advantage Score Max 1.02192 +trainer/Advantage Score Min -2.76403 +trainer/V1 Predictions Mean -70.2746 +trainer/V1 Predictions Std 19.1899 +trainer/V1 Predictions Max 0.0226431 +trainer/V1 Predictions Min -85.5579 +trainer/VF Loss 0.0580585 +expl/num steps total 668000 +expl/num paths total 875 +expl/path length Mean 500 +expl/path length Std 138 +expl/path length Max 638 +expl/path length Min 362 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0305203 +expl/Actions Std 0.834026 +expl/Actions Max 2.61398 +expl/Actions Min -2.22435 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 619480 +eval/num paths total 674 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.156364 +eval/Actions Std 0.70408 +eval/Actions Max 0.999958 +eval/Actions Min -0.999695 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.48366e-06 +time/evaluation sampling (s) 5.16388 +time/exploration sampling (s) 6.6632 +time/logging (s) 0.0120614 +time/saving (s) 0.0166658 +time/training (s) 20.0192 +time/epoch (s) 31.875 +time/total (s) 16401.7 +Epoch -333 +------------------------------ ---------------- +2022-05-15 22:36:22.569396 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -332 finished +------------------------------ ---------------- +epoch -332 +replay_buffer/size 999047 +trainer/num train calls 669000 +trainer/QF1 Loss 1.3431 +trainer/QF2 Loss 1.44944 +trainer/Policy Loss 17.3206 +trainer/Q1 Predictions Mean -72.2632 +trainer/Q1 Predictions Std 17.0513 +trainer/Q1 Predictions Max -1.01962 +trainer/Q1 Predictions Min -89.4091 +trainer/Q2 Predictions Mean -72.3314 +trainer/Q2 Predictions Std 16.9895 +trainer/Q2 Predictions Max -1.17828 +trainer/Q2 Predictions Min -88.9646 +trainer/Q Targets Mean -72.2805 +trainer/Q Targets Std 17.3329 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.0821 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.035094 +trainer/policy/mean Std 0.725051 +trainer/policy/mean Max 0.998849 +trainer/policy/mean Min -0.999087 +trainer/policy/std Mean 0.414487 +trainer/policy/std Std 0.0205336 +trainer/policy/std Max 0.437363 +trainer/policy/std Min 0.38255 +trainer/Advantage Weights Mean 4.48312 +trainer/Advantage Weights Std 16.0807 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.29286e-15 +trainer/Advantage Score Mean -0.365551 +trainer/Advantage Score Std 0.675511 +trainer/Advantage Score Max 1.07898 +trainer/Advantage Score Min -3.42819 +trainer/V1 Predictions Mean -71.972 +trainer/V1 Predictions Std 17.4143 +trainer/V1 Predictions Max -0.140207 +trainer/V1 Predictions Min -88.9343 +trainer/VF Loss 0.0756842 +expl/num steps total 669000 +expl/num paths total 877 +expl/path length Mean 500 +expl/path length Std 85 +expl/path length Max 585 +expl/path length Min 415 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00499801 +expl/Actions Std 0.836014 +expl/Actions Max 2.47057 +expl/Actions Min -2.56117 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 620186 +eval/num paths total 675 +eval/path length Mean 706 +eval/path length Std 0 +eval/path length Max 706 +eval/path length Min 706 +eval/Rewards Mean 0.00141643 +eval/Rewards Std 0.0376088 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0242039 +eval/Actions Std 0.740437 +eval/Actions Max 0.999424 +eval/Actions Min -0.999738 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2185e-05 +time/evaluation sampling (s) 5.45829 +time/exploration sampling (s) 7.09815 +time/logging (s) 0.0109943 +time/saving (s) 0.0157809 +time/training (s) 19.2584 +time/epoch (s) 31.8417 +time/total (s) 16433.6 +Epoch -332 +------------------------------ ---------------- +2022-05-15 22:36:54.537395 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -331 finished +------------------------------ ---------------- +epoch -331 +replay_buffer/size 999047 +trainer/num train calls 670000 +trainer/QF1 Loss 0.732824 +trainer/QF2 Loss 0.72412 +trainer/Policy Loss 11.4944 +trainer/Q1 Predictions Mean -71.8252 +trainer/Q1 Predictions Std 19.4816 +trainer/Q1 Predictions Max -1.17761 +trainer/Q1 Predictions Min -88.6056 +trainer/Q2 Predictions Mean -71.8136 +trainer/Q2 Predictions Std 19.4687 +trainer/Q2 Predictions Max -1.55765 +trainer/Q2 Predictions Min -88.3393 +trainer/Q Targets Mean -72.0232 +trainer/Q Targets Std 19.7455 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9939 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0219125 +trainer/policy/mean Std 0.721732 +trainer/policy/mean Max 0.998396 +trainer/policy/mean Min -0.999 +trainer/policy/std Mean 0.413888 +trainer/policy/std Std 0.0202976 +trainer/policy/std Max 0.434659 +trainer/policy/std Min 0.380515 +trainer/Advantage Weights Mean 2.42785 +trainer/Advantage Weights Std 9.89973 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.1394e-14 +trainer/Advantage Score Mean -0.397471 +trainer/Advantage Score Std 0.576302 +trainer/Advantage Score Max 0.62912 +trainer/Advantage Score Min -3.05993 +trainer/V1 Predictions Mean -71.741 +trainer/V1 Predictions Std 19.8554 +trainer/V1 Predictions Max 1.15492 +trainer/V1 Predictions Min -88.4541 +trainer/VF Loss 0.0561121 +expl/num steps total 670000 +expl/num paths total 878 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00606942 +expl/Actions Std 0.825271 +expl/Actions Max 2.1928 +expl/Actions Min -2.38404 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 621186 +eval/num paths total 676 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0717888 +eval/Actions Std 0.772705 +eval/Actions Max 0.999906 +eval/Actions Min -0.999596 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.16569e-05 +time/evaluation sampling (s) 5.92042 +time/exploration sampling (s) 6.4574 +time/logging (s) 0.0123685 +time/saving (s) 0.0192298 +time/training (s) 19.546 +time/epoch (s) 31.9555 +time/total (s) 16465.5 +Epoch -331 +------------------------------ ---------------- +2022-05-15 22:37:26.862728 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -330 finished +------------------------------ ---------------- +epoch -330 +replay_buffer/size 999047 +trainer/num train calls 671000 +trainer/QF1 Loss 0.78045 +trainer/QF2 Loss 0.79646 +trainer/Policy Loss 6.93731 +trainer/Q1 Predictions Mean -70.9267 +trainer/Q1 Predictions Std 18.513 +trainer/Q1 Predictions Max -0.663878 +trainer/Q1 Predictions Min -85.7697 +trainer/Q2 Predictions Mean -70.9516 +trainer/Q2 Predictions Std 18.5894 +trainer/Q2 Predictions Max -0.912739 +trainer/Q2 Predictions Min -85.9932 +trainer/Q Targets Mean -70.6435 +trainer/Q Targets Std 18.649 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9239 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00681328 +trainer/policy/mean Std 0.734683 +trainer/policy/mean Max 0.999901 +trainer/policy/mean Min -0.997502 +trainer/policy/std Mean 0.415017 +trainer/policy/std Std 0.0207656 +trainer/policy/std Max 0.434922 +trainer/policy/std Min 0.380728 +trainer/Advantage Weights Mean 2.89362 +trainer/Advantage Weights Std 15.8258 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.61876e-21 +trainer/Advantage Score Mean -0.671823 +trainer/Advantage Score Std 0.742112 +trainer/Advantage Score Max 1.33979 +trainer/Advantage Score Min -4.73916 +trainer/V1 Predictions Mean -70.3533 +trainer/V1 Predictions Std 18.8128 +trainer/V1 Predictions Max -0.956597 +trainer/V1 Predictions Min -85.7015 +trainer/VF Loss 0.118014 +expl/num steps total 671000 +expl/num paths total 880 +expl/path length Mean 500 +expl/path length Std 292 +expl/path length Max 792 +expl/path length Min 208 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0465339 +expl/Actions Std 0.82172 +expl/Actions Max 2.40881 +expl/Actions Min -2.4173 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 622186 +eval/num paths total 677 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.176629 +eval/Actions Std 0.627714 +eval/Actions Max 0.998882 +eval/Actions Min -0.999452 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.42494e-06 +time/evaluation sampling (s) 5.34668 +time/exploration sampling (s) 6.97087 +time/logging (s) 0.0110761 +time/saving (s) 0.0159185 +time/training (s) 19.9604 +time/epoch (s) 32.3049 +time/total (s) 16497.8 +Epoch -330 +------------------------------ ---------------- +2022-05-15 22:37:58.939573 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -329 finished +------------------------------ ---------------- +epoch -329 +replay_buffer/size 999047 +trainer/num train calls 672000 +trainer/QF1 Loss 0.77778 +trainer/QF2 Loss 0.655974 +trainer/Policy Loss 46.7806 +trainer/Q1 Predictions Mean -69.945 +trainer/Q1 Predictions Std 18.7857 +trainer/Q1 Predictions Max -0.612851 +trainer/Q1 Predictions Min -86.4668 +trainer/Q2 Predictions Mean -70.0474 +trainer/Q2 Predictions Std 18.7339 +trainer/Q2 Predictions Max -0.835043 +trainer/Q2 Predictions Min -86.555 +trainer/Q Targets Mean -70.4155 +trainer/Q Targets Std 18.8696 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6252 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00912639 +trainer/policy/mean Std 0.725916 +trainer/policy/mean Max 0.99944 +trainer/policy/mean Min -0.999874 +trainer/policy/std Mean 0.415859 +trainer/policy/std Std 0.020941 +trainer/policy/std Max 0.437731 +trainer/policy/std Min 0.385051 +trainer/Advantage Weights Mean 10.1253 +trainer/Advantage Weights Std 27.1975 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.25605e-23 +trainer/Advantage Score Mean -0.2732 +trainer/Advantage Score Std 0.743532 +trainer/Advantage Score Max 1.53464 +trainer/Advantage Score Min -5.21458 +trainer/V1 Predictions Mean -70.1884 +trainer/V1 Predictions Std 18.9731 +trainer/V1 Predictions Max -0.299808 +trainer/V1 Predictions Min -86.3774 +trainer/VF Loss 0.116359 +expl/num steps total 672000 +expl/num paths total 882 +expl/path length Mean 500 +expl/path length Std 363 +expl/path length Max 863 +expl/path length Min 137 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0456471 +expl/Actions Std 0.826291 +expl/Actions Max 2.31781 +expl/Actions Min -2.30534 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 623186 +eval/num paths total 678 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.025148 +eval/Actions Std 0.792884 +eval/Actions Max 0.999043 +eval/Actions Min -0.999322 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.29002e-05 +time/evaluation sampling (s) 5.26162 +time/exploration sampling (s) 6.96571 +time/logging (s) 0.0123648 +time/saving (s) 0.0168455 +time/training (s) 19.8036 +time/epoch (s) 32.0601 +time/total (s) 16529.9 +Epoch -329 +------------------------------ ---------------- +2022-05-15 22:38:30.529628 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -328 finished +------------------------------ ---------------- +epoch -328 +replay_buffer/size 999047 +trainer/num train calls 673000 +trainer/QF1 Loss 0.750632 +trainer/QF2 Loss 0.699441 +trainer/Policy Loss 6.22667 +trainer/Q1 Predictions Mean -71.9513 +trainer/Q1 Predictions Std 17.1797 +trainer/Q1 Predictions Max -1.07193 +trainer/Q1 Predictions Min -85.8367 +trainer/Q2 Predictions Mean -71.8695 +trainer/Q2 Predictions Std 17.3044 +trainer/Q2 Predictions Max -0.623913 +trainer/Q2 Predictions Min -86.8173 +trainer/Q Targets Mean -71.7868 +trainer/Q Targets Std 17.1868 +trainer/Q Targets Max -1.82505 +trainer/Q Targets Min -86.0897 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0310365 +trainer/policy/mean Std 0.724179 +trainer/policy/mean Max 0.99984 +trainer/policy/mean Min -0.998492 +trainer/policy/std Mean 0.413865 +trainer/policy/std Std 0.0208004 +trainer/policy/std Max 0.43644 +trainer/policy/std Min 0.383031 +trainer/Advantage Weights Mean 2.10264 +trainer/Advantage Weights Std 11.8714 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.00457e-13 +trainer/Advantage Score Mean -0.501465 +trainer/Advantage Score Std 0.577122 +trainer/Advantage Score Max 1.27187 +trainer/Advantage Score Min -2.9929 +trainer/V1 Predictions Mean -71.5141 +trainer/V1 Predictions Std 17.4056 +trainer/V1 Predictions Max -0.0563887 +trainer/V1 Predictions Min -86.002 +trainer/VF Loss 0.0704421 +expl/num steps total 673000 +expl/num paths total 884 +expl/path length Mean 500 +expl/path length Std 273 +expl/path length Max 773 +expl/path length Min 227 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0155673 +expl/Actions Std 0.819017 +expl/Actions Max 2.84362 +expl/Actions Min -2.61199 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 624186 +eval/num paths total 679 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0501401 +eval/Actions Std 0.746144 +eval/Actions Max 0.999927 +eval/Actions Min -0.999817 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.22241e-05 +time/evaluation sampling (s) 5.21067 +time/exploration sampling (s) 6.20845 +time/logging (s) 0.0110427 +time/saving (s) 0.0164899 +time/training (s) 20.1227 +time/epoch (s) 31.5694 +time/total (s) 16561.5 +Epoch -328 +------------------------------ ---------------- +2022-05-15 22:39:03.115475 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -327 finished +------------------------------ ---------------- +epoch -327 +replay_buffer/size 999047 +trainer/num train calls 674000 +trainer/QF1 Loss 0.743648 +trainer/QF2 Loss 0.67147 +trainer/Policy Loss 7.47839 +trainer/Q1 Predictions Mean -69.8924 +trainer/Q1 Predictions Std 20.3225 +trainer/Q1 Predictions Max 0.154415 +trainer/Q1 Predictions Min -86.1244 +trainer/Q2 Predictions Mean -69.7948 +trainer/Q2 Predictions Std 20.3504 +trainer/Q2 Predictions Max -0.370573 +trainer/Q2 Predictions Min -85.9299 +trainer/Q Targets Mean -69.9027 +trainer/Q Targets Std 20.334 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0739 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00422463 +trainer/policy/mean Std 0.71862 +trainer/policy/mean Max 0.999121 +trainer/policy/mean Min -0.997543 +trainer/policy/std Mean 0.413518 +trainer/policy/std Std 0.0202683 +trainer/policy/std Max 0.435075 +trainer/policy/std Min 0.382912 +trainer/Advantage Weights Mean 2.36848 +trainer/Advantage Weights Std 12.7792 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.54317e-15 +trainer/Advantage Score Mean -0.422726 +trainer/Advantage Score Std 0.570079 +trainer/Advantage Score Max 1.06625 +trainer/Advantage Score Min -3.32738 +trainer/V1 Predictions Mean -69.6556 +trainer/V1 Predictions Std 20.4659 +trainer/V1 Predictions Max 0.915615 +trainer/V1 Predictions Min -85.8915 +trainer/VF Loss 0.0613381 +expl/num steps total 674000 +expl/num paths total 886 +expl/path length Mean 500 +expl/path length Std 60 +expl/path length Max 560 +expl/path length Min 440 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0169827 +expl/Actions Std 0.828195 +expl/Actions Max 2.57451 +expl/Actions Min -2.45012 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 625186 +eval/num paths total 680 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.12681 +eval/Actions Std 0.646357 +eval/Actions Max 0.999569 +eval/Actions Min -0.99965 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.27358e-05 +time/evaluation sampling (s) 5.77718 +time/exploration sampling (s) 6.75985 +time/logging (s) 0.0124348 +time/saving (s) 0.0163717 +time/training (s) 20.0058 +time/epoch (s) 32.5717 +time/total (s) 16594.1 +Epoch -327 +------------------------------ ---------------- +2022-05-15 22:39:35.056539 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -326 finished +------------------------------ ---------------- +epoch -326 +replay_buffer/size 999047 +trainer/num train calls 675000 +trainer/QF1 Loss 1.52307 +trainer/QF2 Loss 1.36611 +trainer/Policy Loss 28.8347 +trainer/Q1 Predictions Mean -71.9822 +trainer/Q1 Predictions Std 16.6994 +trainer/Q1 Predictions Max -1.50492 +trainer/Q1 Predictions Min -85.3147 +trainer/Q2 Predictions Mean -71.9991 +trainer/Q2 Predictions Std 16.6642 +trainer/Q2 Predictions Max -0.716137 +trainer/Q2 Predictions Min -85.2278 +trainer/Q Targets Mean -72.6035 +trainer/Q Targets Std 16.571 +trainer/Q Targets Max -3.52256 +trainer/Q Targets Min -85.621 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0290953 +trainer/policy/mean Std 0.720803 +trainer/policy/mean Max 0.998108 +trainer/policy/mean Min -0.999739 +trainer/policy/std Mean 0.414051 +trainer/policy/std Std 0.0201239 +trainer/policy/std Max 0.436049 +trainer/policy/std Min 0.38351 +trainer/Advantage Weights Mean 7.1605 +trainer/Advantage Weights Std 19.9201 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.76133e-15 +trainer/Advantage Score Mean -0.158177 +trainer/Advantage Score Std 0.595019 +trainer/Advantage Score Max 2.50025 +trainer/Advantage Score Min -3.29782 +trainer/V1 Predictions Mean -72.2809 +trainer/V1 Predictions Std 16.8654 +trainer/V1 Predictions Max -1.48767 +trainer/V1 Predictions Min -85.5018 +trainer/VF Loss 0.0904399 +expl/num steps total 675000 +expl/num paths total 888 +expl/path length Mean 500 +expl/path length Std 197 +expl/path length Max 697 +expl/path length Min 303 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0180945 +expl/Actions Std 0.815945 +expl/Actions Max 2.2819 +expl/Actions Min -2.4515 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 626186 +eval/num paths total 681 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0906604 +eval/Actions Std 0.624032 +eval/Actions Max 0.99897 +eval/Actions Min -0.999601 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.18571e-05 +time/evaluation sampling (s) 4.9476 +time/exploration sampling (s) 6.52302 +time/logging (s) 0.0096089 +time/saving (s) 0.0131045 +time/training (s) 20.4304 +time/epoch (s) 31.9238 +time/total (s) 16626 +Epoch -326 +------------------------------ ---------------- +2022-05-15 22:40:06.384608 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -325 finished +------------------------------ ---------------- +epoch -325 +replay_buffer/size 999047 +trainer/num train calls 676000 +trainer/QF1 Loss 3.56502 +trainer/QF2 Loss 3.47168 +trainer/Policy Loss 17.3953 +trainer/Q1 Predictions Mean -72.8923 +trainer/Q1 Predictions Std 17.2806 +trainer/Q1 Predictions Max 0.0519621 +trainer/Q1 Predictions Min -86.2092 +trainer/Q2 Predictions Mean -72.8995 +trainer/Q2 Predictions Std 17.3261 +trainer/Q2 Predictions Max -0.523452 +trainer/Q2 Predictions Min -86.5973 +trainer/Q Targets Mean -72.7289 +trainer/Q Targets Std 17.3335 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9109 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0138496 +trainer/policy/mean Std 0.720864 +trainer/policy/mean Max 0.999621 +trainer/policy/mean Min -0.997796 +trainer/policy/std Mean 0.414515 +trainer/policy/std Std 0.0201436 +trainer/policy/std Max 0.435593 +trainer/policy/std Min 0.382912 +trainer/Advantage Weights Mean 4.82719 +trainer/Advantage Weights Std 17.8257 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.04158e-19 +trainer/Advantage Score Mean -0.368314 +trainer/Advantage Score Std 0.673117 +trainer/Advantage Score Max 0.781143 +trainer/Advantage Score Min -4.37084 +trainer/V1 Predictions Mean -72.5025 +trainer/V1 Predictions Std 17.6543 +trainer/V1 Predictions Max 1.12058 +trainer/V1 Predictions Min -86.174 +trainer/VF Loss 0.0719789 +expl/num steps total 676000 +expl/num paths total 889 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.155529 +expl/Actions Std 0.814152 +expl/Actions Max 2.49825 +expl/Actions Min -2.23392 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 627186 +eval/num paths total 682 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0470004 +eval/Actions Std 0.720461 +eval/Actions Max 0.999696 +eval/Actions Min -0.999819 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02818e-05 +time/evaluation sampling (s) 4.96339 +time/exploration sampling (s) 6.30152 +time/logging (s) 0.00955978 +time/saving (s) 0.012261 +time/training (s) 20.0292 +time/epoch (s) 31.3159 +time/total (s) 16657.3 +Epoch -325 +------------------------------ ---------------- +2022-05-15 22:40:38.379709 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -324 finished +------------------------------ ---------------- +epoch -324 +replay_buffer/size 999047 +trainer/num train calls 677000 +trainer/QF1 Loss 0.541218 +trainer/QF2 Loss 0.563199 +trainer/Policy Loss 28.3228 +trainer/Q1 Predictions Mean -73.0517 +trainer/Q1 Predictions Std 17.3238 +trainer/Q1 Predictions Max -1.4794 +trainer/Q1 Predictions Min -86.8227 +trainer/Q2 Predictions Mean -73.0915 +trainer/Q2 Predictions Std 17.3405 +trainer/Q2 Predictions Max -0.45826 +trainer/Q2 Predictions Min -86.7465 +trainer/Q Targets Mean -72.8809 +trainer/Q Targets Std 17.3496 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3509 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0213454 +trainer/policy/mean Std 0.727251 +trainer/policy/mean Max 0.999646 +trainer/policy/mean Min -0.999389 +trainer/policy/std Mean 0.414046 +trainer/policy/std Std 0.0201593 +trainer/policy/std Max 0.435868 +trainer/policy/std Min 0.383273 +trainer/Advantage Weights Mean 5.90564 +trainer/Advantage Weights Std 21.3881 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.72746e-16 +trainer/Advantage Score Mean -0.380733 +trainer/Advantage Score Std 0.641306 +trainer/Advantage Score Max 0.812393 +trainer/Advantage Score Min -3.62947 +trainer/V1 Predictions Mean -72.6826 +trainer/V1 Predictions Std 17.3917 +trainer/V1 Predictions Max -0.396395 +trainer/V1 Predictions Min -86.6211 +trainer/VF Loss 0.0717835 +expl/num steps total 677000 +expl/num paths total 891 +expl/path length Mean 500 +expl/path length Std 48 +expl/path length Max 548 +expl/path length Min 452 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0227385 +expl/Actions Std 0.827272 +expl/Actions Max 2.17482 +expl/Actions Min -2.17848 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 628186 +eval/num paths total 683 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0537993 +eval/Actions Std 0.707921 +eval/Actions Max 0.999627 +eval/Actions Min -0.999931 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.19167e-05 +time/evaluation sampling (s) 4.82448 +time/exploration sampling (s) 7.57343 +time/logging (s) 0.0127558 +time/saving (s) 0.0187985 +time/training (s) 19.5483 +time/epoch (s) 31.9778 +time/total (s) 16689.3 +Epoch -324 +------------------------------ ---------------- +2022-05-15 22:41:10.839689 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -323 finished +------------------------------ ---------------- +epoch -323 +replay_buffer/size 999047 +trainer/num train calls 678000 +trainer/QF1 Loss 0.724378 +trainer/QF2 Loss 0.591225 +trainer/Policy Loss 23.5111 +trainer/Q1 Predictions Mean -71.9149 +trainer/Q1 Predictions Std 17.884 +trainer/Q1 Predictions Max -0.931809 +trainer/Q1 Predictions Min -86.9528 +trainer/Q2 Predictions Mean -71.8658 +trainer/Q2 Predictions Std 17.9523 +trainer/Q2 Predictions Max -1.04106 +trainer/Q2 Predictions Min -87.041 +trainer/Q Targets Mean -71.8551 +trainer/Q Targets Std 17.9677 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6893 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0055223 +trainer/policy/mean Std 0.742596 +trainer/policy/mean Max 0.999181 +trainer/policy/mean Min -0.998575 +trainer/policy/std Mean 0.413346 +trainer/policy/std Std 0.0199907 +trainer/policy/std Max 0.434655 +trainer/policy/std Min 0.383139 +trainer/Advantage Weights Mean 5.52233 +trainer/Advantage Weights Std 20.2149 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.55821e-19 +trainer/Advantage Score Mean -0.363426 +trainer/Advantage Score Std 0.624681 +trainer/Advantage Score Max 2.07468 +trainer/Advantage Score Min -4.28098 +trainer/V1 Predictions Mean -71.5857 +trainer/V1 Predictions Std 18.1158 +trainer/V1 Predictions Max 0.590175 +trainer/V1 Predictions Min -86.5551 +trainer/VF Loss 0.0979887 +expl/num steps total 678000 +expl/num paths total 893 +expl/path length Mean 500 +expl/path length Std 198 +expl/path length Max 698 +expl/path length Min 302 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0606474 +expl/Actions Std 0.826058 +expl/Actions Max 2.60463 +expl/Actions Min -2.19332 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 628770 +eval/num paths total 684 +eval/path length Mean 584 +eval/path length Std 0 +eval/path length Max 584 +eval/path length Min 584 +eval/Rewards Mean 0.00171233 +eval/Rewards Std 0.0413449 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0150132 +eval/Actions Std 0.737707 +eval/Actions Max 0.999837 +eval/Actions Min -0.999743 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.00238e-05 +time/evaluation sampling (s) 5.21793 +time/exploration sampling (s) 6.90473 +time/logging (s) 0.00998005 +time/saving (s) 0.0149297 +time/training (s) 20.2902 +time/epoch (s) 32.4378 +time/total (s) 16721.7 +Epoch -323 +------------------------------ ---------------- +2022-05-15 22:41:42.554375 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -322 finished +------------------------------ ---------------- +epoch -322 +replay_buffer/size 999047 +trainer/num train calls 679000 +trainer/QF1 Loss 0.8998 +trainer/QF2 Loss 1.03291 +trainer/Policy Loss 5.09833 +trainer/Q1 Predictions Mean -71.0179 +trainer/Q1 Predictions Std 19.1519 +trainer/Q1 Predictions Max 0.142493 +trainer/Q1 Predictions Min -85.6463 +trainer/Q2 Predictions Mean -71.0418 +trainer/Q2 Predictions Std 19.1234 +trainer/Q2 Predictions Max 0.293896 +trainer/Q2 Predictions Min -85.7127 +trainer/Q Targets Mean -70.7603 +trainer/Q Targets Std 19.3518 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.579 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0186176 +trainer/policy/mean Std 0.730056 +trainer/policy/mean Max 0.997285 +trainer/policy/mean Min -0.999079 +trainer/policy/std Mean 0.41411 +trainer/policy/std Std 0.0206411 +trainer/policy/std Max 0.435725 +trainer/policy/std Min 0.381713 +trainer/Advantage Weights Mean 0.729024 +trainer/Advantage Weights Std 6.32003 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.4178e-16 +trainer/Advantage Score Mean -0.585866 +trainer/Advantage Score Std 0.655759 +trainer/Advantage Score Max 0.629944 +trainer/Advantage Score Min -3.53557 +trainer/V1 Predictions Mean -70.4674 +trainer/V1 Predictions Std 19.4802 +trainer/V1 Predictions Max 0.753058 +trainer/V1 Predictions Min -85.4733 +trainer/VF Loss 0.0793611 +expl/num steps total 679000 +expl/num paths total 895 +expl/path length Mean 500 +expl/path length Std 330 +expl/path length Max 830 +expl/path length Min 170 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0551361 +expl/Actions Std 0.828239 +expl/Actions Max 2.30429 +expl/Actions Min -2.22276 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 629770 +eval/num paths total 685 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.15727 +eval/Actions Std 0.735386 +eval/Actions Max 0.999453 +eval/Actions Min -0.999555 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 6.40796e-06 +time/evaluation sampling (s) 5.47434 +time/exploration sampling (s) 6.8618 +time/logging (s) 0.0118269 +time/saving (s) 0.0179458 +time/training (s) 19.3361 +time/epoch (s) 31.702 +time/total (s) 16753.5 +Epoch -322 +------------------------------ ---------------- +2022-05-15 22:42:15.130594 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -321 finished +------------------------------ ---------------- +epoch -321 +replay_buffer/size 999047 +trainer/num train calls 680000 +trainer/QF1 Loss 0.830198 +trainer/QF2 Loss 0.91665 +trainer/Policy Loss 16.3006 +trainer/Q1 Predictions Mean -70.1336 +trainer/Q1 Predictions Std 20.2452 +trainer/Q1 Predictions Max -0.431262 +trainer/Q1 Predictions Min -86.2197 +trainer/Q2 Predictions Mean -70.136 +trainer/Q2 Predictions Std 20.2174 +trainer/Q2 Predictions Max 0.00638667 +trainer/Q2 Predictions Min -85.9635 +trainer/Q Targets Mean -70.2024 +trainer/Q Targets Std 20.0388 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7709 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0157171 +trainer/policy/mean Std 0.74336 +trainer/policy/mean Max 0.999297 +trainer/policy/mean Min -0.999101 +trainer/policy/std Mean 0.41356 +trainer/policy/std Std 0.0188529 +trainer/policy/std Max 0.433035 +trainer/policy/std Min 0.383491 +trainer/Advantage Weights Mean 4.80784 +trainer/Advantage Weights Std 19.8329 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.16563e-18 +trainer/Advantage Score Mean -0.482553 +trainer/Advantage Score Std 0.751769 +trainer/Advantage Score Max 1.78157 +trainer/Advantage Score Min -3.92311 +trainer/V1 Predictions Mean -69.8502 +trainer/V1 Predictions Std 20.3136 +trainer/V1 Predictions Max -1.3758 +trainer/V1 Predictions Min -86.021 +trainer/VF Loss 0.109294 +expl/num steps total 680000 +expl/num paths total 896 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.186642 +expl/Actions Std 0.871353 +expl/Actions Max 2.21917 +expl/Actions Min -2.71472 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 630770 +eval/num paths total 686 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.166902 +eval/Actions Std 0.808474 +eval/Actions Max 0.999975 +eval/Actions Min -0.999986 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.20462e-05 +time/evaluation sampling (s) 5.506 +time/exploration sampling (s) 7.23489 +time/logging (s) 0.00830067 +time/saving (s) 0.0187069 +time/training (s) 19.7849 +time/epoch (s) 32.5528 +time/total (s) 16786 +Epoch -321 +------------------------------ ---------------- +2022-05-15 22:42:47.371055 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -320 finished +------------------------------ ---------------- +epoch -320 +replay_buffer/size 999047 +trainer/num train calls 681000 +trainer/QF1 Loss 0.58304 +trainer/QF2 Loss 0.54892 +trainer/Policy Loss 10.3937 +trainer/Q1 Predictions Mean -72.3018 +trainer/Q1 Predictions Std 17.3321 +trainer/Q1 Predictions Max 0.739394 +trainer/Q1 Predictions Min -85.7774 +trainer/Q2 Predictions Mean -72.2508 +trainer/Q2 Predictions Std 17.2627 +trainer/Q2 Predictions Max -0.371005 +trainer/Q2 Predictions Min -86.0109 +trainer/Q Targets Mean -72.2528 +trainer/Q Targets Std 17.4744 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.6525 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0221958 +trainer/policy/mean Std 0.72598 +trainer/policy/mean Max 0.99867 +trainer/policy/mean Min -0.999879 +trainer/policy/std Mean 0.413734 +trainer/policy/std Std 0.0198315 +trainer/policy/std Max 0.434214 +trainer/policy/std Min 0.382535 +trainer/Advantage Weights Mean 1.22299 +trainer/Advantage Weights Std 8.48549 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.23746e-33 +trainer/Advantage Score Mean -0.619933 +trainer/Advantage Score Std 0.717612 +trainer/Advantage Score Max 1.13424 +trainer/Advantage Score Min -7.57722 +trainer/V1 Predictions Mean -71.96 +trainer/V1 Predictions Std 17.6682 +trainer/V1 Predictions Max 0.6368 +trainer/V1 Predictions Min -85.4344 +trainer/VF Loss 0.0960411 +expl/num steps total 681000 +expl/num paths total 898 +expl/path length Mean 500 +expl/path length Std 449 +expl/path length Max 949 +expl/path length Min 51 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0366814 +expl/Actions Std 0.839576 +expl/Actions Max 2.43873 +expl/Actions Min -2.40332 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 631456 +eval/num paths total 687 +eval/path length Mean 686 +eval/path length Std 0 +eval/path length Max 686 +eval/path length Min 686 +eval/Rewards Mean 0.00145773 +eval/Rewards Std 0.0381523 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0309644 +eval/Actions Std 0.736321 +eval/Actions Max 0.999812 +eval/Actions Min -0.999922 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.14962e-05 +time/evaluation sampling (s) 5.32732 +time/exploration sampling (s) 6.68523 +time/logging (s) 0.00991034 +time/saving (s) 0.0162718 +time/training (s) 20.1909 +time/epoch (s) 32.2296 +time/total (s) 16818.2 +Epoch -320 +------------------------------ ---------------- +2022-05-15 22:43:18.829630 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -319 finished +------------------------------ ---------------- +epoch -319 +replay_buffer/size 999047 +trainer/num train calls 682000 +trainer/QF1 Loss 0.666082 +trainer/QF2 Loss 0.685192 +trainer/Policy Loss 31.4978 +trainer/Q1 Predictions Mean -70.4355 +trainer/Q1 Predictions Std 20.218 +trainer/Q1 Predictions Max 0.602585 +trainer/Q1 Predictions Min -87.5167 +trainer/Q2 Predictions Mean -70.5277 +trainer/Q2 Predictions Std 20.1525 +trainer/Q2 Predictions Max -0.507808 +trainer/Q2 Predictions Min -87.4681 +trainer/Q Targets Mean -70.3724 +trainer/Q Targets Std 19.9311 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8431 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00872148 +trainer/policy/mean Std 0.728089 +trainer/policy/mean Max 0.999299 +trainer/policy/mean Min -0.997482 +trainer/policy/std Mean 0.41482 +trainer/policy/std Std 0.019606 +trainer/policy/std Max 0.437218 +trainer/policy/std Min 0.386308 +trainer/Advantage Weights Mean 5.30752 +trainer/Advantage Weights Std 19.6573 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.86393e-23 +trainer/Advantage Score Mean -0.334524 +trainer/Advantage Score Std 0.707027 +trainer/Advantage Score Max 2.67257 +trainer/Advantage Score Min -5.23368 +trainer/V1 Predictions Mean -69.9838 +trainer/V1 Predictions Std 20.187 +trainer/V1 Predictions Max -0.294158 +trainer/V1 Predictions Min -86.7871 +trainer/VF Loss 0.104517 +expl/num steps total 682000 +expl/num paths total 900 +expl/path length Mean 500 +expl/path length Std 118 +expl/path length Max 618 +expl/path length Min 382 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0152604 +expl/Actions Std 0.833148 +expl/Actions Max 2.18432 +expl/Actions Min -2.59521 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 632456 +eval/num paths total 688 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.307928 +eval/Actions Std 0.61477 +eval/Actions Max 0.999509 +eval/Actions Min -0.999795 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.17698e-06 +time/evaluation sampling (s) 5.35054 +time/exploration sampling (s) 6.20743 +time/logging (s) 0.0122044 +time/saving (s) 0.0174583 +time/training (s) 19.8584 +time/epoch (s) 31.446 +time/total (s) 16849.7 +Epoch -319 +------------------------------ ---------------- +2022-05-15 22:43:50.354273 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -318 finished +------------------------------ ---------------- +epoch -318 +replay_buffer/size 999047 +trainer/num train calls 683000 +trainer/QF1 Loss 1.02633 +trainer/QF2 Loss 0.96711 +trainer/Policy Loss 72.0833 +trainer/Q1 Predictions Mean -71.9581 +trainer/Q1 Predictions Std 16.5783 +trainer/Q1 Predictions Max -1.94169 +trainer/Q1 Predictions Min -85.7078 +trainer/Q2 Predictions Mean -72.0006 +trainer/Q2 Predictions Std 16.5282 +trainer/Q2 Predictions Max -2.30794 +trainer/Q2 Predictions Min -85.8018 +trainer/Q Targets Mean -72.5594 +trainer/Q Targets Std 16.6858 +trainer/Q Targets Max -2.90977 +trainer/Q Targets Min -87.1323 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.015459 +trainer/policy/mean Std 0.725006 +trainer/policy/mean Max 0.999347 +trainer/policy/mean Min -0.998696 +trainer/policy/std Mean 0.4155 +trainer/policy/std Std 0.0196373 +trainer/policy/std Max 0.437992 +trainer/policy/std Min 0.385478 +trainer/Advantage Weights Mean 17.0682 +trainer/Advantage Weights Std 32.9305 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.50513e-13 +trainer/Advantage Score Mean -0.082591 +trainer/Advantage Score Std 0.666452 +trainer/Advantage Score Max 1.96799 +trainer/Advantage Score Min -2.95247 +trainer/V1 Predictions Mean -72.3967 +trainer/V1 Predictions Std 16.716 +trainer/V1 Predictions Max -2.04354 +trainer/V1 Predictions Min -86.4055 +trainer/VF Loss 0.150793 +expl/num steps total 683000 +expl/num paths total 902 +expl/path length Mean 500 +expl/path length Std 331 +expl/path length Max 831 +expl/path length Min 169 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0448842 +expl/Actions Std 0.823401 +expl/Actions Max 2.33676 +expl/Actions Min -2.17765 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 633090 +eval/num paths total 689 +eval/path length Mean 634 +eval/path length Std 0 +eval/path length Max 634 +eval/path length Min 634 +eval/Rewards Mean 0.00157729 +eval/Rewards Std 0.0396837 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0136835 +eval/Actions Std 0.715844 +eval/Actions Max 0.999681 +eval/Actions Min -0.999287 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.21377e-06 +time/evaluation sampling (s) 5.32678 +time/exploration sampling (s) 6.05644 +time/logging (s) 0.00982124 +time/saving (s) 0.0164395 +time/training (s) 20.0935 +time/epoch (s) 31.503 +time/total (s) 16881.2 +Epoch -318 +------------------------------ ---------------- +2022-05-15 22:44:21.910035 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -317 finished +------------------------------ ---------------- +epoch -317 +replay_buffer/size 999047 +trainer/num train calls 684000 +trainer/QF1 Loss 1.22183 +trainer/QF2 Loss 1.51824 +trainer/Policy Loss 21.0537 +trainer/Q1 Predictions Mean -71.4181 +trainer/Q1 Predictions Std 18.6489 +trainer/Q1 Predictions Max -1.55374 +trainer/Q1 Predictions Min -86.4379 +trainer/Q2 Predictions Mean -71.3957 +trainer/Q2 Predictions Std 18.5675 +trainer/Q2 Predictions Max -2.48435 +trainer/Q2 Predictions Min -86.1757 +trainer/Q Targets Mean -71.0842 +trainer/Q Targets Std 18.7846 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1476 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00311342 +trainer/policy/mean Std 0.720981 +trainer/policy/mean Max 0.999417 +trainer/policy/mean Min -0.998841 +trainer/policy/std Mean 0.414281 +trainer/policy/std Std 0.020915 +trainer/policy/std Max 0.440583 +trainer/policy/std Min 0.383488 +trainer/Advantage Weights Mean 5.95655 +trainer/Advantage Weights Std 20.4937 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.47462e-12 +trainer/Advantage Score Mean -0.247689 +trainer/Advantage Score Std 0.550323 +trainer/Advantage Score Max 1.77774 +trainer/Advantage Score Min -2.72426 +trainer/V1 Predictions Mean -70.8284 +trainer/V1 Predictions Std 18.8182 +trainer/V1 Predictions Max -1.22374 +trainer/V1 Predictions Min -86.1958 +trainer/VF Loss 0.0740294 +expl/num steps total 684000 +expl/num paths total 903 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0349116 +expl/Actions Std 0.869899 +expl/Actions Max 2.48283 +expl/Actions Min -2.53233 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 633522 +eval/num paths total 690 +eval/path length Mean 432 +eval/path length Std 0 +eval/path length Max 432 +eval/path length Min 432 +eval/Rewards Mean 0.00231481 +eval/Rewards Std 0.0480568 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0378969 +eval/Actions Std 0.756403 +eval/Actions Max 0.999886 +eval/Actions Min -0.999625 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.20282e-06 +time/evaluation sampling (s) 4.89659 +time/exploration sampling (s) 6.43224 +time/logging (s) 0.00948121 +time/saving (s) 0.0169199 +time/training (s) 20.1865 +time/epoch (s) 31.5417 +time/total (s) 16912.8 +Epoch -317 +------------------------------ ---------------- +2022-05-15 22:44:54.078718 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -316 finished +------------------------------ ---------------- +epoch -316 +replay_buffer/size 999047 +trainer/num train calls 685000 +trainer/QF1 Loss 0.928323 +trainer/QF2 Loss 0.902582 +trainer/Policy Loss 31.4218 +trainer/Q1 Predictions Mean -73.222 +trainer/Q1 Predictions Std 16.5201 +trainer/Q1 Predictions Max -0.49848 +trainer/Q1 Predictions Min -86.9516 +trainer/Q2 Predictions Mean -73.2041 +trainer/Q2 Predictions Std 16.5627 +trainer/Q2 Predictions Max -0.607068 +trainer/Q2 Predictions Min -86.7889 +trainer/Q Targets Mean -73.5346 +trainer/Q Targets Std 16.2856 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5654 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00723204 +trainer/policy/mean Std 0.730057 +trainer/policy/mean Max 0.999647 +trainer/policy/mean Min -0.999625 +trainer/policy/std Mean 0.415098 +trainer/policy/std Std 0.0208701 +trainer/policy/std Max 0.438905 +trainer/policy/std Min 0.383514 +trainer/Advantage Weights Mean 9.3826 +trainer/Advantage Weights Std 24.4313 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.20288e-16 +trainer/Advantage Score Mean -0.180822 +trainer/Advantage Score Std 0.667707 +trainer/Advantage Score Max 1.77871 +trainer/Advantage Score Min -3.66566 +trainer/V1 Predictions Mean -73.228 +trainer/V1 Predictions Std 16.4822 +trainer/V1 Predictions Max -3.02504 +trainer/V1 Predictions Min -86.7435 +trainer/VF Loss 0.111682 +expl/num steps total 685000 +expl/num paths total 904 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0145113 +expl/Actions Std 0.835586 +expl/Actions Max 2.26427 +expl/Actions Min -2.59663 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 634125 +eval/num paths total 691 +eval/path length Mean 603 +eval/path length Std 0 +eval/path length Max 603 +eval/path length Min 603 +eval/Rewards Mean 0.00165837 +eval/Rewards Std 0.0406894 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.000403583 +eval/Actions Std 0.748355 +eval/Actions Max 0.999011 +eval/Actions Min -0.999723 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.01142e-05 +time/evaluation sampling (s) 4.82321 +time/exploration sampling (s) 7.174 +time/logging (s) 0.00979607 +time/saving (s) 0.01738 +time/training (s) 20.128 +time/epoch (s) 32.1523 +time/total (s) 16944.9 +Epoch -316 +------------------------------ ---------------- +2022-05-15 22:45:26.623795 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -315 finished +------------------------------ ---------------- +epoch -315 +replay_buffer/size 999047 +trainer/num train calls 686000 +trainer/QF1 Loss 0.998047 +trainer/QF2 Loss 1.02172 +trainer/Policy Loss 16.7727 +trainer/Q1 Predictions Mean -69.2741 +trainer/Q1 Predictions Std 20.1493 +trainer/Q1 Predictions Max -0.76998 +trainer/Q1 Predictions Min -86.7411 +trainer/Q2 Predictions Mean -69.1875 +trainer/Q2 Predictions Std 20.1154 +trainer/Q2 Predictions Max -0.795581 +trainer/Q2 Predictions Min -86.441 +trainer/Q Targets Mean -69.3835 +trainer/Q Targets Std 20.1833 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8434 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0129668 +trainer/policy/mean Std 0.732535 +trainer/policy/mean Max 0.999115 +trainer/policy/mean Min -0.9998 +trainer/policy/std Mean 0.413962 +trainer/policy/std Std 0.0208183 +trainer/policy/std Max 0.437559 +trainer/policy/std Min 0.382173 +trainer/Advantage Weights Mean 3.27908 +trainer/Advantage Weights Std 14.7595 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.90113e-23 +trainer/Advantage Score Mean -0.43902 +trainer/Advantage Score Std 0.668776 +trainer/Advantage Score Max 3.00141 +trainer/Advantage Score Min -5.2317 +trainer/V1 Predictions Mean -69.0127 +trainer/V1 Predictions Std 20.3007 +trainer/V1 Predictions Max -1.45985 +trainer/V1 Predictions Min -86.7911 +trainer/VF Loss 0.103773 +expl/num steps total 686000 +expl/num paths total 905 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.167841 +expl/Actions Std 0.879008 +expl/Actions Max 2.32551 +expl/Actions Min -2.51095 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 634804 +eval/num paths total 692 +eval/path length Mean 679 +eval/path length Std 0 +eval/path length Max 679 +eval/path length Min 679 +eval/Rewards Mean 0.00147275 +eval/Rewards Std 0.0383482 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.0317583 +eval/Actions Std 0.71799 +eval/Actions Max 0.999695 +eval/Actions Min -0.999787 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.24909e-05 +time/evaluation sampling (s) 5.55774 +time/exploration sampling (s) 6.99303 +time/logging (s) 0.0132941 +time/saving (s) 0.0156602 +time/training (s) 19.953 +time/epoch (s) 32.5327 +time/total (s) 16977.5 +Epoch -315 +------------------------------ ---------------- +2022-05-15 22:45:58.866437 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -314 finished +------------------------------ ---------------- +epoch -314 +replay_buffer/size 999047 +trainer/num train calls 687000 +trainer/QF1 Loss 0.701207 +trainer/QF2 Loss 0.559964 +trainer/Policy Loss 20.5484 +trainer/Q1 Predictions Mean -71.768 +trainer/Q1 Predictions Std 18.4805 +trainer/Q1 Predictions Max -0.28289 +trainer/Q1 Predictions Min -87.762 +trainer/Q2 Predictions Mean -71.8277 +trainer/Q2 Predictions Std 18.4678 +trainer/Q2 Predictions Max -0.477345 +trainer/Q2 Predictions Min -87.5901 +trainer/Q Targets Mean -71.7481 +trainer/Q Targets Std 18.4957 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6009 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0431108 +trainer/policy/mean Std 0.725054 +trainer/policy/mean Max 0.998182 +trainer/policy/mean Min -0.999608 +trainer/policy/std Mean 0.412734 +trainer/policy/std Std 0.0204101 +trainer/policy/std Max 0.438178 +trainer/policy/std Min 0.380414 +trainer/Advantage Weights Mean 3.03995 +trainer/Advantage Weights Std 15.6462 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.82273e-20 +trainer/Advantage Score Mean -0.429141 +trainer/Advantage Score Std 0.576763 +trainer/Advantage Score Max 1.72775 +trainer/Advantage Score Min -4.42899 +trainer/V1 Predictions Mean -71.5048 +trainer/V1 Predictions Std 18.5119 +trainer/V1 Predictions Max -0.229106 +trainer/V1 Predictions Min -87.637 +trainer/VF Loss 0.0677344 +expl/num steps total 687000 +expl/num paths total 907 +expl/path length Mean 500 +expl/path length Std 336 +expl/path length Max 836 +expl/path length Min 164 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0373486 +expl/Actions Std 0.833725 +expl/Actions Max 2.47789 +expl/Actions Min -2.44401 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 635804 +eval/num paths total 693 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.243648 +eval/Actions Std 0.674543 +eval/Actions Max 0.999839 +eval/Actions Min -0.99939 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.37202e-05 +time/evaluation sampling (s) 5.16643 +time/exploration sampling (s) 7.00794 +time/logging (s) 0.00762812 +time/saving (s) 0.0152203 +time/training (s) 20.0239 +time/epoch (s) 32.2211 +time/total (s) 17009.7 +Epoch -314 +------------------------------ ---------------- +2022-05-15 22:46:30.767496 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -313 finished +------------------------------ ---------------- +epoch -313 +replay_buffer/size 999047 +trainer/num train calls 688000 +trainer/QF1 Loss 1.19031 +trainer/QF2 Loss 1.19261 +trainer/Policy Loss 21.6235 +trainer/Q1 Predictions Mean -72.3267 +trainer/Q1 Predictions Std 17.8233 +trainer/Q1 Predictions Max -3.53614 +trainer/Q1 Predictions Min -87.0869 +trainer/Q2 Predictions Mean -72.2685 +trainer/Q2 Predictions Std 17.7477 +trainer/Q2 Predictions Max -2.98663 +trainer/Q2 Predictions Min -87.1593 +trainer/Q Targets Mean -72.0438 +trainer/Q Targets Std 18.118 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6217 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0163365 +trainer/policy/mean Std 0.72672 +trainer/policy/mean Max 0.999664 +trainer/policy/mean Min -0.999487 +trainer/policy/std Mean 0.412317 +trainer/policy/std Std 0.0204205 +trainer/policy/std Max 0.436139 +trainer/policy/std Min 0.381527 +trainer/Advantage Weights Mean 5.2072 +trainer/Advantage Weights Std 17.0327 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.77293e-24 +trainer/Advantage Score Mean -0.42291 +trainer/Advantage Score Std 0.873547 +trainer/Advantage Score Max 1.19857 +trainer/Advantage Score Min -5.29824 +trainer/V1 Predictions Mean -71.7366 +trainer/V1 Predictions Std 18.2118 +trainer/V1 Predictions Max -1.66268 +trainer/V1 Predictions Min -86.4391 +trainer/VF Loss 0.111338 +expl/num steps total 688000 +expl/num paths total 909 +expl/path length Mean 500 +expl/path length Std 494 +expl/path length Max 994 +expl/path length Min 6 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0177527 +expl/Actions Std 0.823955 +expl/Actions Max 2.22177 +expl/Actions Min -2.69796 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 636655 +eval/num paths total 695 +eval/path length Mean 425.5 +eval/path length Std 55.5 +eval/path length Max 481 +eval/path length Min 370 +eval/Rewards Mean 0.00235018 +eval/Rewards Std 0.0484216 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0386456 +eval/Actions Std 0.751283 +eval/Actions Max 0.999967 +eval/Actions Min -0.999785 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 4.13833e-06 +time/evaluation sampling (s) 5.1654 +time/exploration sampling (s) 7.01327 +time/logging (s) 0.00779756 +time/saving (s) 0.0139656 +time/training (s) 19.6892 +time/epoch (s) 31.8896 +time/total (s) 17041.6 +Epoch -313 +------------------------------ ---------------- +2022-05-15 22:47:02.120264 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -312 finished +------------------------------ ---------------- +epoch -312 +replay_buffer/size 999047 +trainer/num train calls 689000 +trainer/QF1 Loss 0.867683 +trainer/QF2 Loss 0.776381 +trainer/Policy Loss 25.3909 +trainer/Q1 Predictions Mean -72.9376 +trainer/Q1 Predictions Std 16.8996 +trainer/Q1 Predictions Max 0.613401 +trainer/Q1 Predictions Min -87.8661 +trainer/Q2 Predictions Mean -72.9238 +trainer/Q2 Predictions Std 16.9165 +trainer/Q2 Predictions Max -0.39842 +trainer/Q2 Predictions Min -86.9503 +trainer/Q Targets Mean -72.7026 +trainer/Q Targets Std 16.9845 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1808 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00415618 +trainer/policy/mean Std 0.740115 +trainer/policy/mean Max 0.998911 +trainer/policy/mean Min -0.999378 +trainer/policy/std Mean 0.413595 +trainer/policy/std Std 0.0198054 +trainer/policy/std Max 0.433903 +trainer/policy/std Min 0.382976 +trainer/Advantage Weights Mean 3.90655 +trainer/Advantage Weights Std 17.5107 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.22783e-21 +trainer/Advantage Score Mean -0.364144 +trainer/Advantage Score Std 0.648257 +trainer/Advantage Score Max 1.70505 +trainer/Advantage Score Min -4.8149 +trainer/V1 Predictions Mean -72.5162 +trainer/V1 Predictions Std 17.0278 +trainer/V1 Predictions Max 0.415014 +trainer/V1 Predictions Min -87.3844 +trainer/VF Loss 0.0816439 +expl/num steps total 689000 +expl/num paths total 912 +expl/path length Mean 333.333 +expl/path length Std 202.678 +expl/path length Max 540 +expl/path length Min 58 +expl/Rewards Mean 0.002 +expl/Rewards Std 0.0446766 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.666667 +expl/Returns Std 0.471405 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0183211 +expl/Actions Std 0.845776 +expl/Actions Max 2.2163 +expl/Actions Min -2.36944 +expl/Num Paths 3 +expl/Average Returns 0.666667 +eval/num steps total 637310 +eval/num paths total 696 +eval/path length Mean 655 +eval/path length Std 0 +eval/path length Max 655 +eval/path length Min 655 +eval/Rewards Mean 0.00152672 +eval/Rewards Std 0.0390434 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0179325 +eval/Actions Std 0.752599 +eval/Actions Max 0.999972 +eval/Actions Min -0.999727 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.01207e-05 +time/evaluation sampling (s) 5.29438 +time/exploration sampling (s) 6.52774 +time/logging (s) 0.010909 +time/saving (s) 0.0241609 +time/training (s) 19.483 +time/epoch (s) 31.3402 +time/total (s) 17072.9 +Epoch -312 +------------------------------ ---------------- +2022-05-15 22:47:33.997162 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -311 finished +------------------------------ ---------------- +epoch -311 +replay_buffer/size 999047 +trainer/num train calls 690000 +trainer/QF1 Loss 0.60379 +trainer/QF2 Loss 0.601858 +trainer/Policy Loss 17.872 +trainer/Q1 Predictions Mean -72.9441 +trainer/Q1 Predictions Std 17.6881 +trainer/Q1 Predictions Max -1.0215 +trainer/Q1 Predictions Min -86.6605 +trainer/Q2 Predictions Mean -72.9451 +trainer/Q2 Predictions Std 17.7337 +trainer/Q2 Predictions Max -0.591912 +trainer/Q2 Predictions Min -86.578 +trainer/Q Targets Mean -72.979 +trainer/Q Targets Std 17.4008 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5623 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00263075 +trainer/policy/mean Std 0.73029 +trainer/policy/mean Max 0.999849 +trainer/policy/mean Min -0.999541 +trainer/policy/std Mean 0.414416 +trainer/policy/std Std 0.0192518 +trainer/policy/std Max 0.435014 +trainer/policy/std Min 0.38322 +trainer/Advantage Weights Mean 4.4093 +trainer/Advantage Weights Std 19.3254 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.5564e-13 +trainer/Advantage Score Mean -0.407597 +trainer/Advantage Score Std 0.53121 +trainer/Advantage Score Max 1.19551 +trainer/Advantage Score Min -2.94912 +trainer/V1 Predictions Mean -72.6679 +trainer/V1 Predictions Std 17.6145 +trainer/V1 Predictions Max -0.533413 +trainer/V1 Predictions Min -86.4429 +trainer/VF Loss 0.0657886 +expl/num steps total 690000 +expl/num paths total 914 +expl/path length Mean 500 +expl/path length Std 161 +expl/path length Max 661 +expl/path length Min 339 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0194107 +expl/Actions Std 0.836668 +expl/Actions Max 2.3425 +expl/Actions Min -2.21673 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 638026 +eval/num paths total 697 +eval/path length Mean 716 +eval/path length Std 0 +eval/path length Max 716 +eval/path length Min 716 +eval/Rewards Mean 0.00139665 +eval/Rewards Std 0.0373456 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0303592 +eval/Actions Std 0.746706 +eval/Actions Max 0.999671 +eval/Actions Min -0.999725 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.08052e-05 +time/evaluation sampling (s) 4.91142 +time/exploration sampling (s) 6.81313 +time/logging (s) 0.0116561 +time/saving (s) 0.0191546 +time/training (s) 20.1023 +time/epoch (s) 31.8577 +time/total (s) 17104.8 +Epoch -311 +------------------------------ ---------------- +2022-05-15 22:48:05.691581 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -310 finished +------------------------------ --------------- +epoch -310 +replay_buffer/size 999047 +trainer/num train calls 691000 +trainer/QF1 Loss 1.47491 +trainer/QF2 Loss 1.30545 +trainer/Policy Loss 31.2645 +trainer/Q1 Predictions Mean -70.8885 +trainer/Q1 Predictions Std 18.2511 +trainer/Q1 Predictions Max -0.467447 +trainer/Q1 Predictions Min -86.5231 +trainer/Q2 Predictions Mean -70.8989 +trainer/Q2 Predictions Std 18.3598 +trainer/Q2 Predictions Max -0.626309 +trainer/Q2 Predictions Min -86.383 +trainer/Q Targets Mean -70.916 +trainer/Q Targets Std 18.5186 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3346 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0189672 +trainer/policy/mean Std 0.721935 +trainer/policy/mean Max 0.999935 +trainer/policy/mean Min -0.998377 +trainer/policy/std Mean 0.414287 +trainer/policy/std Std 0.0200803 +trainer/policy/std Max 0.434941 +trainer/policy/std Min 0.383525 +trainer/Advantage Weights Mean 6.42663 +trainer/Advantage Weights Std 21.2502 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.9366e-20 +trainer/Advantage Score Mean -0.395527 +trainer/Advantage Score Std 0.741891 +trainer/Advantage Score Max 1.23555 +trainer/Advantage Score Min -4.39802 +trainer/V1 Predictions Mean -70.6489 +trainer/V1 Predictions Std 18.7537 +trainer/V1 Predictions Max -0.296501 +trainer/V1 Predictions Min -86.262 +trainer/VF Loss 0.0951466 +expl/num steps total 691000 +expl/num paths total 916 +expl/path length Mean 500 +expl/path length Std 36 +expl/path length Max 536 +expl/path length Min 464 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0216698 +expl/Actions Std 0.827565 +expl/Actions Max 2.3745 +expl/Actions Min -2.26038 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 638665 +eval/num paths total 698 +eval/path length Mean 639 +eval/path length Std 0 +eval/path length Max 639 +eval/path length Min 639 +eval/Rewards Mean 0.00156495 +eval/Rewards Std 0.0395284 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0366189 +eval/Actions Std 0.738586 +eval/Actions Max 0.999624 +eval/Actions Min -0.99968 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0692e-05 +time/evaluation sampling (s) 4.97576 +time/exploration sampling (s) 6.63087 +time/logging (s) 0.010814 +time/saving (s) 0.0179775 +time/training (s) 20.0385 +time/epoch (s) 31.6739 +time/total (s) 17136.5 +Epoch -310 +------------------------------ --------------- +2022-05-15 22:48:38.041360 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -309 finished +------------------------------ ---------------- +epoch -309 +replay_buffer/size 999047 +trainer/num train calls 692000 +trainer/QF1 Loss 0.7177 +trainer/QF2 Loss 0.583982 +trainer/Policy Loss 10.4269 +trainer/Q1 Predictions Mean -71.5463 +trainer/Q1 Predictions Std 17.3276 +trainer/Q1 Predictions Max -1.30603 +trainer/Q1 Predictions Min -87.8476 +trainer/Q2 Predictions Mean -71.4164 +trainer/Q2 Predictions Std 17.3723 +trainer/Q2 Predictions Max -0.217837 +trainer/Q2 Predictions Min -87.9553 +trainer/Q Targets Mean -71.2349 +trainer/Q Targets Std 17.4089 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9184 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00127617 +trainer/policy/mean Std 0.719733 +trainer/policy/mean Max 0.999795 +trainer/policy/mean Min -0.998149 +trainer/policy/std Mean 0.412816 +trainer/policy/std Std 0.0205269 +trainer/policy/std Max 0.434512 +trainer/policy/std Min 0.382675 +trainer/Advantage Weights Mean 3.13072 +trainer/Advantage Weights Std 15.5357 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.99822e-16 +trainer/Advantage Score Mean -0.553041 +trainer/Advantage Score Std 0.632147 +trainer/Advantage Score Max 2.08957 +trainer/Advantage Score Min -3.48957 +trainer/V1 Predictions Mean -70.9426 +trainer/V1 Predictions Std 17.4637 +trainer/V1 Predictions Max -0.147342 +trainer/V1 Predictions Min -87.6734 +trainer/VF Loss 0.0952103 +expl/num steps total 692000 +expl/num paths total 918 +expl/path length Mean 500 +expl/path length Std 95 +expl/path length Max 595 +expl/path length Min 405 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0114156 +expl/Actions Std 0.818826 +expl/Actions Max 2.19602 +expl/Actions Min -2.18599 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 639665 +eval/num paths total 699 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.173696 +eval/Actions Std 0.781641 +eval/Actions Max 0.999732 +eval/Actions Min -0.999489 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.10922e-06 +time/evaluation sampling (s) 5.53039 +time/exploration sampling (s) 6.58476 +time/logging (s) 0.0116993 +time/saving (s) 0.0155093 +time/training (s) 20.1878 +time/epoch (s) 32.3302 +time/total (s) 17168.8 +Epoch -309 +------------------------------ ---------------- +2022-05-15 22:49:09.897163 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -308 finished +------------------------------ ---------------- +epoch -308 +replay_buffer/size 999047 +trainer/num train calls 693000 +trainer/QF1 Loss 0.75424 +trainer/QF2 Loss 0.71009 +trainer/Policy Loss 35.5371 +trainer/Q1 Predictions Mean -72.6004 +trainer/Q1 Predictions Std 16.5244 +trainer/Q1 Predictions Max -0.500357 +trainer/Q1 Predictions Min -86.437 +trainer/Q2 Predictions Mean -72.6077 +trainer/Q2 Predictions Std 16.4768 +trainer/Q2 Predictions Max -0.373443 +trainer/Q2 Predictions Min -86.4377 +trainer/Q Targets Mean -72.7074 +trainer/Q Targets Std 16.4741 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6619 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00991645 +trainer/policy/mean Std 0.729087 +trainer/policy/mean Max 0.999567 +trainer/policy/mean Min -0.99906 +trainer/policy/std Mean 0.413499 +trainer/policy/std Std 0.0205882 +trainer/policy/std Max 0.436805 +trainer/policy/std Min 0.381758 +trainer/Advantage Weights Mean 7.88295 +trainer/Advantage Weights Std 22.6422 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.05364e-18 +trainer/Advantage Score Mean -0.272919 +trainer/Advantage Score Std 0.671006 +trainer/Advantage Score Max 1.50466 +trainer/Advantage Score Min -3.9493 +trainer/V1 Predictions Mean -72.4613 +trainer/V1 Predictions Std 16.5842 +trainer/V1 Predictions Max 0.959638 +trainer/V1 Predictions Min -86.4099 +trainer/VF Loss 0.0927438 +expl/num steps total 693000 +expl/num paths total 920 +expl/path length Mean 500 +expl/path length Std 281 +expl/path length Max 781 +expl/path length Min 219 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0337114 +expl/Actions Std 0.798218 +expl/Actions Max 2.46318 +expl/Actions Min -2.20034 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 640665 +eval/num paths total 700 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.165903 +eval/Actions Std 0.666763 +eval/Actions Max 0.999738 +eval/Actions Min -0.99992 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.26825e-06 +time/evaluation sampling (s) 5.37036 +time/exploration sampling (s) 7.32278 +time/logging (s) 0.00763256 +time/saving (s) 0.0154203 +time/training (s) 19.1188 +time/epoch (s) 31.835 +time/total (s) 17200.7 +Epoch -308 +------------------------------ ---------------- +2022-05-15 22:49:41.413859 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -307 finished +------------------------------ ---------------- +epoch -307 +replay_buffer/size 999047 +trainer/num train calls 694000 +trainer/QF1 Loss 0.692657 +trainer/QF2 Loss 0.734856 +trainer/Policy Loss 16.4357 +trainer/Q1 Predictions Mean -73.5704 +trainer/Q1 Predictions Std 16.4899 +trainer/Q1 Predictions Max -0.677551 +trainer/Q1 Predictions Min -86.3164 +trainer/Q2 Predictions Mean -73.588 +trainer/Q2 Predictions Std 16.4435 +trainer/Q2 Predictions Max -0.776454 +trainer/Q2 Predictions Min -86.1662 +trainer/Q Targets Mean -73.6906 +trainer/Q Targets Std 16.6775 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6809 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00206398 +trainer/policy/mean Std 0.741353 +trainer/policy/mean Max 0.999451 +trainer/policy/mean Min -0.99948 +trainer/policy/std Mean 0.414131 +trainer/policy/std Std 0.0211542 +trainer/policy/std Max 0.438189 +trainer/policy/std Min 0.381033 +trainer/Advantage Weights Mean 5.77162 +trainer/Advantage Weights Std 20.0454 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.37176e-20 +trainer/Advantage Score Mean -0.291332 +trainer/Advantage Score Std 0.621538 +trainer/Advantage Score Max 1.32153 +trainer/Advantage Score Min -4.57356 +trainer/V1 Predictions Mean -73.5294 +trainer/V1 Predictions Std 16.7127 +trainer/V1 Predictions Max -0.58368 +trainer/V1 Predictions Min -86.4986 +trainer/VF Loss 0.0772055 +expl/num steps total 694000 +expl/num paths total 922 +expl/path length Mean 500 +expl/path length Std 236 +expl/path length Max 736 +expl/path length Min 264 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0161331 +expl/Actions Std 0.833516 +expl/Actions Max 2.20375 +expl/Actions Min -2.46445 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 641665 +eval/num paths total 701 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.348211 +eval/Actions Std 0.73246 +eval/Actions Max 0.998751 +eval/Actions Min -0.999782 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.10201e-06 +time/evaluation sampling (s) 5.09108 +time/exploration sampling (s) 6.71603 +time/logging (s) 0.0111179 +time/saving (s) 0.0159293 +time/training (s) 19.6719 +time/epoch (s) 31.506 +time/total (s) 17232.2 +Epoch -307 +------------------------------ ---------------- +2022-05-15 22:50:13.254304 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -306 finished +------------------------------ ---------------- +epoch -306 +replay_buffer/size 999047 +trainer/num train calls 695000 +trainer/QF1 Loss 1.05899 +trainer/QF2 Loss 0.962147 +trainer/Policy Loss 18.6675 +trainer/Q1 Predictions Mean -73.0431 +trainer/Q1 Predictions Std 16.6084 +trainer/Q1 Predictions Max -3.01129 +trainer/Q1 Predictions Min -86.659 +trainer/Q2 Predictions Mean -73.0462 +trainer/Q2 Predictions Std 16.5894 +trainer/Q2 Predictions Max -4.78557 +trainer/Q2 Predictions Min -86.725 +trainer/Q Targets Mean -72.6851 +trainer/Q Targets Std 16.6128 +trainer/Q Targets Max -4.01828 +trainer/Q Targets Min -86.4037 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0108005 +trainer/policy/mean Std 0.741318 +trainer/policy/mean Max 0.99922 +trainer/policy/mean Min -0.999928 +trainer/policy/std Mean 0.415723 +trainer/policy/std Std 0.0208604 +trainer/policy/std Max 0.437068 +trainer/policy/std Min 0.383253 +trainer/Advantage Weights Mean 3.81464 +trainer/Advantage Weights Std 17.1034 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.73975e-21 +trainer/Advantage Score Mean -0.476776 +trainer/Advantage Score Std 0.606793 +trainer/Advantage Score Max 1.23567 +trainer/Advantage Score Min -4.73464 +trainer/V1 Predictions Mean -72.4987 +trainer/V1 Predictions Std 16.737 +trainer/V1 Predictions Max -2.62289 +trainer/V1 Predictions Min -86.3667 +trainer/VF Loss 0.0784241 +expl/num steps total 695000 +expl/num paths total 923 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0500415 +expl/Actions Std 0.84695 +expl/Actions Max 2.44615 +expl/Actions Min -2.29948 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 642665 +eval/num paths total 702 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.129887 +eval/Actions Std 0.726895 +eval/Actions Max 0.999645 +eval/Actions Min -0.999757 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.05882e-05 +time/evaluation sampling (s) 4.87161 +time/exploration sampling (s) 7.33015 +time/logging (s) 0.0127669 +time/saving (s) 0.0194958 +time/training (s) 19.5902 +time/epoch (s) 31.8242 +time/total (s) 17264 +Epoch -306 +------------------------------ ---------------- +2022-05-15 22:50:45.311151 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -305 finished +------------------------------ ---------------- +epoch -305 +replay_buffer/size 999047 +trainer/num train calls 696000 +trainer/QF1 Loss 13.238 +trainer/QF2 Loss 13.7604 +trainer/Policy Loss 34.1285 +trainer/Q1 Predictions Mean -72.5457 +trainer/Q1 Predictions Std 16.8736 +trainer/Q1 Predictions Max -1.12321 +trainer/Q1 Predictions Min -88.3476 +trainer/Q2 Predictions Mean -72.5423 +trainer/Q2 Predictions Std 16.7628 +trainer/Q2 Predictions Max -1.30937 +trainer/Q2 Predictions Min -88.5805 +trainer/Q Targets Mean -72.511 +trainer/Q Targets Std 17.3813 +trainer/Q Targets Max -1.34727 +trainer/Q Targets Min -90.3392 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0211431 +trainer/policy/mean Std 0.725908 +trainer/policy/mean Max 0.999292 +trainer/policy/mean Min -0.998362 +trainer/policy/std Mean 0.41233 +trainer/policy/std Std 0.0209958 +trainer/policy/std Max 0.436085 +trainer/policy/std Min 0.381747 +trainer/Advantage Weights Mean 6.01558 +trainer/Advantage Weights Std 20.0078 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.92747e-17 +trainer/Advantage Score Mean -0.394614 +trainer/Advantage Score Std 0.781523 +trainer/Advantage Score Max 1.25523 +trainer/Advantage Score Min -3.73644 +trainer/V1 Predictions Mean -72.389 +trainer/V1 Predictions Std 17.2706 +trainer/V1 Predictions Max -0.254974 +trainer/V1 Predictions Min -88.6068 +trainer/VF Loss 0.097825 +expl/num steps total 696000 +expl/num paths total 925 +expl/path length Mean 500 +expl/path length Std 230 +expl/path length Max 730 +expl/path length Min 270 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0192939 +expl/Actions Std 0.828971 +expl/Actions Max 2.19135 +expl/Actions Min -2.45813 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 643485 +eval/num paths total 703 +eval/path length Mean 820 +eval/path length Std 0 +eval/path length Max 820 +eval/path length Min 820 +eval/Rewards Mean 0.00121951 +eval/Rewards Std 0.0349002 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0361175 +eval/Actions Std 0.749302 +eval/Actions Max 0.999723 +eval/Actions Min -0.999445 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.06958e-05 +time/evaluation sampling (s) 5.73642 +time/exploration sampling (s) 6.31839 +time/logging (s) 0.0114008 +time/saving (s) 0.0150494 +time/training (s) 19.9541 +time/epoch (s) 32.0354 +time/total (s) 17296 +Epoch -305 +------------------------------ ---------------- +2022-05-15 22:51:16.569052 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -304 finished +------------------------------ ---------------- +epoch -304 +replay_buffer/size 999047 +trainer/num train calls 697000 +trainer/QF1 Loss 1.03303 +trainer/QF2 Loss 0.852166 +trainer/Policy Loss 12.3587 +trainer/Q1 Predictions Mean -72.2815 +trainer/Q1 Predictions Std 17.1294 +trainer/Q1 Predictions Max -4.64143 +trainer/Q1 Predictions Min -87.1282 +trainer/Q2 Predictions Mean -72.2211 +trainer/Q2 Predictions Std 17.1358 +trainer/Q2 Predictions Max -4.43053 +trainer/Q2 Predictions Min -87.5243 +trainer/Q Targets Mean -72.0319 +trainer/Q Targets Std 17.2374 +trainer/Q Targets Max -2.9199 +trainer/Q Targets Min -86.9654 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00786237 +trainer/policy/mean Std 0.723315 +trainer/policy/mean Max 0.999159 +trainer/policy/mean Min -0.999257 +trainer/policy/std Mean 0.412218 +trainer/policy/std Std 0.0192125 +trainer/policy/std Max 0.433863 +trainer/policy/std Min 0.384337 +trainer/Advantage Weights Mean 4.05992 +trainer/Advantage Weights Std 17.0712 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.73202e-22 +trainer/Advantage Score Mean -0.5217 +trainer/Advantage Score Std 0.772806 +trainer/Advantage Score Max 0.860058 +trainer/Advantage Score Min -4.96518 +trainer/V1 Predictions Mean -71.6655 +trainer/V1 Predictions Std 17.6241 +trainer/V1 Predictions Max -2.01762 +trainer/V1 Predictions Min -87.1045 +trainer/VF Loss 0.100753 +expl/num steps total 697000 +expl/num paths total 926 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0132588 +expl/Actions Std 0.825814 +expl/Actions Max 2.39174 +expl/Actions Min -2.30351 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 644173 +eval/num paths total 704 +eval/path length Mean 688 +eval/path length Std 0 +eval/path length Max 688 +eval/path length Min 688 +eval/Rewards Mean 0.00145349 +eval/Rewards Std 0.0380969 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0539122 +eval/Actions Std 0.731948 +eval/Actions Max 0.999915 +eval/Actions Min -0.999539 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.27149e-05 +time/evaluation sampling (s) 4.70984 +time/exploration sampling (s) 7.29184 +time/logging (s) 0.00704257 +time/saving (s) 0.0122377 +time/training (s) 19.2158 +time/epoch (s) 31.2368 +time/total (s) 17327.3 +Epoch -304 +------------------------------ ---------------- +2022-05-15 22:51:47.774390 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -303 finished +------------------------------ ---------------- +epoch -303 +replay_buffer/size 999047 +trainer/num train calls 698000 +trainer/QF1 Loss 0.757229 +trainer/QF2 Loss 0.733758 +trainer/Policy Loss 25.3211 +trainer/Q1 Predictions Mean -71.1193 +trainer/Q1 Predictions Std 18.9389 +trainer/Q1 Predictions Max -0.388311 +trainer/Q1 Predictions Min -86.8962 +trainer/Q2 Predictions Mean -71.0556 +trainer/Q2 Predictions Std 18.9368 +trainer/Q2 Predictions Max -0.0122787 +trainer/Q2 Predictions Min -86.7291 +trainer/Q Targets Mean -71.0769 +trainer/Q Targets Std 19.0297 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6401 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00488232 +trainer/policy/mean Std 0.736135 +trainer/policy/mean Max 0.997008 +trainer/policy/mean Min -0.999249 +trainer/policy/std Mean 0.411442 +trainer/policy/std Std 0.0207579 +trainer/policy/std Max 0.436605 +trainer/policy/std Min 0.380407 +trainer/Advantage Weights Mean 5.13722 +trainer/Advantage Weights Std 20.036 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.69625e-16 +trainer/Advantage Score Mean -0.485809 +trainer/Advantage Score Std 0.65392 +trainer/Advantage Score Max 0.876862 +trainer/Advantage Score Min -3.63129 +trainer/V1 Predictions Mean -70.8292 +trainer/V1 Predictions Std 19.0895 +trainer/V1 Predictions Max 1.03127 +trainer/V1 Predictions Min -86.4292 +trainer/VF Loss 0.0815524 +expl/num steps total 698000 +expl/num paths total 928 +expl/path length Mean 500 +expl/path length Std 234 +expl/path length Max 734 +expl/path length Min 266 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0147549 +expl/Actions Std 0.822912 +expl/Actions Max 2.34936 +expl/Actions Min -2.15525 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 644733 +eval/num paths total 705 +eval/path length Mean 560 +eval/path length Std 0 +eval/path length Max 560 +eval/path length Min 560 +eval/Rewards Mean 0.00178571 +eval/Rewards Std 0.04222 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0219144 +eval/Actions Std 0.739803 +eval/Actions Max 0.999844 +eval/Actions Min -0.999769 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.16095e-06 +time/evaluation sampling (s) 5.18161 +time/exploration sampling (s) 6.70559 +time/logging (s) 0.00818699 +time/saving (s) 0.0155253 +time/training (s) 19.2833 +time/epoch (s) 31.1942 +time/total (s) 17358.5 +Epoch -303 +------------------------------ ---------------- +2022-05-15 22:52:19.900080 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -302 finished +------------------------------ ---------------- +epoch -302 +replay_buffer/size 999047 +trainer/num train calls 699000 +trainer/QF1 Loss 1.26744 +trainer/QF2 Loss 0.929665 +trainer/Policy Loss 18.9087 +trainer/Q1 Predictions Mean -71.5492 +trainer/Q1 Predictions Std 17.4759 +trainer/Q1 Predictions Max -2.30891 +trainer/Q1 Predictions Min -88.7571 +trainer/Q2 Predictions Mean -71.6362 +trainer/Q2 Predictions Std 17.4335 +trainer/Q2 Predictions Max -2.23814 +trainer/Q2 Predictions Min -88.702 +trainer/Q Targets Mean -71.6547 +trainer/Q Targets Std 17.7462 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.215 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0369326 +trainer/policy/mean Std 0.72399 +trainer/policy/mean Max 0.99838 +trainer/policy/mean Min -0.999459 +trainer/policy/std Mean 0.412855 +trainer/policy/std Std 0.0209983 +trainer/policy/std Max 0.437511 +trainer/policy/std Min 0.384968 +trainer/Advantage Weights Mean 5.35568 +trainer/Advantage Weights Std 18.8199 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.1e-44 +trainer/Advantage Score Mean -0.422171 +trainer/Advantage Score Std 0.94781 +trainer/Advantage Score Max 1.68978 +trainer/Advantage Score Min -10.1248 +trainer/V1 Predictions Mean -71.297 +trainer/V1 Predictions Std 17.9957 +trainer/V1 Predictions Max -1.39658 +trainer/V1 Predictions Min -89.2029 +trainer/VF Loss 0.13194 +expl/num steps total 699000 +expl/num paths total 929 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0229339 +expl/Actions Std 0.84182 +expl/Actions Max 2.26194 +expl/Actions Min -2.25303 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 645733 +eval/num paths total 706 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00972734 +eval/Actions Std 0.736213 +eval/Actions Max 0.999841 +eval/Actions Min -0.999531 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.08168e-05 +time/evaluation sampling (s) 5.71871 +time/exploration sampling (s) 6.45964 +time/logging (s) 0.0132343 +time/saving (s) 0.0200719 +time/training (s) 19.9037 +time/epoch (s) 32.1153 +time/total (s) 17390.6 +Epoch -302 +------------------------------ ---------------- +2022-05-15 22:52:51.981117 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -301 finished +------------------------------ ---------------- +epoch -301 +replay_buffer/size 999047 +trainer/num train calls 700000 +trainer/QF1 Loss 1.44486 +trainer/QF2 Loss 1.37425 +trainer/Policy Loss 25.3139 +trainer/Q1 Predictions Mean -70.7548 +trainer/Q1 Predictions Std 19.6087 +trainer/Q1 Predictions Max -2.18734 +trainer/Q1 Predictions Min -86.7756 +trainer/Q2 Predictions Mean -70.6719 +trainer/Q2 Predictions Std 19.6942 +trainer/Q2 Predictions Max -2.47743 +trainer/Q2 Predictions Min -86.6374 +trainer/Q Targets Mean -70.3596 +trainer/Q Targets Std 19.5486 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3449 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00197154 +trainer/policy/mean Std 0.73753 +trainer/policy/mean Max 0.999343 +trainer/policy/mean Min -0.999337 +trainer/policy/std Mean 0.412644 +trainer/policy/std Std 0.020448 +trainer/policy/std Max 0.436191 +trainer/policy/std Min 0.383388 +trainer/Advantage Weights Mean 3.59547 +trainer/Advantage Weights Std 16.5425 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.34127e-27 +trainer/Advantage Score Mean -0.606438 +trainer/Advantage Score Std 0.859409 +trainer/Advantage Score Max 1.93561 +trainer/Advantage Score Min -6.18762 +trainer/V1 Predictions Mean -69.9681 +trainer/V1 Predictions Std 19.9288 +trainer/V1 Predictions Max -1.34393 +trainer/V1 Predictions Min -86.1813 +trainer/VF Loss 0.134291 +expl/num steps total 700000 +expl/num paths total 931 +expl/path length Mean 500 +expl/path length Std 216 +expl/path length Max 716 +expl/path length Min 284 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0210542 +expl/Actions Std 0.820681 +expl/Actions Max 2.32905 +expl/Actions Min -2.31952 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 646733 +eval/num paths total 707 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.191383 +eval/Actions Std 0.766177 +eval/Actions Max 0.999541 +eval/Actions Min -0.999518 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0137e-05 +time/evaluation sampling (s) 5.80736 +time/exploration sampling (s) 6.94633 +time/logging (s) 0.0122158 +time/saving (s) 0.0174141 +time/training (s) 19.2755 +time/epoch (s) 32.0589 +time/total (s) 17422.7 +Epoch -301 +------------------------------ ---------------- +2022-05-15 22:53:24.197963 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -300 finished +------------------------------ ---------------- +epoch -300 +replay_buffer/size 999047 +trainer/num train calls 701000 +trainer/QF1 Loss 1.18661 +trainer/QF2 Loss 1.44039 +trainer/Policy Loss 32.3613 +trainer/Q1 Predictions Mean -71.3041 +trainer/Q1 Predictions Std 19.1776 +trainer/Q1 Predictions Max -0.542394 +trainer/Q1 Predictions Min -90.5404 +trainer/Q2 Predictions Mean -71.3861 +trainer/Q2 Predictions Std 19.1012 +trainer/Q2 Predictions Max -0.906787 +trainer/Q2 Predictions Min -90.6995 +trainer/Q Targets Mean -71.1611 +trainer/Q Targets Std 19.3531 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.6174 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0107386 +trainer/policy/mean Std 0.725621 +trainer/policy/mean Max 0.998064 +trainer/policy/mean Min -0.999872 +trainer/policy/std Mean 0.412819 +trainer/policy/std Std 0.0207884 +trainer/policy/std Max 0.434729 +trainer/policy/std Min 0.384323 +trainer/Advantage Weights Mean 8.46545 +trainer/Advantage Weights Std 25.2487 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.8456e-15 +trainer/Advantage Score Mean -0.33347 +trainer/Advantage Score Std 0.632608 +trainer/Advantage Score Max 1.29621 +trainer/Advantage Score Min -3.3926 +trainer/V1 Predictions Mean -70.9432 +trainer/V1 Predictions Std 19.1446 +trainer/V1 Predictions Max -0.949119 +trainer/V1 Predictions Min -89.8177 +trainer/VF Loss 0.0893349 +expl/num steps total 701000 +expl/num paths total 933 +expl/path length Mean 500 +expl/path length Std 35 +expl/path length Max 535 +expl/path length Min 465 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0377442 +expl/Actions Std 0.849879 +expl/Actions Max 2.32101 +expl/Actions Min -2.28823 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 647733 +eval/num paths total 708 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.137608 +eval/Actions Std 0.721251 +eval/Actions Max 0.999787 +eval/Actions Min -0.999666 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.00099e-05 +time/evaluation sampling (s) 5.44501 +time/exploration sampling (s) 6.85799 +time/logging (s) 0.00800378 +time/saving (s) 0.0285765 +time/training (s) 19.8564 +time/epoch (s) 32.196 +time/total (s) 17454.9 +Epoch -300 +------------------------------ ---------------- +2022-05-15 22:53:56.502616 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -299 finished +------------------------------ ---------------- +epoch -299 +replay_buffer/size 999047 +trainer/num train calls 702000 +trainer/QF1 Loss 0.836805 +trainer/QF2 Loss 0.827612 +trainer/Policy Loss 8.7374 +trainer/Q1 Predictions Mean -69.7825 +trainer/Q1 Predictions Std 19.2707 +trainer/Q1 Predictions Max -2.69397 +trainer/Q1 Predictions Min -89.1073 +trainer/Q2 Predictions Mean -69.8107 +trainer/Q2 Predictions Std 19.1933 +trainer/Q2 Predictions Max -2.59253 +trainer/Q2 Predictions Min -88.9001 +trainer/Q Targets Mean -69.6341 +trainer/Q Targets Std 19.3793 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.1972 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0259915 +trainer/policy/mean Std 0.725368 +trainer/policy/mean Max 0.998314 +trainer/policy/mean Min -0.999045 +trainer/policy/std Mean 0.413343 +trainer/policy/std Std 0.0220559 +trainer/policy/std Max 0.436754 +trainer/policy/std Min 0.381863 +trainer/Advantage Weights Mean 2.02203 +trainer/Advantage Weights Std 12.9341 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.92438e-27 +trainer/Advantage Score Mean -0.681277 +trainer/Advantage Score Std 0.863311 +trainer/Advantage Score Max 2.75579 +trainer/Advantage Score Min -6.05756 +trainer/V1 Predictions Mean -69.306 +trainer/V1 Predictions Std 19.38 +trainer/V1 Predictions Max -1.6069 +trainer/V1 Predictions Min -88.2072 +trainer/VF Loss 0.164123 +expl/num steps total 702000 +expl/num paths total 935 +expl/path length Mean 500 +expl/path length Std 177 +expl/path length Max 677 +expl/path length Min 323 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00979441 +expl/Actions Std 0.833719 +expl/Actions Max 2.43653 +expl/Actions Min -2.42249 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 648733 +eval/num paths total 709 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00156833 +eval/Actions Std 0.745625 +eval/Actions Max 0.999942 +eval/Actions Min -0.99939 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.00746e-05 +time/evaluation sampling (s) 5.02489 +time/exploration sampling (s) 7.10185 +time/logging (s) 0.00800844 +time/saving (s) 0.0179535 +time/training (s) 20.1377 +time/epoch (s) 32.2904 +time/total (s) 17487.2 +Epoch -299 +------------------------------ ---------------- +2022-05-15 22:54:27.902749 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -298 finished +------------------------------ ---------------- +epoch -298 +replay_buffer/size 999047 +trainer/num train calls 703000 +trainer/QF1 Loss 0.751144 +trainer/QF2 Loss 0.692314 +trainer/Policy Loss 20.3735 +trainer/Q1 Predictions Mean -72.6202 +trainer/Q1 Predictions Std 15.7548 +trainer/Q1 Predictions Max -0.426415 +trainer/Q1 Predictions Min -86.3963 +trainer/Q2 Predictions Mean -72.6402 +trainer/Q2 Predictions Std 15.6937 +trainer/Q2 Predictions Max -0.29725 +trainer/Q2 Predictions Min -86.1574 +trainer/Q Targets Mean -72.7338 +trainer/Q Targets Std 15.657 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4453 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0195617 +trainer/policy/mean Std 0.725396 +trainer/policy/mean Max 0.999913 +trainer/policy/mean Min -0.99887 +trainer/policy/std Mean 0.413513 +trainer/policy/std Std 0.0222485 +trainer/policy/std Max 0.437794 +trainer/policy/std Min 0.382444 +trainer/Advantage Weights Mean 5.01023 +trainer/Advantage Weights Std 19.0967 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.31564e-15 +trainer/Advantage Score Mean -0.395563 +trainer/Advantage Score Std 0.55955 +trainer/Advantage Score Max 1.59902 +trainer/Advantage Score Min -3.42645 +trainer/V1 Predictions Mean -72.5059 +trainer/V1 Predictions Std 15.6652 +trainer/V1 Predictions Max 0.744013 +trainer/V1 Predictions Min -86.3567 +trainer/VF Loss 0.0705199 +expl/num steps total 703000 +expl/num paths total 937 +expl/path length Mean 500 +expl/path length Std 83 +expl/path length Max 583 +expl/path length Min 417 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0430631 +expl/Actions Std 0.835469 +expl/Actions Max 2.47426 +expl/Actions Min -2.17151 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 649733 +eval/num paths total 710 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.110513 +eval/Actions Std 0.787876 +eval/Actions Max 0.999874 +eval/Actions Min -0.999165 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.1038e-05 +time/evaluation sampling (s) 5.30305 +time/exploration sampling (s) 6.45152 +time/logging (s) 0.0114164 +time/saving (s) 0.0159844 +time/training (s) 19.6089 +time/epoch (s) 31.3909 +time/total (s) 17518.6 +Epoch -298 +------------------------------ ---------------- +2022-05-15 22:54:59.213229 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -297 finished +------------------------------ ---------------- +epoch -297 +replay_buffer/size 999047 +trainer/num train calls 704000 +trainer/QF1 Loss 0.743032 +trainer/QF2 Loss 0.697557 +trainer/Policy Loss 15.52 +trainer/Q1 Predictions Mean -71.2278 +trainer/Q1 Predictions Std 18.122 +trainer/Q1 Predictions Max -1.28625 +trainer/Q1 Predictions Min -87.1088 +trainer/Q2 Predictions Mean -71.2316 +trainer/Q2 Predictions Std 18.1448 +trainer/Q2 Predictions Max -1.60994 +trainer/Q2 Predictions Min -86.6052 +trainer/Q Targets Mean -71.3418 +trainer/Q Targets Std 18.1562 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9217 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00345625 +trainer/policy/mean Std 0.715755 +trainer/policy/mean Max 0.99923 +trainer/policy/mean Min -0.999406 +trainer/policy/std Mean 0.413573 +trainer/policy/std Std 0.0210898 +trainer/policy/std Max 0.434305 +trainer/policy/std Min 0.381483 +trainer/Advantage Weights Mean 3.47146 +trainer/Advantage Weights Std 15.5923 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.13739e-17 +trainer/Advantage Score Mean -0.458152 +trainer/Advantage Score Std 0.608019 +trainer/Advantage Score Max 1.24216 +trainer/Advantage Score Min -3.69316 +trainer/V1 Predictions Mean -71.0515 +trainer/V1 Predictions Std 18.3294 +trainer/V1 Predictions Max -0.43484 +trainer/V1 Predictions Min -86.7652 +trainer/VF Loss 0.072177 +expl/num steps total 704000 +expl/num paths total 939 +expl/path length Mean 500 +expl/path length Std 242 +expl/path length Max 742 +expl/path length Min 258 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0147525 +expl/Actions Std 0.831803 +expl/Actions Max 2.16637 +expl/Actions Min -2.18189 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 650486 +eval/num paths total 711 +eval/path length Mean 753 +eval/path length Std 0 +eval/path length Max 753 +eval/path length Min 753 +eval/Rewards Mean 0.00132802 +eval/Rewards Std 0.0364178 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0261097 +eval/Actions Std 0.745167 +eval/Actions Max 0.999878 +eval/Actions Min -0.999887 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.06615e-06 +time/evaluation sampling (s) 5.42904 +time/exploration sampling (s) 6.11537 +time/logging (s) 0.0114863 +time/saving (s) 0.0207895 +time/training (s) 19.718 +time/epoch (s) 31.2947 +time/total (s) 17549.9 +Epoch -297 +------------------------------ ---------------- +2022-05-15 22:55:31.569025 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -296 finished +------------------------------ ---------------- +epoch -296 +replay_buffer/size 999047 +trainer/num train calls 705000 +trainer/QF1 Loss 0.805607 +trainer/QF2 Loss 0.911324 +trainer/Policy Loss 17.2931 +trainer/Q1 Predictions Mean -72.3885 +trainer/Q1 Predictions Std 18.0459 +trainer/Q1 Predictions Max -0.98065 +trainer/Q1 Predictions Min -86.1852 +trainer/Q2 Predictions Mean -72.4631 +trainer/Q2 Predictions Std 18.0159 +trainer/Q2 Predictions Max -1.86144 +trainer/Q2 Predictions Min -86.304 +trainer/Q Targets Mean -72.2454 +trainer/Q Targets Std 17.9526 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0748 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0225086 +trainer/policy/mean Std 0.732894 +trainer/policy/mean Max 0.999764 +trainer/policy/mean Min -0.999633 +trainer/policy/std Mean 0.413102 +trainer/policy/std Std 0.0206176 +trainer/policy/std Max 0.435238 +trainer/policy/std Min 0.380825 +trainer/Advantage Weights Mean 4.41213 +trainer/Advantage Weights Std 18.2186 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.57484e-14 +trainer/Advantage Score Mean -0.39026 +trainer/Advantage Score Std 0.532526 +trainer/Advantage Score Max 1.47654 +trainer/Advantage Score Min -3.1782 +trainer/V1 Predictions Mean -71.9594 +trainer/V1 Predictions Std 18.0335 +trainer/V1 Predictions Max -1.26645 +trainer/V1 Predictions Min -86.008 +trainer/VF Loss 0.0626958 +expl/num steps total 705000 +expl/num paths total 941 +expl/path length Mean 500 +expl/path length Std 68 +expl/path length Max 568 +expl/path length Min 432 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.073101 +expl/Actions Std 0.830095 +expl/Actions Max 2.33918 +expl/Actions Min -2.38608 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 651388 +eval/num paths total 712 +eval/path length Mean 902 +eval/path length Std 0 +eval/path length Max 902 +eval/path length Min 902 +eval/Rewards Mean 0.00110865 +eval/Rewards Std 0.0332779 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0380215 +eval/Actions Std 0.736335 +eval/Actions Max 0.999971 +eval/Actions Min -0.999623 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.09542e-05 +time/evaluation sampling (s) 5.4829 +time/exploration sampling (s) 6.80495 +time/logging (s) 0.0110991 +time/saving (s) 0.0161565 +time/training (s) 20.0211 +time/epoch (s) 32.3362 +time/total (s) 17582.2 +Epoch -296 +------------------------------ ---------------- +2022-05-15 22:56:03.385258 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -295 finished +------------------------------ ---------------- +epoch -295 +replay_buffer/size 999047 +trainer/num train calls 706000 +trainer/QF1 Loss 0.641565 +trainer/QF2 Loss 0.631066 +trainer/Policy Loss 10.5322 +trainer/Q1 Predictions Mean -70.5286 +trainer/Q1 Predictions Std 19.2414 +trainer/Q1 Predictions Max -0.661327 +trainer/Q1 Predictions Min -88.2314 +trainer/Q2 Predictions Mean -70.462 +trainer/Q2 Predictions Std 19.1513 +trainer/Q2 Predictions Max 0.553919 +trainer/Q2 Predictions Min -88.3107 +trainer/Q Targets Mean -70.5096 +trainer/Q Targets Std 19.2717 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.3646 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.018341 +trainer/policy/mean Std 0.717134 +trainer/policy/mean Max 0.999541 +trainer/policy/mean Min -0.999569 +trainer/policy/std Mean 0.414036 +trainer/policy/std Std 0.0196266 +trainer/policy/std Max 0.438361 +trainer/policy/std Min 0.3844 +trainer/Advantage Weights Mean 2.91824 +trainer/Advantage Weights Std 15.7546 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3724e-14 +trainer/Advantage Score Mean -0.616231 +trainer/Advantage Score Std 0.589946 +trainer/Advantage Score Max 0.828476 +trainer/Advantage Score Min -3.19196 +trainer/V1 Predictions Mean -70.2213 +trainer/V1 Predictions Std 19.4296 +trainer/V1 Predictions Max 0.0271494 +trainer/V1 Predictions Min -88.2823 +trainer/VF Loss 0.0815369 +expl/num steps total 706000 +expl/num paths total 942 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0805143 +expl/Actions Std 0.822894 +expl/Actions Max 2.47676 +expl/Actions Min -2.52803 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 652388 +eval/num paths total 713 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.212323 +eval/Actions Std 0.61667 +eval/Actions Max 0.999737 +eval/Actions Min -0.999914 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.13081e-05 +time/evaluation sampling (s) 4.96697 +time/exploration sampling (s) 7.30016 +time/logging (s) 0.0165655 +time/saving (s) 0.0188458 +time/training (s) 19.502 +time/epoch (s) 31.8046 +time/total (s) 17614 +Epoch -295 +------------------------------ ---------------- +2022-05-15 22:56:35.412375 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -294 finished +------------------------------ ---------------- +epoch -294 +replay_buffer/size 999047 +trainer/num train calls 707000 +trainer/QF1 Loss 0.432104 +trainer/QF2 Loss 0.498288 +trainer/Policy Loss 15.3428 +trainer/Q1 Predictions Mean -71.8993 +trainer/Q1 Predictions Std 18.0773 +trainer/Q1 Predictions Max -2.63213 +trainer/Q1 Predictions Min -86.8481 +trainer/Q2 Predictions Mean -71.8687 +trainer/Q2 Predictions Std 18.1193 +trainer/Q2 Predictions Max -3.64639 +trainer/Q2 Predictions Min -86.8408 +trainer/Q Targets Mean -72.0325 +trainer/Q Targets Std 18.0566 +trainer/Q Targets Max -3.31062 +trainer/Q Targets Min -86.7176 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0132121 +trainer/policy/mean Std 0.736937 +trainer/policy/mean Max 0.998906 +trainer/policy/mean Min -0.998414 +trainer/policy/std Mean 0.413988 +trainer/policy/std Std 0.0191947 +trainer/policy/std Max 0.435415 +trainer/policy/std Min 0.384729 +trainer/Advantage Weights Mean 4.77805 +trainer/Advantage Weights Std 19.65 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.34305e-11 +trainer/Advantage Score Mean -0.387962 +trainer/Advantage Score Std 0.571027 +trainer/Advantage Score Max 2.18112 +trainer/Advantage Score Min -2.50335 +trainer/V1 Predictions Mean -71.7342 +trainer/V1 Predictions Std 18.2729 +trainer/V1 Predictions Max -1.43102 +trainer/V1 Predictions Min -86.6382 +trainer/VF Loss 0.0854067 +expl/num steps total 707000 +expl/num paths total 943 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.030879 +expl/Actions Std 0.821033 +expl/Actions Max 2.37837 +expl/Actions Min -2.34346 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 653008 +eval/num paths total 714 +eval/path length Mean 620 +eval/path length Std 0 +eval/path length Max 620 +eval/path length Min 620 +eval/Rewards Mean 0.0016129 +eval/Rewards Std 0.0401286 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00508654 +eval/Actions Std 0.742897 +eval/Actions Max 0.999974 +eval/Actions Min -0.999488 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.81982e-06 +time/evaluation sampling (s) 5.24709 +time/exploration sampling (s) 6.85576 +time/logging (s) 0.00942846 +time/saving (s) 0.016669 +time/training (s) 19.8745 +time/epoch (s) 32.0034 +time/total (s) 17646 +Epoch -294 +------------------------------ ---------------- +2022-05-15 22:57:06.619815 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -293 finished +------------------------------ ---------------- +epoch -293 +replay_buffer/size 999047 +trainer/num train calls 708000 +trainer/QF1 Loss 0.612327 +trainer/QF2 Loss 0.54975 +trainer/Policy Loss 16.4274 +trainer/Q1 Predictions Mean -72.4807 +trainer/Q1 Predictions Std 17.796 +trainer/Q1 Predictions Max -3.07018 +trainer/Q1 Predictions Min -89.2762 +trainer/Q2 Predictions Mean -72.4375 +trainer/Q2 Predictions Std 17.772 +trainer/Q2 Predictions Max -3.42879 +trainer/Q2 Predictions Min -89.0411 +trainer/Q Targets Mean -72.2723 +trainer/Q Targets Std 17.752 +trainer/Q Targets Max -2.94168 +trainer/Q Targets Min -88.9706 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0100162 +trainer/policy/mean Std 0.721855 +trainer/policy/mean Max 0.997672 +trainer/policy/mean Min -0.997811 +trainer/policy/std Mean 0.413859 +trainer/policy/std Std 0.0193182 +trainer/policy/std Max 0.434701 +trainer/policy/std Min 0.384157 +trainer/Advantage Weights Mean 3.6775 +trainer/Advantage Weights Std 17.5207 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.31937e-17 +trainer/Advantage Score Mean -0.511531 +trainer/Advantage Score Std 0.683192 +trainer/Advantage Score Max 1.37584 +trainer/Advantage Score Min -3.83026 +trainer/V1 Predictions Mean -72.0022 +trainer/V1 Predictions Std 17.9213 +trainer/V1 Predictions Max -2.31416 +trainer/V1 Predictions Min -88.7127 +trainer/VF Loss 0.0904643 +expl/num steps total 708000 +expl/num paths total 944 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0826513 +expl/Actions Std 0.826317 +expl/Actions Max 2.52285 +expl/Actions Min -2.29554 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 654008 +eval/num paths total 715 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.136835 +eval/Actions Std 0.716835 +eval/Actions Max 0.999595 +eval/Actions Min -0.999456 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.00072e-06 +time/evaluation sampling (s) 5.26583 +time/exploration sampling (s) 6.50216 +time/logging (s) 0.00901038 +time/saving (s) 0.0132812 +time/training (s) 19.3985 +time/epoch (s) 31.1888 +time/total (s) 17677.2 +Epoch -293 +------------------------------ ---------------- +2022-05-15 22:57:37.808252 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -292 finished +------------------------------ ---------------- +epoch -292 +replay_buffer/size 999047 +trainer/num train calls 709000 +trainer/QF1 Loss 0.719367 +trainer/QF2 Loss 0.861422 +trainer/Policy Loss 22.4159 +trainer/Q1 Predictions Mean -70.4905 +trainer/Q1 Predictions Std 18.5705 +trainer/Q1 Predictions Max 0.21739 +trainer/Q1 Predictions Min -85.6825 +trainer/Q2 Predictions Mean -70.4897 +trainer/Q2 Predictions Std 18.4126 +trainer/Q2 Predictions Max -0.455184 +trainer/Q2 Predictions Min -85.4557 +trainer/Q Targets Mean -70.3336 +trainer/Q Targets Std 18.4776 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.442 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.010838 +trainer/policy/mean Std 0.718685 +trainer/policy/mean Max 0.998334 +trainer/policy/mean Min -0.999323 +trainer/policy/std Mean 0.414027 +trainer/policy/std Std 0.0205598 +trainer/policy/std Max 0.437476 +trainer/policy/std Min 0.381982 +trainer/Advantage Weights Mean 4.56083 +trainer/Advantage Weights Std 18.3446 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.1517e-16 +trainer/Advantage Score Mean -0.46754 +trainer/Advantage Score Std 0.703375 +trainer/Advantage Score Max 2.1424 +trainer/Advantage Score Min -3.54178 +trainer/V1 Predictions Mean -70.039 +trainer/V1 Predictions Std 18.7258 +trainer/V1 Predictions Max -0.153101 +trainer/V1 Predictions Min -85.2448 +trainer/VF Loss 0.105582 +expl/num steps total 709000 +expl/num paths total 946 +expl/path length Mean 500 +expl/path length Std 2 +expl/path length Max 502 +expl/path length Min 498 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0212547 +expl/Actions Std 0.832684 +expl/Actions Max 2.50163 +expl/Actions Min -2.36305 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 655008 +eval/num paths total 716 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0174387 +eval/Actions Std 0.749346 +eval/Actions Max 0.999662 +eval/Actions Min -0.999764 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.79002e-06 +time/evaluation sampling (s) 5.02422 +time/exploration sampling (s) 6.74255 +time/logging (s) 0.0257589 +time/saving (s) 0.0199245 +time/training (s) 19.3767 +time/epoch (s) 31.1891 +time/total (s) 17708.4 +Epoch -292 +------------------------------ ---------------- +2022-05-15 22:58:09.799719 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -291 finished +------------------------------ ---------------- +epoch -291 +replay_buffer/size 999047 +trainer/num train calls 710000 +trainer/QF1 Loss 0.844542 +trainer/QF2 Loss 0.826911 +trainer/Policy Loss 35.4818 +trainer/Q1 Predictions Mean -70.7048 +trainer/Q1 Predictions Std 18.4239 +trainer/Q1 Predictions Max -2.18624 +trainer/Q1 Predictions Min -85.9353 +trainer/Q2 Predictions Mean -70.6296 +trainer/Q2 Predictions Std 18.481 +trainer/Q2 Predictions Max -2.19516 +trainer/Q2 Predictions Min -85.8918 +trainer/Q Targets Mean -70.7662 +trainer/Q Targets Std 18.6457 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0358 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0130402 +trainer/policy/mean Std 0.728452 +trainer/policy/mean Max 0.998969 +trainer/policy/mean Min -0.99731 +trainer/policy/std Mean 0.414223 +trainer/policy/std Std 0.0199101 +trainer/policy/std Max 0.43701 +trainer/policy/std Min 0.383119 +trainer/Advantage Weights Mean 10.8565 +trainer/Advantage Weights Std 27.5543 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.05866e-19 +trainer/Advantage Score Mean -0.277313 +trainer/Advantage Score Std 0.742552 +trainer/Advantage Score Max 1.56361 +trainer/Advantage Score Min -4.36921 +trainer/V1 Predictions Mean -70.5451 +trainer/V1 Predictions Std 18.7044 +trainer/V1 Predictions Max -1.24884 +trainer/V1 Predictions Min -85.8477 +trainer/VF Loss 0.109277 +expl/num steps total 710000 +expl/num paths total 948 +expl/path length Mean 500 +expl/path length Std 120 +expl/path length Max 620 +expl/path length Min 380 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0108979 +expl/Actions Std 0.838494 +expl/Actions Max 2.55723 +expl/Actions Min -2.5725 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 656008 +eval/num paths total 717 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.022694 +eval/Actions Std 0.740395 +eval/Actions Max 0.999978 +eval/Actions Min -0.999548 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.93903e-06 +time/evaluation sampling (s) 5.38736 +time/exploration sampling (s) 6.46632 +time/logging (s) 0.012763 +time/saving (s) 0.0185157 +time/training (s) 20.0682 +time/epoch (s) 31.9532 +time/total (s) 17740.4 +Epoch -291 +------------------------------ ---------------- +2022-05-15 22:58:41.620162 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -290 finished +------------------------------ ---------------- +epoch -290 +replay_buffer/size 999047 +trainer/num train calls 711000 +trainer/QF1 Loss 1.14211 +trainer/QF2 Loss 1.1511 +trainer/Policy Loss 14.5227 +trainer/Q1 Predictions Mean -71.3919 +trainer/Q1 Predictions Std 16.6775 +trainer/Q1 Predictions Max -4.67163 +trainer/Q1 Predictions Min -86.3478 +trainer/Q2 Predictions Mean -71.4719 +trainer/Q2 Predictions Std 16.6883 +trainer/Q2 Predictions Max -4.54783 +trainer/Q2 Predictions Min -86.635 +trainer/Q Targets Mean -71.6326 +trainer/Q Targets Std 16.9781 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0227 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0168741 +trainer/policy/mean Std 0.725221 +trainer/policy/mean Max 0.9996 +trainer/policy/mean Min -0.999011 +trainer/policy/std Mean 0.413731 +trainer/policy/std Std 0.020224 +trainer/policy/std Max 0.437312 +trainer/policy/std Min 0.383821 +trainer/Advantage Weights Mean 4.86224 +trainer/Advantage Weights Std 17.734 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.39028e-11 +trainer/Advantage Score Mean -0.347422 +trainer/Advantage Score Std 0.613008 +trainer/Advantage Score Max 2.99779 +trainer/Advantage Score Min -2.49989 +trainer/V1 Predictions Mean -71.3805 +trainer/V1 Predictions Std 16.9674 +trainer/V1 Predictions Max -2.88134 +trainer/V1 Predictions Min -86.8913 +trainer/VF Loss 0.0916702 +expl/num steps total 711000 +expl/num paths total 950 +expl/path length Mean 500 +expl/path length Std 116 +expl/path length Max 616 +expl/path length Min 384 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0212264 +expl/Actions Std 0.833131 +expl/Actions Max 2.45072 +expl/Actions Min -2.36423 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 657008 +eval/num paths total 718 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0328534 +eval/Actions Std 0.733698 +eval/Actions Max 0.999901 +eval/Actions Min -0.999789 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.09291e-05 +time/evaluation sampling (s) 5.55041 +time/exploration sampling (s) 6.28929 +time/logging (s) 0.00956658 +time/saving (s) 0.0154526 +time/training (s) 19.9328 +time/epoch (s) 31.7975 +time/total (s) 17772.2 +Epoch -290 +------------------------------ ---------------- +2022-05-15 22:59:14.732527 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -289 finished +------------------------------ ---------------- +epoch -289 +replay_buffer/size 999047 +trainer/num train calls 712000 +trainer/QF1 Loss 0.581126 +trainer/QF2 Loss 0.546524 +trainer/Policy Loss 33.6217 +trainer/Q1 Predictions Mean -73.2391 +trainer/Q1 Predictions Std 16.8371 +trainer/Q1 Predictions Max -2.1332 +trainer/Q1 Predictions Min -86.103 +trainer/Q2 Predictions Mean -73.3465 +trainer/Q2 Predictions Std 16.8231 +trainer/Q2 Predictions Max -1.94512 +trainer/Q2 Predictions Min -86.0208 +trainer/Q Targets Mean -73.458 +trainer/Q Targets Std 16.9428 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0794 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00334572 +trainer/policy/mean Std 0.73161 +trainer/policy/mean Max 0.998667 +trainer/policy/mean Min -0.999215 +trainer/policy/std Mean 0.412839 +trainer/policy/std Std 0.0198676 +trainer/policy/std Max 0.435431 +trainer/policy/std Min 0.384239 +trainer/Advantage Weights Mean 7.88306 +trainer/Advantage Weights Std 22.4767 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.59134e-38 +trainer/Advantage Score Mean -0.341807 +trainer/Advantage Score Std 0.922017 +trainer/Advantage Score Max 0.993706 +trainer/Advantage Score Min -8.65461 +trainer/V1 Predictions Mean -73.1262 +trainer/V1 Predictions Std 17.0866 +trainer/V1 Predictions Max -2.84153 +trainer/V1 Predictions Min -85.9526 +trainer/VF Loss 0.124918 +expl/num steps total 712000 +expl/num paths total 952 +expl/path length Mean 500 +expl/path length Std 374 +expl/path length Max 874 +expl/path length Min 126 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0397291 +expl/Actions Std 0.825429 +expl/Actions Max 2.81946 +expl/Actions Min -2.22174 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 658008 +eval/num paths total 719 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0327021 +eval/Actions Std 0.72796 +eval/Actions Max 0.99973 +eval/Actions Min -0.99955 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.31829e-05 +time/evaluation sampling (s) 5.75228 +time/exploration sampling (s) 7.25747 +time/logging (s) 0.0125049 +time/saving (s) 0.0169134 +time/training (s) 20.06 +time/epoch (s) 33.0991 +time/total (s) 17805.3 +Epoch -289 +------------------------------ ---------------- +2022-05-15 22:59:46.254820 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -288 finished +------------------------------ ---------------- +epoch -288 +replay_buffer/size 999047 +trainer/num train calls 713000 +trainer/QF1 Loss 0.699547 +trainer/QF2 Loss 0.705 +trainer/Policy Loss 14.3839 +trainer/Q1 Predictions Mean -72.7787 +trainer/Q1 Predictions Std 16.3834 +trainer/Q1 Predictions Max -1.7455 +trainer/Q1 Predictions Min -87.4337 +trainer/Q2 Predictions Mean -72.8646 +trainer/Q2 Predictions Std 16.3395 +trainer/Q2 Predictions Max -1.45899 +trainer/Q2 Predictions Min -87.6727 +trainer/Q Targets Mean -72.6822 +trainer/Q Targets Std 16.0875 +trainer/Q Targets Max -2.91018 +trainer/Q Targets Min -86.6 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.010864 +trainer/policy/mean Std 0.725951 +trainer/policy/mean Max 0.99927 +trainer/policy/mean Min -0.999756 +trainer/policy/std Mean 0.411713 +trainer/policy/std Std 0.0218521 +trainer/policy/std Max 0.433284 +trainer/policy/std Min 0.375652 +trainer/Advantage Weights Mean 4.83339 +trainer/Advantage Weights Std 18.6319 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.56833e-15 +trainer/Advantage Score Mean -0.427427 +trainer/Advantage Score Std 0.59971 +trainer/Advantage Score Max 1.42577 +trainer/Advantage Score Min -3.32667 +trainer/V1 Predictions Mean -72.3877 +trainer/V1 Predictions Std 16.3114 +trainer/V1 Predictions Max -1.8764 +trainer/V1 Predictions Min -86.49 +trainer/VF Loss 0.0774194 +expl/num steps total 713000 +expl/num paths total 954 +expl/path length Mean 500 +expl/path length Std 273 +expl/path length Max 773 +expl/path length Min 227 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0886522 +expl/Actions Std 0.82558 +expl/Actions Max 2.51692 +expl/Actions Min -2.25374 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 659008 +eval/num paths total 720 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.181038 +eval/Actions Std 0.694862 +eval/Actions Max 0.999685 +eval/Actions Min -0.999929 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.3232e-06 +time/evaluation sampling (s) 5.17845 +time/exploration sampling (s) 6.17085 +time/logging (s) 0.0121603 +time/saving (s) 0.0184197 +time/training (s) 20.1242 +time/epoch (s) 31.5041 +time/total (s) 17836.8 +Epoch -288 +------------------------------ ---------------- +2022-05-15 23:00:18.633272 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -287 finished +------------------------------ ---------------- +epoch -287 +replay_buffer/size 999047 +trainer/num train calls 714000 +trainer/QF1 Loss 0.806093 +trainer/QF2 Loss 0.939051 +trainer/Policy Loss 24.776 +trainer/Q1 Predictions Mean -71.4552 +trainer/Q1 Predictions Std 19.2106 +trainer/Q1 Predictions Max -0.23382 +trainer/Q1 Predictions Min -87.1388 +trainer/Q2 Predictions Mean -71.5495 +trainer/Q2 Predictions Std 19.2619 +trainer/Q2 Predictions Max -0.416868 +trainer/Q2 Predictions Min -87.2939 +trainer/Q Targets Mean -71.5491 +trainer/Q Targets Std 19.4886 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5277 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0175358 +trainer/policy/mean Std 0.727759 +trainer/policy/mean Max 0.998895 +trainer/policy/mean Min -0.99848 +trainer/policy/std Mean 0.41425 +trainer/policy/std Std 0.020681 +trainer/policy/std Max 0.434739 +trainer/policy/std Min 0.382744 +trainer/Advantage Weights Mean 5.04997 +trainer/Advantage Weights Std 19.1299 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.06662e-20 +trainer/Advantage Score Mean -0.396732 +trainer/Advantage Score Std 0.67387 +trainer/Advantage Score Max 1.50817 +trainer/Advantage Score Min -4.49311 +trainer/V1 Predictions Mean -71.3726 +trainer/V1 Predictions Std 19.3336 +trainer/V1 Predictions Max 0.585491 +trainer/V1 Predictions Min -86.6065 +trainer/VF Loss 0.0992317 +expl/num steps total 714000 +expl/num paths total 955 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.127178 +expl/Actions Std 0.880378 +expl/Actions Max 2.27246 +expl/Actions Min -2.6583 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 659614 +eval/num paths total 721 +eval/path length Mean 606 +eval/path length Std 0 +eval/path length Max 606 +eval/path length Min 606 +eval/Rewards Mean 0.00165017 +eval/Rewards Std 0.0405887 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0201955 +eval/Actions Std 0.740337 +eval/Actions Max 0.999736 +eval/Actions Min -0.999944 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.41587e-06 +time/evaluation sampling (s) 5.26415 +time/exploration sampling (s) 6.72366 +time/logging (s) 0.00976831 +time/saving (s) 0.0168295 +time/training (s) 20.3416 +time/epoch (s) 32.356 +time/total (s) 17869.2 +Epoch -287 +------------------------------ ---------------- +2022-05-15 23:00:50.553596 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -286 finished +------------------------------ ---------------- +epoch -286 +replay_buffer/size 999047 +trainer/num train calls 715000 +trainer/QF1 Loss 1.06612 +trainer/QF2 Loss 1.08412 +trainer/Policy Loss 63.9202 +trainer/Q1 Predictions Mean -70.1862 +trainer/Q1 Predictions Std 19.0902 +trainer/Q1 Predictions Max -1.12098 +trainer/Q1 Predictions Min -85.4114 +trainer/Q2 Predictions Mean -70.2733 +trainer/Q2 Predictions Std 19.2143 +trainer/Q2 Predictions Max -1.38307 +trainer/Q2 Predictions Min -85.9678 +trainer/Q Targets Mean -70.6983 +trainer/Q Targets Std 18.7541 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.329 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00557731 +trainer/policy/mean Std 0.722401 +trainer/policy/mean Max 0.999108 +trainer/policy/mean Min -0.999901 +trainer/policy/std Mean 0.41331 +trainer/policy/std Std 0.0214101 +trainer/policy/std Max 0.436011 +trainer/policy/std Min 0.38116 +trainer/Advantage Weights Mean 13.4429 +trainer/Advantage Weights Std 32.0263 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.08215e-16 +trainer/Advantage Score Mean -0.252044 +trainer/Advantage Score Std 0.641287 +trainer/Advantage Score Max 1.8324 +trainer/Advantage Score Min -3.57157 +trainer/V1 Predictions Mean -70.4303 +trainer/V1 Predictions Std 18.8643 +trainer/V1 Predictions Max -2.11813 +trainer/V1 Predictions Min -86.0845 +trainer/VF Loss 0.123909 +expl/num steps total 715000 +expl/num paths total 956 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0552786 +expl/Actions Std 0.829738 +expl/Actions Max 2.05837 +expl/Actions Min -2.25144 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 660567 +eval/num paths total 723 +eval/path length Mean 476.5 +eval/path length Std 18.5 +eval/path length Max 495 +eval/path length Min 458 +eval/Rewards Mean 0.00209864 +eval/Rewards Std 0.0457628 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0248654 +eval/Actions Std 0.7384 +eval/Actions Max 0.999786 +eval/Actions Min -0.999878 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 4.09968e-06 +time/evaluation sampling (s) 5.13683 +time/exploration sampling (s) 7.19367 +time/logging (s) 0.0131605 +time/saving (s) 0.0194441 +time/training (s) 19.5428 +time/epoch (s) 31.9059 +time/total (s) 17901.1 +Epoch -286 +------------------------------ ---------------- +2022-05-15 23:01:21.582251 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -285 finished +------------------------------ ---------------- +epoch -285 +replay_buffer/size 999047 +trainer/num train calls 716000 +trainer/QF1 Loss 0.739467 +trainer/QF2 Loss 0.815658 +trainer/Policy Loss 13.2249 +trainer/Q1 Predictions Mean -72.8439 +trainer/Q1 Predictions Std 16.3969 +trainer/Q1 Predictions Max -1.99022 +trainer/Q1 Predictions Min -86.6961 +trainer/Q2 Predictions Mean -72.8512 +trainer/Q2 Predictions Std 16.3173 +trainer/Q2 Predictions Max -2.59816 +trainer/Q2 Predictions Min -86.6734 +trainer/Q Targets Mean -72.6216 +trainer/Q Targets Std 16.4308 +trainer/Q Targets Max -1.06178 +trainer/Q Targets Min -86.5068 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0158058 +trainer/policy/mean Std 0.726151 +trainer/policy/mean Max 0.99969 +trainer/policy/mean Min -0.998566 +trainer/policy/std Mean 0.414007 +trainer/policy/std Std 0.0213611 +trainer/policy/std Max 0.435559 +trainer/policy/std Min 0.382886 +trainer/Advantage Weights Mean 2.94548 +trainer/Advantage Weights Std 14.689 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.27415e-13 +trainer/Advantage Score Mean -0.534135 +trainer/Advantage Score Std 0.562242 +trainer/Advantage Score Max 0.592995 +trainer/Advantage Score Min -2.77064 +trainer/V1 Predictions Mean -72.3828 +trainer/V1 Predictions Std 16.4771 +trainer/V1 Predictions Max -1.75344 +trainer/V1 Predictions Min -86.1989 +trainer/VF Loss 0.067565 +expl/num steps total 716000 +expl/num paths total 958 +expl/path length Mean 500 +expl/path length Std 156 +expl/path length Max 656 +expl/path length Min 344 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0376214 +expl/Actions Std 0.840241 +expl/Actions Max 2.48891 +expl/Actions Min -2.37323 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 661047 +eval/num paths total 724 +eval/path length Mean 480 +eval/path length Std 0 +eval/path length Max 480 +eval/path length Min 480 +eval/Rewards Mean 0.00208333 +eval/Rewards Std 0.045596 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.037856 +eval/Actions Std 0.747197 +eval/Actions Max 0.999975 +eval/Actions Min -0.999926 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.09598e-05 +time/evaluation sampling (s) 5.17116 +time/exploration sampling (s) 6.41678 +time/logging (s) 0.0101138 +time/saving (s) 0.0169394 +time/training (s) 19.3896 +time/epoch (s) 31.0046 +time/total (s) 17932.1 +Epoch -285 +------------------------------ ---------------- +2022-05-15 23:01:53.289116 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -284 finished +------------------------------ ---------------- +epoch -284 +replay_buffer/size 999047 +trainer/num train calls 717000 +trainer/QF1 Loss 0.545005 +trainer/QF2 Loss 0.647488 +trainer/Policy Loss 15.0843 +trainer/Q1 Predictions Mean -72.9784 +trainer/Q1 Predictions Std 18.1616 +trainer/Q1 Predictions Max -0.293468 +trainer/Q1 Predictions Min -89.1464 +trainer/Q2 Predictions Mean -72.975 +trainer/Q2 Predictions Std 18.1526 +trainer/Q2 Predictions Max -0.108931 +trainer/Q2 Predictions Min -88.8566 +trainer/Q Targets Mean -72.7004 +trainer/Q Targets Std 17.873 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5121 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00846571 +trainer/policy/mean Std 0.718003 +trainer/policy/mean Max 0.998538 +trainer/policy/mean Min -0.999011 +trainer/policy/std Mean 0.413585 +trainer/policy/std Std 0.0204653 +trainer/policy/std Max 0.43419 +trainer/policy/std Min 0.381582 +trainer/Advantage Weights Mean 3.83843 +trainer/Advantage Weights Std 17.3198 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.71838e-13 +trainer/Advantage Score Mean -0.513374 +trainer/Advantage Score Std 0.540135 +trainer/Advantage Score Max 1.42681 +trainer/Advantage Score Min -2.77682 +trainer/V1 Predictions Mean -72.4339 +trainer/V1 Predictions Std 18.0143 +trainer/V1 Predictions Max 0.0403014 +trainer/V1 Predictions Min -88.4328 +trainer/VF Loss 0.0812664 +expl/num steps total 717000 +expl/num paths total 959 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.175042 +expl/Actions Std 0.871363 +expl/Actions Max 2.44259 +expl/Actions Min -2.48571 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 661602 +eval/num paths total 725 +eval/path length Mean 555 +eval/path length Std 0 +eval/path length Max 555 +eval/path length Min 555 +eval/Rewards Mean 0.0018018 +eval/Rewards Std 0.0424094 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0184641 +eval/Actions Std 0.738463 +eval/Actions Max 0.999538 +eval/Actions Min -0.999179 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.23495e-06 +time/evaluation sampling (s) 4.93897 +time/exploration sampling (s) 6.91554 +time/logging (s) 0.0103274 +time/saving (s) 0.0179476 +time/training (s) 19.8051 +time/epoch (s) 31.6879 +time/total (s) 17963.8 +Epoch -284 +------------------------------ ---------------- +2022-05-15 23:02:25.426325 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -283 finished +------------------------------ ---------------- +epoch -283 +replay_buffer/size 999047 +trainer/num train calls 718000 +trainer/QF1 Loss 0.673361 +trainer/QF2 Loss 0.70915 +trainer/Policy Loss 29.8637 +trainer/Q1 Predictions Mean -71.5975 +trainer/Q1 Predictions Std 18.5411 +trainer/Q1 Predictions Max -0.963746 +trainer/Q1 Predictions Min -86.1697 +trainer/Q2 Predictions Mean -71.5287 +trainer/Q2 Predictions Std 18.565 +trainer/Q2 Predictions Max -0.526562 +trainer/Q2 Predictions Min -85.9727 +trainer/Q Targets Mean -71.8534 +trainer/Q Targets Std 18.7664 +trainer/Q Targets Max 0.0382242 +trainer/Q Targets Min -86.198 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0213996 +trainer/policy/mean Std 0.722173 +trainer/policy/mean Max 0.99975 +trainer/policy/mean Min -0.999819 +trainer/policy/std Mean 0.411711 +trainer/policy/std Std 0.020953 +trainer/policy/std Max 0.433459 +trainer/policy/std Min 0.378848 +trainer/Advantage Weights Mean 8.7976 +trainer/Advantage Weights Std 23.1814 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.57285e-11 +trainer/Advantage Score Mean -0.221375 +trainer/Advantage Score Std 0.547718 +trainer/Advantage Score Max 1.01522 +trainer/Advantage Score Min -2.31798 +trainer/V1 Predictions Mean -71.6086 +trainer/V1 Predictions Std 18.746 +trainer/V1 Predictions Max -0.644937 +trainer/V1 Predictions Min -86.125 +trainer/VF Loss 0.0646834 +expl/num steps total 718000 +expl/num paths total 960 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00559428 +expl/Actions Std 0.819953 +expl/Actions Max 2.38028 +expl/Actions Min -2.31609 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 662602 +eval/num paths total 726 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.394358 +eval/Actions Std 0.795745 +eval/Actions Max 0.999488 +eval/Actions Min -0.9998 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.22529e-05 +time/evaluation sampling (s) 4.95363 +time/exploration sampling (s) 7.36437 +time/logging (s) 0.00868595 +time/saving (s) 0.0154326 +time/training (s) 19.7739 +time/epoch (s) 32.116 +time/total (s) 17995.9 +Epoch -283 +------------------------------ ---------------- +2022-05-15 23:02:57.947286 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -282 finished +------------------------------ ---------------- +epoch -282 +replay_buffer/size 999047 +trainer/num train calls 719000 +trainer/QF1 Loss 0.697991 +trainer/QF2 Loss 0.707796 +trainer/Policy Loss 9.13959 +trainer/Q1 Predictions Mean -71.4131 +trainer/Q1 Predictions Std 19.2814 +trainer/Q1 Predictions Max -0.527977 +trainer/Q1 Predictions Min -85.9811 +trainer/Q2 Predictions Mean -71.4543 +trainer/Q2 Predictions Std 19.2977 +trainer/Q2 Predictions Max -0.374214 +trainer/Q2 Predictions Min -86.1081 +trainer/Q Targets Mean -71.2456 +trainer/Q Targets Std 19.5286 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0699 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000415945 +trainer/policy/mean Std 0.730788 +trainer/policy/mean Max 0.999727 +trainer/policy/mean Min -0.999208 +trainer/policy/std Mean 0.412853 +trainer/policy/std Std 0.0210468 +trainer/policy/std Max 0.433853 +trainer/policy/std Min 0.379017 +trainer/Advantage Weights Mean 3.84329 +trainer/Advantage Weights Std 17.1815 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.11034e-16 +trainer/Advantage Score Mean -0.414434 +trainer/Advantage Score Std 0.555192 +trainer/Advantage Score Max 1.65244 +trainer/Advantage Score Min -3.54279 +trainer/V1 Predictions Mean -71.1422 +trainer/V1 Predictions Std 19.3716 +trainer/V1 Predictions Max -0.0490175 +trainer/V1 Predictions Min -85.9061 +trainer/VF Loss 0.0739439 +expl/num steps total 719000 +expl/num paths total 962 +expl/path length Mean 500 +expl/path length Std 190 +expl/path length Max 690 +expl/path length Min 310 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0259861 +expl/Actions Std 0.829137 +expl/Actions Max 2.50082 +expl/Actions Min -2.29784 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 663228 +eval/num paths total 727 +eval/path length Mean 626 +eval/path length Std 0 +eval/path length Max 626 +eval/path length Min 626 +eval/Rewards Mean 0.00159744 +eval/Rewards Std 0.0399361 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.039447 +eval/Actions Std 0.725287 +eval/Actions Max 0.999567 +eval/Actions Min -0.999577 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.45258e-05 +time/evaluation sampling (s) 5.71775 +time/exploration sampling (s) 7.37466 +time/logging (s) 0.00743606 +time/saving (s) 0.0115287 +time/training (s) 19.3942 +time/epoch (s) 32.5056 +time/total (s) 18028.4 +Epoch -282 +------------------------------ ---------------- +2022-05-15 23:03:29.059483 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -281 finished +------------------------------ ---------------- +epoch -281 +replay_buffer/size 999047 +trainer/num train calls 720000 +trainer/QF1 Loss 0.617553 +trainer/QF2 Loss 0.704737 +trainer/Policy Loss 24.3984 +trainer/Q1 Predictions Mean -70.8766 +trainer/Q1 Predictions Std 19.7078 +trainer/Q1 Predictions Max -0.831735 +trainer/Q1 Predictions Min -86.2662 +trainer/Q2 Predictions Mean -70.8911 +trainer/Q2 Predictions Std 19.7105 +trainer/Q2 Predictions Max -0.646441 +trainer/Q2 Predictions Min -86.2843 +trainer/Q Targets Mean -71.035 +trainer/Q Targets Std 19.9381 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7109 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0212195 +trainer/policy/mean Std 0.734264 +trainer/policy/mean Max 0.999771 +trainer/policy/mean Min -0.999172 +trainer/policy/std Mean 0.411233 +trainer/policy/std Std 0.0212155 +trainer/policy/std Max 0.43293 +trainer/policy/std Min 0.378076 +trainer/Advantage Weights Mean 6.22655 +trainer/Advantage Weights Std 20.9274 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.41157e-16 +trainer/Advantage Score Mean -0.31828 +trainer/Advantage Score Std 0.581332 +trainer/Advantage Score Max 1.45155 +trainer/Advantage Score Min -3.64967 +trainer/V1 Predictions Mean -70.8372 +trainer/V1 Predictions Std 19.9667 +trainer/V1 Predictions Max -0.548471 +trainer/V1 Predictions Min -86.7346 +trainer/VF Loss 0.0679793 +expl/num steps total 720000 +expl/num paths total 964 +expl/path length Mean 500 +expl/path length Std 123 +expl/path length Max 623 +expl/path length Min 377 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0250005 +expl/Actions Std 0.82698 +expl/Actions Max 2.13295 +expl/Actions Min -2.4965 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 663843 +eval/num paths total 728 +eval/path length Mean 615 +eval/path length Std 0 +eval/path length Max 615 +eval/path length Min 615 +eval/Rewards Mean 0.00162602 +eval/Rewards Std 0.0402911 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0476072 +eval/Actions Std 0.740048 +eval/Actions Max 0.999837 +eval/Actions Min -0.99975 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.27121e-05 +time/evaluation sampling (s) 5.13048 +time/exploration sampling (s) 6.89017 +time/logging (s) 0.0102644 +time/saving (s) 0.0178272 +time/training (s) 19.0549 +time/epoch (s) 31.1037 +time/total (s) 18059.5 +Epoch -281 +------------------------------ ---------------- +2022-05-15 23:04:00.308368 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -280 finished +------------------------------ ---------------- +epoch -280 +replay_buffer/size 999047 +trainer/num train calls 721000 +trainer/QF1 Loss 1.03465 +trainer/QF2 Loss 1.0841 +trainer/Policy Loss 13.3348 +trainer/Q1 Predictions Mean -70.8603 +trainer/Q1 Predictions Std 17.9511 +trainer/Q1 Predictions Max -0.576085 +trainer/Q1 Predictions Min -86.467 +trainer/Q2 Predictions Mean -70.8558 +trainer/Q2 Predictions Std 17.9868 +trainer/Q2 Predictions Max -0.296995 +trainer/Q2 Predictions Min -86.4037 +trainer/Q Targets Mean -70.4474 +trainer/Q Targets Std 17.9877 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.7546 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00310865 +trainer/policy/mean Std 0.735543 +trainer/policy/mean Max 0.99924 +trainer/policy/mean Min -0.999928 +trainer/policy/std Mean 0.410791 +trainer/policy/std Std 0.0195175 +trainer/policy/std Max 0.430903 +trainer/policy/std Min 0.379693 +trainer/Advantage Weights Mean 2.47148 +trainer/Advantage Weights Std 13.2515 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.90896e-16 +trainer/Advantage Score Mean -0.601699 +trainer/Advantage Score Std 0.609441 +trainer/Advantage Score Max 0.635162 +trainer/Advantage Score Min -3.61948 +trainer/V1 Predictions Mean -70.13 +trainer/V1 Predictions Std 18.1317 +trainer/V1 Predictions Max 0.589575 +trainer/V1 Predictions Min -85.5915 +trainer/VF Loss 0.0802015 +expl/num steps total 721000 +expl/num paths total 966 +expl/path length Mean 500 +expl/path length Std 81 +expl/path length Max 581 +expl/path length Min 419 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00921096 +expl/Actions Std 0.834707 +expl/Actions Max 2.149 +expl/Actions Min -2.42449 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 664843 +eval/num paths total 729 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.337735 +eval/Actions Std 0.674034 +eval/Actions Max 0.999664 +eval/Actions Min -0.999833 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.53996e-06 +time/evaluation sampling (s) 5.24472 +time/exploration sampling (s) 6.62973 +time/logging (s) 0.0121808 +time/saving (s) 0.0173852 +time/training (s) 19.3271 +time/epoch (s) 31.2311 +time/total (s) 18090.8 +Epoch -280 +------------------------------ ---------------- +2022-05-15 23:04:32.080917 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -279 finished +------------------------------ ---------------- +epoch -279 +replay_buffer/size 999047 +trainer/num train calls 722000 +trainer/QF1 Loss 0.923826 +trainer/QF2 Loss 0.867782 +trainer/Policy Loss 15.6454 +trainer/Q1 Predictions Mean -71.4695 +trainer/Q1 Predictions Std 18.3171 +trainer/Q1 Predictions Max -0.683883 +trainer/Q1 Predictions Min -86.5813 +trainer/Q2 Predictions Mean -71.4479 +trainer/Q2 Predictions Std 18.3824 +trainer/Q2 Predictions Max -0.0564134 +trainer/Q2 Predictions Min -86.6874 +trainer/Q Targets Mean -71.1004 +trainer/Q Targets Std 18.048 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1114 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0157497 +trainer/policy/mean Std 0.733668 +trainer/policy/mean Max 0.998915 +trainer/policy/mean Min -0.997947 +trainer/policy/std Mean 0.413521 +trainer/policy/std Std 0.0198463 +trainer/policy/std Max 0.433122 +trainer/policy/std Min 0.382481 +trainer/Advantage Weights Mean 5.45225 +trainer/Advantage Weights Std 20.9947 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.94223e-22 +trainer/Advantage Score Mean -0.508186 +trainer/Advantage Score Std 0.786 +trainer/Advantage Score Max 1.88955 +trainer/Advantage Score Min -4.92851 +trainer/V1 Predictions Mean -70.7638 +trainer/V1 Predictions Std 18.3091 +trainer/V1 Predictions Max 0.0660974 +trainer/V1 Predictions Min -86.1825 +trainer/VF Loss 0.119312 +expl/num steps total 722000 +expl/num paths total 968 +expl/path length Mean 500 +expl/path length Std 432 +expl/path length Max 932 +expl/path length Min 68 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0145276 +expl/Actions Std 0.820209 +expl/Actions Max 2.26204 +expl/Actions Min -2.16984 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 665843 +eval/num paths total 730 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0275018 +eval/Actions Std 0.706804 +eval/Actions Max 0.999215 +eval/Actions Min -0.999914 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.86871e-06 +time/evaluation sampling (s) 5.63263 +time/exploration sampling (s) 6.70264 +time/logging (s) 0.0125985 +time/saving (s) 0.0190934 +time/training (s) 19.386 +time/epoch (s) 31.753 +time/total (s) 18122.5 +Epoch -279 +------------------------------ ---------------- +2022-05-15 23:05:04.171168 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -278 finished +------------------------------ ---------------- +epoch -278 +replay_buffer/size 999047 +trainer/num train calls 723000 +trainer/QF1 Loss 0.907824 +trainer/QF2 Loss 0.863598 +trainer/Policy Loss 37.3012 +trainer/Q1 Predictions Mean -71.2972 +trainer/Q1 Predictions Std 17.4901 +trainer/Q1 Predictions Max -4.51269 +trainer/Q1 Predictions Min -87.2882 +trainer/Q2 Predictions Mean -71.2301 +trainer/Q2 Predictions Std 17.3935 +trainer/Q2 Predictions Max -4.25345 +trainer/Q2 Predictions Min -87.3547 +trainer/Q Targets Mean -71.6845 +trainer/Q Targets Std 17.4793 +trainer/Q Targets Max -6.06635 +trainer/Q Targets Min -87.0958 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00412481 +trainer/policy/mean Std 0.736536 +trainer/policy/mean Max 0.998759 +trainer/policy/mean Min -0.999544 +trainer/policy/std Mean 0.414526 +trainer/policy/std Std 0.0209311 +trainer/policy/std Max 0.438937 +trainer/policy/std Min 0.381487 +trainer/Advantage Weights Mean 8.66903 +trainer/Advantage Weights Std 24.1858 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.75375e-16 +trainer/Advantage Score Mean -0.291686 +trainer/Advantage Score Std 0.689031 +trainer/Advantage Score Max 3.14394 +trainer/Advantage Score Min -3.58284 +trainer/V1 Predictions Mean -71.4191 +trainer/V1 Predictions Std 17.6467 +trainer/V1 Predictions Max -5.23229 +trainer/V1 Predictions Min -87.5533 +trainer/VF Loss 0.125982 +expl/num steps total 723000 +expl/num paths total 970 +expl/path length Mean 500 +expl/path length Std 243 +expl/path length Max 743 +expl/path length Min 257 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.02409 +expl/Actions Std 0.833849 +expl/Actions Max 2.62728 +expl/Actions Min -2.45084 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 666307 +eval/num paths total 731 +eval/path length Mean 464 +eval/path length Std 0 +eval/path length Max 464 +eval/path length Min 464 +eval/Rewards Mean 0.00215517 +eval/Rewards Std 0.0463738 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0410949 +eval/Actions Std 0.748155 +eval/Actions Max 0.999846 +eval/Actions Min -0.99987 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.08848e-05 +time/evaluation sampling (s) 5.54295 +time/exploration sampling (s) 6.64249 +time/logging (s) 0.0085617 +time/saving (s) 0.0164383 +time/training (s) 19.8547 +time/epoch (s) 32.0652 +time/total (s) 18154.6 +Epoch -278 +------------------------------ ---------------- +2022-05-15 23:05:35.801716 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -277 finished +------------------------------ ---------------- +epoch -277 +replay_buffer/size 999047 +trainer/num train calls 724000 +trainer/QF1 Loss 0.766951 +trainer/QF2 Loss 0.681032 +trainer/Policy Loss 20.5827 +trainer/Q1 Predictions Mean -72.7486 +trainer/Q1 Predictions Std 16.1899 +trainer/Q1 Predictions Max 0.439544 +trainer/Q1 Predictions Min -85.8358 +trainer/Q2 Predictions Mean -72.8831 +trainer/Q2 Predictions Std 16.2784 +trainer/Q2 Predictions Max -0.445068 +trainer/Q2 Predictions Min -86.336 +trainer/Q Targets Mean -72.9855 +trainer/Q Targets Std 16.1344 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9916 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0255897 +trainer/policy/mean Std 0.728065 +trainer/policy/mean Max 0.99916 +trainer/policy/mean Min -0.999766 +trainer/policy/std Mean 0.416295 +trainer/policy/std Std 0.0206604 +trainer/policy/std Max 0.437248 +trainer/policy/std Min 0.383581 +trainer/Advantage Weights Mean 5.79492 +trainer/Advantage Weights Std 20.3125 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.19002e-14 +trainer/Advantage Score Mean -0.308545 +trainer/Advantage Score Std 0.607106 +trainer/Advantage Score Max 1.39761 +trainer/Advantage Score Min -3.10762 +trainer/V1 Predictions Mean -72.6557 +trainer/V1 Predictions Std 16.3105 +trainer/V1 Predictions Max -0.541557 +trainer/V1 Predictions Min -85.8453 +trainer/VF Loss 0.0803817 +expl/num steps total 724000 +expl/num paths total 971 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.195586 +expl/Actions Std 0.8307 +expl/Actions Max 2.22603 +expl/Actions Min -2.20076 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 667307 +eval/num paths total 732 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.455888 +eval/Actions Std 0.638281 +eval/Actions Max 0.999855 +eval/Actions Min -0.998196 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.75421e-06 +time/evaluation sampling (s) 5.06824 +time/exploration sampling (s) 7.24483 +time/logging (s) 0.00739979 +time/saving (s) 0.0118448 +time/training (s) 19.2818 +time/epoch (s) 31.6141 +time/total (s) 18186.2 +Epoch -277 +------------------------------ ---------------- +2022-05-15 23:06:07.544181 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -276 finished +------------------------------ ---------------- +epoch -276 +replay_buffer/size 999047 +trainer/num train calls 725000 +trainer/QF1 Loss 1.14843 +trainer/QF2 Loss 1.02367 +trainer/Policy Loss 30.6553 +trainer/Q1 Predictions Mean -71.683 +trainer/Q1 Predictions Std 19.2974 +trainer/Q1 Predictions Max -1.66933 +trainer/Q1 Predictions Min -85.7555 +trainer/Q2 Predictions Mean -71.7365 +trainer/Q2 Predictions Std 19.2601 +trainer/Q2 Predictions Max -2.35119 +trainer/Q2 Predictions Min -85.9782 +trainer/Q Targets Mean -71.8678 +trainer/Q Targets Std 19.2011 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8127 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.012394 +trainer/policy/mean Std 0.730936 +trainer/policy/mean Max 0.998722 +trainer/policy/mean Min -0.999342 +trainer/policy/std Mean 0.415164 +trainer/policy/std Std 0.0222607 +trainer/policy/std Max 0.440615 +trainer/policy/std Min 0.381746 +trainer/Advantage Weights Mean 8.50376 +trainer/Advantage Weights Std 24.2013 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.08719e-17 +trainer/Advantage Score Mean -0.234497 +trainer/Advantage Score Std 0.635212 +trainer/Advantage Score Max 1.82741 +trainer/Advantage Score Min -3.84081 +trainer/V1 Predictions Mean -71.5987 +trainer/V1 Predictions Std 19.3283 +trainer/V1 Predictions Max -1.36641 +trainer/V1 Predictions Min -85.6425 +trainer/VF Loss 0.0956579 +expl/num steps total 725000 +expl/num paths total 973 +expl/path length Mean 500 +expl/path length Std 219 +expl/path length Max 719 +expl/path length Min 281 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00841436 +expl/Actions Std 0.827446 +expl/Actions Max 2.23053 +expl/Actions Min -2.28737 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 668307 +eval/num paths total 733 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.229794 +eval/Actions Std 0.654581 +eval/Actions Max 0.999789 +eval/Actions Min -0.999609 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 5.27501e-06 +time/evaluation sampling (s) 5.12712 +time/exploration sampling (s) 6.53676 +time/logging (s) 0.0128597 +time/saving (s) 0.0217908 +time/training (s) 20.0372 +time/epoch (s) 31.7357 +time/total (s) 18218 +Epoch -276 +------------------------------ ---------------- +2022-05-15 23:06:39.588252 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -275 finished +------------------------------ ---------------- +epoch -275 +replay_buffer/size 999047 +trainer/num train calls 726000 +trainer/QF1 Loss 0.617883 +trainer/QF2 Loss 0.614815 +trainer/Policy Loss 23.386 +trainer/Q1 Predictions Mean -72.2151 +trainer/Q1 Predictions Std 17.1438 +trainer/Q1 Predictions Max -1.19125 +trainer/Q1 Predictions Min -86.49 +trainer/Q2 Predictions Mean -72.1141 +trainer/Q2 Predictions Std 17.1905 +trainer/Q2 Predictions Max -1.26958 +trainer/Q2 Predictions Min -86.2238 +trainer/Q Targets Mean -72.3122 +trainer/Q Targets Std 17.5537 +trainer/Q Targets Max -0.373588 +trainer/Q Targets Min -86.5714 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0465786 +trainer/policy/mean Std 0.725737 +trainer/policy/mean Max 0.999396 +trainer/policy/mean Min -0.999083 +trainer/policy/std Mean 0.415858 +trainer/policy/std Std 0.0203987 +trainer/policy/std Max 0.44059 +trainer/policy/std Min 0.384318 +trainer/Advantage Weights Mean 5.01803 +trainer/Advantage Weights Std 16.6628 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.16232e-35 +trainer/Advantage Score Mean -0.332341 +trainer/Advantage Score Std 0.774782 +trainer/Advantage Score Max 0.997673 +trainer/Advantage Score Min -7.98193 +trainer/V1 Predictions Mean -72.1319 +trainer/V1 Predictions Std 17.4956 +trainer/V1 Predictions Max -0.700671 +trainer/V1 Predictions Min -86.4518 +trainer/VF Loss 0.0884404 +expl/num steps total 726000 +expl/num paths total 975 +expl/path length Mean 500 +expl/path length Std 89 +expl/path length Max 589 +expl/path length Min 411 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0305651 +expl/Actions Std 0.833154 +expl/Actions Max 2.33119 +expl/Actions Min -2.4095 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 669170 +eval/num paths total 734 +eval/path length Mean 863 +eval/path length Std 0 +eval/path length Max 863 +eval/path length Min 863 +eval/Rewards Mean 0.00115875 +eval/Rewards Std 0.0340207 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00535978 +eval/Actions Std 0.727438 +eval/Actions Max 0.999793 +eval/Actions Min -0.999885 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.73208e-06 +time/evaluation sampling (s) 5.59096 +time/exploration sampling (s) 6.99327 +time/logging (s) 0.0110992 +time/saving (s) 0.0175243 +time/training (s) 19.4092 +time/epoch (s) 32.0221 +time/total (s) 18250 +Epoch -275 +------------------------------ ---------------- +2022-05-15 23:07:11.352751 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -274 finished +------------------------------ ---------------- +epoch -274 +replay_buffer/size 999047 +trainer/num train calls 727000 +trainer/QF1 Loss 1.25503 +trainer/QF2 Loss 1.22073 +trainer/Policy Loss 44.2782 +trainer/Q1 Predictions Mean -71.1192 +trainer/Q1 Predictions Std 20.0366 +trainer/Q1 Predictions Max 0.340177 +trainer/Q1 Predictions Min -86.525 +trainer/Q2 Predictions Mean -71.079 +trainer/Q2 Predictions Std 20.0898 +trainer/Q2 Predictions Max -0.384285 +trainer/Q2 Predictions Min -86.3731 +trainer/Q Targets Mean -71.2103 +trainer/Q Targets Std 19.6806 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6153 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00894285 +trainer/policy/mean Std 0.723183 +trainer/policy/mean Max 0.998876 +trainer/policy/mean Min -0.998925 +trainer/policy/std Mean 0.414632 +trainer/policy/std Std 0.0197387 +trainer/policy/std Max 0.43642 +trainer/policy/std Min 0.38653 +trainer/Advantage Weights Mean 10.8952 +trainer/Advantage Weights Std 27.804 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.31691e-13 +trainer/Advantage Score Mean -0.224297 +trainer/Advantage Score Std 0.577449 +trainer/Advantage Score Max 1.67193 +trainer/Advantage Score Min -2.96583 +trainer/V1 Predictions Mean -70.9705 +trainer/V1 Predictions Std 19.8132 +trainer/V1 Predictions Max -0.564374 +trainer/V1 Predictions Min -86.4626 +trainer/VF Loss 0.093732 +expl/num steps total 727000 +expl/num paths total 977 +expl/path length Mean 500 +expl/path length Std 481 +expl/path length Max 981 +expl/path length Min 19 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0357995 +expl/Actions Std 0.822314 +expl/Actions Max 2.18626 +expl/Actions Min -2.26766 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 670170 +eval/num paths total 735 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.015995 +eval/Actions Std 0.735422 +eval/Actions Max 0.999933 +eval/Actions Min -0.99961 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.00189e-06 +time/evaluation sampling (s) 5.12248 +time/exploration sampling (s) 7.20913 +time/logging (s) 0.00756736 +time/saving (s) 0.0112085 +time/training (s) 19.3946 +time/epoch (s) 31.745 +time/total (s) 18281.7 +Epoch -274 +------------------------------ ---------------- +2022-05-15 23:07:43.476479 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -273 finished +------------------------------ ---------------- +epoch -273 +replay_buffer/size 999047 +trainer/num train calls 728000 +trainer/QF1 Loss 1.62821 +trainer/QF2 Loss 1.59776 +trainer/Policy Loss 34.2158 +trainer/Q1 Predictions Mean -72.3249 +trainer/Q1 Predictions Std 17.3331 +trainer/Q1 Predictions Max -2.84058 +trainer/Q1 Predictions Min -86.2728 +trainer/Q2 Predictions Mean -72.3912 +trainer/Q2 Predictions Std 17.4563 +trainer/Q2 Predictions Max -2.47123 +trainer/Q2 Predictions Min -85.7203 +trainer/Q Targets Mean -72.7917 +trainer/Q Targets Std 17.7872 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.308 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0124962 +trainer/policy/mean Std 0.725989 +trainer/policy/mean Max 0.998313 +trainer/policy/mean Min -0.999127 +trainer/policy/std Mean 0.415905 +trainer/policy/std Std 0.0196633 +trainer/policy/std Max 0.438289 +trainer/policy/std Min 0.388638 +trainer/Advantage Weights Mean 8.94694 +trainer/Advantage Weights Std 24.8186 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.04646e-13 +trainer/Advantage Score Mean -0.202685 +trainer/Advantage Score Std 0.55232 +trainer/Advantage Score Max 1.79108 +trainer/Advantage Score Min -2.98882 +trainer/V1 Predictions Mean -72.6021 +trainer/V1 Predictions Std 17.7568 +trainer/V1 Predictions Max -2.65413 +trainer/V1 Predictions Min -86.2413 +trainer/VF Loss 0.0756624 +expl/num steps total 728000 +expl/num paths total 979 +expl/path length Mean 500 +expl/path length Std 268 +expl/path length Max 768 +expl/path length Min 232 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0508104 +expl/Actions Std 0.834515 +expl/Actions Max 2.3243 +expl/Actions Min -2.20594 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 671170 +eval/num paths total 736 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.113352 +eval/Actions Std 0.656729 +eval/Actions Max 0.99908 +eval/Actions Min -0.999897 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.95724e-06 +time/evaluation sampling (s) 5.42484 +time/exploration sampling (s) 7.38269 +time/logging (s) 0.0123546 +time/saving (s) 0.0186367 +time/training (s) 19.2772 +time/epoch (s) 32.1157 +time/total (s) 18313.9 +Epoch -273 +------------------------------ ---------------- +2022-05-15 23:08:16.182356 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -272 finished +------------------------------ ---------------- +epoch -272 +replay_buffer/size 999047 +trainer/num train calls 729000 +trainer/QF1 Loss 1.44049 +trainer/QF2 Loss 1.24529 +trainer/Policy Loss 46.3076 +trainer/Q1 Predictions Mean -71.3785 +trainer/Q1 Predictions Std 18.8345 +trainer/Q1 Predictions Max -0.457 +trainer/Q1 Predictions Min -85.8106 +trainer/Q2 Predictions Mean -71.5071 +trainer/Q2 Predictions Std 18.8206 +trainer/Q2 Predictions Max -0.163196 +trainer/Q2 Predictions Min -85.6665 +trainer/Q Targets Mean -71.7043 +trainer/Q Targets Std 18.4333 +trainer/Q Targets Max -2.01515 +trainer/Q Targets Min -86.0336 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0060983 +trainer/policy/mean Std 0.724176 +trainer/policy/mean Max 0.999757 +trainer/policy/mean Min -0.999347 +trainer/policy/std Mean 0.413735 +trainer/policy/std Std 0.0203871 +trainer/policy/std Max 0.436448 +trainer/policy/std Min 0.386217 +trainer/Advantage Weights Mean 11.6037 +trainer/Advantage Weights Std 26.5133 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.18529e-13 +trainer/Advantage Score Mean -0.152776 +trainer/Advantage Score Std 0.576153 +trainer/Advantage Score Max 1.245 +trainer/Advantage Score Min -2.87751 +trainer/V1 Predictions Mean -71.5044 +trainer/V1 Predictions Std 18.6021 +trainer/V1 Predictions Max -1.4301 +trainer/V1 Predictions Min -85.8403 +trainer/VF Loss 0.0812151 +expl/num steps total 729000 +expl/num paths total 980 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0376865 +expl/Actions Std 0.832653 +expl/Actions Max 2.35309 +expl/Actions Min -2.41087 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 672170 +eval/num paths total 737 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0980489 +eval/Actions Std 0.737774 +eval/Actions Max 0.994371 +eval/Actions Min -0.998138 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.11139e-05 +time/evaluation sampling (s) 5.7647 +time/exploration sampling (s) 7.11668 +time/logging (s) 0.0108775 +time/saving (s) 0.0177928 +time/training (s) 19.7739 +time/epoch (s) 32.6839 +time/total (s) 18346.6 +Epoch -272 +------------------------------ ---------------- +2022-05-15 23:08:47.960300 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -271 finished +------------------------------ ---------------- +epoch -271 +replay_buffer/size 999047 +trainer/num train calls 730000 +trainer/QF1 Loss 0.822866 +trainer/QF2 Loss 0.742764 +trainer/Policy Loss 17.913 +trainer/Q1 Predictions Mean -69.9436 +trainer/Q1 Predictions Std 18.4206 +trainer/Q1 Predictions Max -4.85129 +trainer/Q1 Predictions Min -85.7909 +trainer/Q2 Predictions Mean -69.9891 +trainer/Q2 Predictions Std 18.5262 +trainer/Q2 Predictions Max -3.7732 +trainer/Q2 Predictions Min -85.5362 +trainer/Q Targets Mean -70.3348 +trainer/Q Targets Std 18.5411 +trainer/Q Targets Max -3.93261 +trainer/Q Targets Min -86.1707 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0162091 +trainer/policy/mean Std 0.728967 +trainer/policy/mean Max 0.999791 +trainer/policy/mean Min -0.998983 +trainer/policy/std Mean 0.413058 +trainer/policy/std Std 0.0208923 +trainer/policy/std Max 0.434414 +trainer/policy/std Min 0.384525 +trainer/Advantage Weights Mean 3.84461 +trainer/Advantage Weights Std 17.7196 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.46418e-29 +trainer/Advantage Score Mean -0.461199 +trainer/Advantage Score Std 0.724489 +trainer/Advantage Score Max 1.04916 +trainer/Advantage Score Min -6.58731 +trainer/V1 Predictions Mean -69.938 +trainer/V1 Predictions Std 18.8328 +trainer/V1 Predictions Max -2.41766 +trainer/V1 Predictions Min -86.2741 +trainer/VF Loss 0.0883894 +expl/num steps total 730000 +expl/num paths total 982 +expl/path length Mean 500 +expl/path length Std 429 +expl/path length Max 929 +expl/path length Min 71 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0123849 +expl/Actions Std 0.81742 +expl/Actions Max 2.35388 +expl/Actions Min -2.18953 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 673170 +eval/num paths total 738 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.170444 +eval/Actions Std 0.734253 +eval/Actions Max 0.998956 +eval/Actions Min -0.999609 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02981e-05 +time/evaluation sampling (s) 5.35514 +time/exploration sampling (s) 6.46246 +time/logging (s) 0.00980756 +time/saving (s) 0.0118962 +time/training (s) 19.9208 +time/epoch (s) 31.7601 +time/total (s) 18378.3 +Epoch -271 +------------------------------ ---------------- +2022-05-15 23:09:19.145306 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -270 finished +------------------------------ ---------------- +epoch -270 +replay_buffer/size 999047 +trainer/num train calls 731000 +trainer/QF1 Loss 0.535539 +trainer/QF2 Loss 0.534928 +trainer/Policy Loss 11.5303 +trainer/Q1 Predictions Mean -72.4381 +trainer/Q1 Predictions Std 17.6453 +trainer/Q1 Predictions Max -0.836357 +trainer/Q1 Predictions Min -86.6043 +trainer/Q2 Predictions Mean -72.3375 +trainer/Q2 Predictions Std 17.651 +trainer/Q2 Predictions Max -0.377934 +trainer/Q2 Predictions Min -86.5934 +trainer/Q Targets Mean -72.2386 +trainer/Q Targets Std 17.7443 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3798 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0201666 +trainer/policy/mean Std 0.718353 +trainer/policy/mean Max 0.999129 +trainer/policy/mean Min -0.999169 +trainer/policy/std Mean 0.413685 +trainer/policy/std Std 0.0204546 +trainer/policy/std Max 0.435943 +trainer/policy/std Min 0.386183 +trainer/Advantage Weights Mean 2.33271 +trainer/Advantage Weights Std 12.5806 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.70117e-18 +trainer/Advantage Score Mean -0.542303 +trainer/Advantage Score Std 0.598034 +trainer/Advantage Score Max 0.838551 +trainer/Advantage Score Min -4.01379 +trainer/V1 Predictions Mean -71.9678 +trainer/V1 Predictions Std 17.9248 +trainer/V1 Predictions Max 0.893822 +trainer/V1 Predictions Min -86.5762 +trainer/VF Loss 0.073049 +expl/num steps total 731000 +expl/num paths total 984 +expl/path length Mean 500 +expl/path length Std 341 +expl/path length Max 841 +expl/path length Min 159 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0261651 +expl/Actions Std 0.826416 +expl/Actions Max 2.32787 +expl/Actions Min -2.22617 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 673703 +eval/num paths total 739 +eval/path length Mean 533 +eval/path length Std 0 +eval/path length Max 533 +eval/path length Min 533 +eval/Rewards Mean 0.00187617 +eval/Rewards Std 0.0432742 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0234916 +eval/Actions Std 0.743233 +eval/Actions Max 0.999842 +eval/Actions Min -0.999792 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.28518e-05 +time/evaluation sampling (s) 5.0173 +time/exploration sampling (s) 6.71591 +time/logging (s) 0.0103163 +time/saving (s) 0.0184155 +time/training (s) 19.4055 +time/epoch (s) 31.1675 +time/total (s) 18409.5 +Epoch -270 +------------------------------ ---------------- +2022-05-15 23:09:50.941849 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -269 finished +------------------------------ ---------------- +epoch -269 +replay_buffer/size 999047 +trainer/num train calls 732000 +trainer/QF1 Loss 0.899703 +trainer/QF2 Loss 0.997663 +trainer/Policy Loss 17.6464 +trainer/Q1 Predictions Mean -71.0097 +trainer/Q1 Predictions Std 18.575 +trainer/Q1 Predictions Max -2.20765 +trainer/Q1 Predictions Min -86.337 +trainer/Q2 Predictions Mean -71.0225 +trainer/Q2 Predictions Std 18.569 +trainer/Q2 Predictions Max -1.24482 +trainer/Q2 Predictions Min -86.1378 +trainer/Q Targets Mean -70.6126 +trainer/Q Targets Std 18.7194 +trainer/Q Targets Max -2.00867 +trainer/Q Targets Min -85.6991 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0248731 +trainer/policy/mean Std 0.729189 +trainer/policy/mean Max 0.999167 +trainer/policy/mean Min -0.999327 +trainer/policy/std Mean 0.415428 +trainer/policy/std Std 0.0204843 +trainer/policy/std Max 0.436126 +trainer/policy/std Min 0.386371 +trainer/Advantage Weights Mean 4.63432 +trainer/Advantage Weights Std 18.4338 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.0094e-23 +trainer/Advantage Score Mean -0.496787 +trainer/Advantage Score Std 0.863277 +trainer/Advantage Score Max 1.95686 +trainer/Advantage Score Min -5.07612 +trainer/V1 Predictions Mean -70.3979 +trainer/V1 Predictions Std 18.9099 +trainer/V1 Predictions Max -0.0266965 +trainer/V1 Predictions Min -85.9423 +trainer/VF Loss 0.140587 +expl/num steps total 732000 +expl/num paths total 985 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0595586 +expl/Actions Std 0.839841 +expl/Actions Max 2.68497 +expl/Actions Min -2.30208 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 674703 +eval/num paths total 740 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0547737 +eval/Actions Std 0.716081 +eval/Actions Max 0.999603 +eval/Actions Min -0.999576 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.29701e-05 +time/evaluation sampling (s) 5.2237 +time/exploration sampling (s) 6.72843 +time/logging (s) 0.0105732 +time/saving (s) 0.0155981 +time/training (s) 19.7948 +time/epoch (s) 31.7731 +time/total (s) 18441.3 +Epoch -269 +------------------------------ ---------------- +2022-05-15 23:10:21.537225 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -268 finished +------------------------------ ---------------- +epoch -268 +replay_buffer/size 999047 +trainer/num train calls 733000 +trainer/QF1 Loss 0.490646 +trainer/QF2 Loss 0.566943 +trainer/Policy Loss 32.6616 +trainer/Q1 Predictions Mean -73.3203 +trainer/Q1 Predictions Std 15.3667 +trainer/Q1 Predictions Max -1.54049 +trainer/Q1 Predictions Min -89.6115 +trainer/Q2 Predictions Mean -73.3754 +trainer/Q2 Predictions Std 15.3301 +trainer/Q2 Predictions Max -1.41888 +trainer/Q2 Predictions Min -89.0966 +trainer/Q Targets Mean -73.4348 +trainer/Q Targets Std 15.3439 +trainer/Q Targets Max -1.54632 +trainer/Q Targets Min -89.2252 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0111365 +trainer/policy/mean Std 0.718717 +trainer/policy/mean Max 0.999078 +trainer/policy/mean Min -0.998144 +trainer/policy/std Mean 0.413316 +trainer/policy/std Std 0.0198866 +trainer/policy/std Max 0.436516 +trainer/policy/std Min 0.383504 +trainer/Advantage Weights Mean 5.95796 +trainer/Advantage Weights Std 19.4945 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.38631e-20 +trainer/Advantage Score Mean -0.354311 +trainer/Advantage Score Std 0.66721 +trainer/Advantage Score Max 1.134 +trainer/Advantage Score Min -4.57251 +trainer/V1 Predictions Mean -73.1376 +trainer/V1 Predictions Std 15.3816 +trainer/V1 Predictions Max -1.3307 +trainer/V1 Predictions Min -89.2203 +trainer/VF Loss 0.0794784 +expl/num steps total 733000 +expl/num paths total 987 +expl/path length Mean 500 +expl/path length Std 14 +expl/path length Max 514 +expl/path length Min 486 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0366035 +expl/Actions Std 0.833341 +expl/Actions Max 2.33373 +expl/Actions Min -2.15158 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 675490 +eval/num paths total 741 +eval/path length Mean 787 +eval/path length Std 0 +eval/path length Max 787 +eval/path length Min 787 +eval/Rewards Mean 0.00127065 +eval/Rewards Std 0.0356235 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0590359 +eval/Actions Std 0.741585 +eval/Actions Max 0.999811 +eval/Actions Min -0.999658 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.07768e-05 +time/evaluation sampling (s) 4.76 +time/exploration sampling (s) 6.52757 +time/logging (s) 0.00850029 +time/saving (s) 0.0168789 +time/training (s) 19.2582 +time/epoch (s) 30.5712 +time/total (s) 18471.9 +Epoch -268 +------------------------------ ---------------- +2022-05-15 23:10:53.445642 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -267 finished +------------------------------ ---------------- +epoch -267 +replay_buffer/size 999047 +trainer/num train calls 734000 +trainer/QF1 Loss 0.714268 +trainer/QF2 Loss 0.663709 +trainer/Policy Loss 15.016 +trainer/Q1 Predictions Mean -69.8441 +trainer/Q1 Predictions Std 20.3822 +trainer/Q1 Predictions Max -0.441903 +trainer/Q1 Predictions Min -85.9796 +trainer/Q2 Predictions Mean -69.966 +trainer/Q2 Predictions Std 20.3918 +trainer/Q2 Predictions Max 0.140521 +trainer/Q2 Predictions Min -85.8982 +trainer/Q Targets Mean -70.1142 +trainer/Q Targets Std 20.2278 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2904 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00277218 +trainer/policy/mean Std 0.723445 +trainer/policy/mean Max 0.999598 +trainer/policy/mean Min -0.999842 +trainer/policy/std Mean 0.413612 +trainer/policy/std Std 0.0203874 +trainer/policy/std Max 0.435874 +trainer/policy/std Min 0.382439 +trainer/Advantage Weights Mean 3.52831 +trainer/Advantage Weights Std 15.3288 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.11843e-17 +trainer/Advantage Score Mean -0.378851 +trainer/Advantage Score Std 0.612673 +trainer/Advantage Score Max 1.35193 +trainer/Advantage Score Min -3.9032 +trainer/V1 Predictions Mean -69.7954 +trainer/V1 Predictions Std 20.498 +trainer/V1 Predictions Max 0.162948 +trainer/V1 Predictions Min -86.0327 +trainer/VF Loss 0.0708172 +expl/num steps total 734000 +expl/num paths total 989 +expl/path length Mean 500 +expl/path length Std 108 +expl/path length Max 608 +expl/path length Min 392 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00611337 +expl/Actions Std 0.831174 +expl/Actions Max 2.23677 +expl/Actions Min -2.40308 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 676490 +eval/num paths total 742 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.194322 +eval/Actions Std 0.743511 +eval/Actions Max 0.999413 +eval/Actions Min -0.999653 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 5.13718e-06 +time/evaluation sampling (s) 4.86056 +time/exploration sampling (s) 7.30248 +time/logging (s) 0.0128872 +time/saving (s) 0.0201234 +time/training (s) 19.7017 +time/epoch (s) 31.8978 +time/total (s) 18503.8 +Epoch -267 +------------------------------ ---------------- +2022-05-15 23:11:25.817117 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -266 finished +------------------------------ ---------------- +epoch -266 +replay_buffer/size 999047 +trainer/num train calls 735000 +trainer/QF1 Loss 4.30353 +trainer/QF2 Loss 4.18409 +trainer/Policy Loss 15.4969 +trainer/Q1 Predictions Mean -72.0526 +trainer/Q1 Predictions Std 18.8983 +trainer/Q1 Predictions Max -0.932986 +trainer/Q1 Predictions Min -89.0584 +trainer/Q2 Predictions Mean -72.0717 +trainer/Q2 Predictions Std 18.8572 +trainer/Q2 Predictions Max -2.1695 +trainer/Q2 Predictions Min -88.59 +trainer/Q Targets Mean -71.7257 +trainer/Q Targets Std 19.0109 +trainer/Q Targets Max -1.25183 +trainer/Q Targets Min -88.7722 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0370363 +trainer/policy/mean Std 0.749522 +trainer/policy/mean Max 0.999286 +trainer/policy/mean Min -0.998822 +trainer/policy/std Mean 0.411595 +trainer/policy/std Std 0.020764 +trainer/policy/std Max 0.435639 +trainer/policy/std Min 0.382871 +trainer/Advantage Weights Mean 4.83785 +trainer/Advantage Weights Std 19.8506 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.30587e-20 +trainer/Advantage Score Mean -0.425702 +trainer/Advantage Score Std 0.546268 +trainer/Advantage Score Max 1.7881 +trainer/Advantage Score Min -4.57848 +trainer/V1 Predictions Mean -71.6416 +trainer/V1 Predictions Std 18.9383 +trainer/V1 Predictions Max -0.666728 +trainer/V1 Predictions Min -88.3668 +trainer/VF Loss 0.0781292 +expl/num steps total 735000 +expl/num paths total 991 +expl/path length Mean 500 +expl/path length Std 281 +expl/path length Max 781 +expl/path length Min 219 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.012889 +expl/Actions Std 0.857198 +expl/Actions Max 2.32634 +expl/Actions Min -2.49615 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 677490 +eval/num paths total 743 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.215634 +eval/Actions Std 0.759759 +eval/Actions Max 0.999404 +eval/Actions Min -0.99962 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.07128e-06 +time/evaluation sampling (s) 5.58183 +time/exploration sampling (s) 7.09214 +time/logging (s) 0.0128378 +time/saving (s) 0.0219344 +time/training (s) 19.642 +time/epoch (s) 32.3507 +time/total (s) 18536.1 +Epoch -266 +------------------------------ ---------------- +2022-05-15 23:11:58.316745 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -265 finished +------------------------------ ---------------- +epoch -265 +replay_buffer/size 999047 +trainer/num train calls 736000 +trainer/QF1 Loss 0.539043 +trainer/QF2 Loss 0.59652 +trainer/Policy Loss 24.7993 +trainer/Q1 Predictions Mean -72.6973 +trainer/Q1 Predictions Std 17.4569 +trainer/Q1 Predictions Max -0.570657 +trainer/Q1 Predictions Min -86.4481 +trainer/Q2 Predictions Mean -72.792 +trainer/Q2 Predictions Std 17.4924 +trainer/Q2 Predictions Max -0.239511 +trainer/Q2 Predictions Min -86.5041 +trainer/Q Targets Mean -72.8192 +trainer/Q Targets Std 17.4896 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9073 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000572927 +trainer/policy/mean Std 0.72569 +trainer/policy/mean Max 0.998597 +trainer/policy/mean Min -0.997089 +trainer/policy/std Mean 0.412671 +trainer/policy/std Std 0.0205326 +trainer/policy/std Max 0.435941 +trainer/policy/std Min 0.383347 +trainer/Advantage Weights Mean 4.93436 +trainer/Advantage Weights Std 18.2898 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.557e-17 +trainer/Advantage Score Mean -0.385118 +trainer/Advantage Score Std 0.651256 +trainer/Advantage Score Max 1.3378 +trainer/Advantage Score Min -3.68867 +trainer/V1 Predictions Mean -72.5079 +trainer/V1 Predictions Std 17.6476 +trainer/V1 Predictions Max 0.0101155 +trainer/V1 Predictions Min -86.2338 +trainer/VF Loss 0.0762223 +expl/num steps total 736000 +expl/num paths total 992 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.27241 +expl/Actions Std 0.86259 +expl/Actions Max 2.51082 +expl/Actions Min -2.1145 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 678490 +eval/num paths total 744 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0407777 +eval/Actions Std 0.74687 +eval/Actions Max 0.99964 +eval/Actions Min -0.99927 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.09409e-06 +time/evaluation sampling (s) 4.87678 +time/exploration sampling (s) 7.6825 +time/logging (s) 0.0126358 +time/saving (s) 0.0192895 +time/training (s) 19.8882 +time/epoch (s) 32.4794 +time/total (s) 18568.6 +Epoch -265 +------------------------------ ---------------- +2022-05-15 23:12:30.803037 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -264 finished +------------------------------ ---------------- +epoch -264 +replay_buffer/size 999047 +trainer/num train calls 737000 +trainer/QF1 Loss 1.16785 +trainer/QF2 Loss 0.973026 +trainer/Policy Loss 19.278 +trainer/Q1 Predictions Mean -71.0749 +trainer/Q1 Predictions Std 18.263 +trainer/Q1 Predictions Max -2.87935 +trainer/Q1 Predictions Min -86.2994 +trainer/Q2 Predictions Mean -71.1935 +trainer/Q2 Predictions Std 18.1703 +trainer/Q2 Predictions Max -4.1613 +trainer/Q2 Predictions Min -86.4141 +trainer/Q Targets Mean -70.9324 +trainer/Q Targets Std 18.1109 +trainer/Q Targets Max -4.25797 +trainer/Q Targets Min -86.1016 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0114491 +trainer/policy/mean Std 0.726615 +trainer/policy/mean Max 0.999976 +trainer/policy/mean Min -0.99969 +trainer/policy/std Mean 0.412899 +trainer/policy/std Std 0.0196755 +trainer/policy/std Max 0.433883 +trainer/policy/std Min 0.383742 +trainer/Advantage Weights Mean 3.59689 +trainer/Advantage Weights Std 15.4922 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.26483e-14 +trainer/Advantage Score Mean -0.494089 +trainer/Advantage Score Std 0.589939 +trainer/Advantage Score Max 1.03356 +trainer/Advantage Score Min -3.05751 +trainer/V1 Predictions Mean -70.6518 +trainer/V1 Predictions Std 18.3332 +trainer/V1 Predictions Max -3.82574 +trainer/V1 Predictions Min -85.9645 +trainer/VF Loss 0.0721276 +expl/num steps total 737000 +expl/num paths total 994 +expl/path length Mean 500 +expl/path length Std 390 +expl/path length Max 890 +expl/path length Min 110 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0278755 +expl/Actions Std 0.838751 +expl/Actions Max 2.28614 +expl/Actions Min -2.32104 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 679490 +eval/num paths total 745 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.396186 +eval/Actions Std 0.552996 +eval/Actions Max 0.998735 +eval/Actions Min -0.999452 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.81497e-06 +time/evaluation sampling (s) 5.43743 +time/exploration sampling (s) 7.00979 +time/logging (s) 0.00950718 +time/saving (s) 0.0159319 +time/training (s) 19.997 +time/epoch (s) 32.4697 +time/total (s) 18601.1 +Epoch -264 +------------------------------ ---------------- +2022-05-15 23:13:03.163497 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -263 finished +------------------------------ ---------------- +epoch -263 +replay_buffer/size 999047 +trainer/num train calls 738000 +trainer/QF1 Loss 0.629774 +trainer/QF2 Loss 0.649645 +trainer/Policy Loss 21.5177 +trainer/Q1 Predictions Mean -73.8653 +trainer/Q1 Predictions Std 15.2737 +trainer/Q1 Predictions Max -0.129121 +trainer/Q1 Predictions Min -86.4597 +trainer/Q2 Predictions Mean -73.9082 +trainer/Q2 Predictions Std 15.1889 +trainer/Q2 Predictions Max -0.442245 +trainer/Q2 Predictions Min -86.6477 +trainer/Q Targets Mean -73.6752 +trainer/Q Targets Std 15.2503 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0605 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0120961 +trainer/policy/mean Std 0.73027 +trainer/policy/mean Max 0.999642 +trainer/policy/mean Min -0.999339 +trainer/policy/std Mean 0.41242 +trainer/policy/std Std 0.0214012 +trainer/policy/std Max 0.436328 +trainer/policy/std Min 0.380254 +trainer/Advantage Weights Mean 3.92148 +trainer/Advantage Weights Std 18.0156 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.05745e-15 +trainer/Advantage Score Mean -0.401294 +trainer/Advantage Score Std 0.583734 +trainer/Advantage Score Max 3.33246 +trainer/Advantage Score Min -3.44829 +trainer/V1 Predictions Mean -73.4909 +trainer/V1 Predictions Std 15.1727 +trainer/V1 Predictions Max 0.211916 +trainer/V1 Predictions Min -86.0141 +trainer/VF Loss 0.118166 +expl/num steps total 738000 +expl/num paths total 996 +expl/path length Mean 500 +expl/path length Std 20 +expl/path length Max 520 +expl/path length Min 480 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0911581 +expl/Actions Std 0.846363 +expl/Actions Max 2.37904 +expl/Actions Min -2.35814 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 680490 +eval/num paths total 746 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.157477 +eval/Actions Std 0.746634 +eval/Actions Max 0.999718 +eval/Actions Min -0.999723 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.5849e-06 +time/evaluation sampling (s) 5.35592 +time/exploration sampling (s) 7.02008 +time/logging (s) 0.0125898 +time/saving (s) 0.0190457 +time/training (s) 19.9411 +time/epoch (s) 32.3487 +time/total (s) 18633.4 +Epoch -263 +------------------------------ ---------------- +2022-05-15 23:13:35.338560 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -262 finished +------------------------------ ---------------- +epoch -262 +replay_buffer/size 999047 +trainer/num train calls 739000 +trainer/QF1 Loss 1.0674 +trainer/QF2 Loss 1.1183 +trainer/Policy Loss 33.4151 +trainer/Q1 Predictions Mean -70.6815 +trainer/Q1 Predictions Std 21.0341 +trainer/Q1 Predictions Max -0.315573 +trainer/Q1 Predictions Min -86.2045 +trainer/Q2 Predictions Mean -70.6127 +trainer/Q2 Predictions Std 21.0159 +trainer/Q2 Predictions Max -0.515615 +trainer/Q2 Predictions Min -86.0687 +trainer/Q Targets Mean -70.5756 +trainer/Q Targets Std 20.9392 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.295 +trainer/rewards Mean -0.976562 +trainer/rewards Std 0.151288 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0234375 +trainer/terminals Std 0.151288 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0229789 +trainer/policy/mean Std 0.730051 +trainer/policy/mean Max 0.998603 +trainer/policy/mean Min -0.999391 +trainer/policy/std Mean 0.413069 +trainer/policy/std Std 0.0212445 +trainer/policy/std Max 0.436245 +trainer/policy/std Min 0.380315 +trainer/Advantage Weights Mean 7.77591 +trainer/Advantage Weights Std 24.8884 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.09295e-15 +trainer/Advantage Score Mean -0.377197 +trainer/Advantage Score Std 0.672097 +trainer/Advantage Score Max 1.55314 +trainer/Advantage Score Min -3.38002 +trainer/V1 Predictions Mean -70.3334 +trainer/V1 Predictions Std 20.9317 +trainer/V1 Predictions Max -0.258561 +trainer/V1 Predictions Min -86.1081 +trainer/VF Loss 0.108844 +expl/num steps total 739000 +expl/num paths total 997 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0361289 +expl/Actions Std 0.840233 +expl/Actions Max 2.54308 +expl/Actions Min -2.37827 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 681490 +eval/num paths total 747 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0270576 +eval/Actions Std 0.739238 +eval/Actions Max 0.999924 +eval/Actions Min -0.999134 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.32071e-05 +time/evaluation sampling (s) 5.06741 +time/exploration sampling (s) 6.85981 +time/logging (s) 0.0115065 +time/saving (s) 0.0150585 +time/training (s) 20.2001 +time/epoch (s) 32.1539 +time/total (s) 18665.6 +Epoch -262 +------------------------------ ---------------- +2022-05-15 23:14:07.172335 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -261 finished +------------------------------ ---------------- +epoch -261 +replay_buffer/size 999047 +trainer/num train calls 740000 +trainer/QF1 Loss 0.683203 +trainer/QF2 Loss 0.569382 +trainer/Policy Loss 26.8314 +trainer/Q1 Predictions Mean -72.0316 +trainer/Q1 Predictions Std 18.325 +trainer/Q1 Predictions Max -1.39558 +trainer/Q1 Predictions Min -87.3798 +trainer/Q2 Predictions Mean -72.0095 +trainer/Q2 Predictions Std 18.3444 +trainer/Q2 Predictions Max -0.521021 +trainer/Q2 Predictions Min -87.9998 +trainer/Q Targets Mean -72.1855 +trainer/Q Targets Std 18.2667 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3011 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00352638 +trainer/policy/mean Std 0.726383 +trainer/policy/mean Max 0.999759 +trainer/policy/mean Min -0.999094 +trainer/policy/std Mean 0.414564 +trainer/policy/std Std 0.0205571 +trainer/policy/std Max 0.436749 +trainer/policy/std Min 0.383928 +trainer/Advantage Weights Mean 6.86604 +trainer/Advantage Weights Std 21.8717 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.75557e-13 +trainer/Advantage Score Mean -0.297815 +trainer/Advantage Score Std 0.573596 +trainer/Advantage Score Max 1.53201 +trainer/Advantage Score Min -2.892 +trainer/V1 Predictions Mean -71.9331 +trainer/V1 Predictions Std 18.2866 +trainer/V1 Predictions Max 0.380583 +trainer/V1 Predictions Min -87.409 +trainer/VF Loss 0.083737 +expl/num steps total 740000 +expl/num paths total 999 +expl/path length Mean 500 +expl/path length Std 216 +expl/path length Max 716 +expl/path length Min 284 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0426255 +expl/Actions Std 0.828115 +expl/Actions Max 2.65141 +expl/Actions Min -2.29209 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 682490 +eval/num paths total 748 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.169587 +eval/Actions Std 0.698072 +eval/Actions Max 0.999743 +eval/Actions Min -0.999832 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.11111e-05 +time/evaluation sampling (s) 5.17916 +time/exploration sampling (s) 6.96951 +time/logging (s) 0.0129694 +time/saving (s) 0.019386 +time/training (s) 19.6367 +time/epoch (s) 31.8178 +time/total (s) 18697.4 +Epoch -261 +------------------------------ ---------------- +2022-05-15 23:14:39.731573 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -260 finished +------------------------------ ---------------- +epoch -260 +replay_buffer/size 999047 +trainer/num train calls 741000 +trainer/QF1 Loss 0.864842 +trainer/QF2 Loss 0.826166 +trainer/Policy Loss 16.9896 +trainer/Q1 Predictions Mean -72.1662 +trainer/Q1 Predictions Std 17.1053 +trainer/Q1 Predictions Max -0.635651 +trainer/Q1 Predictions Min -86.3271 +trainer/Q2 Predictions Mean -72.1188 +trainer/Q2 Predictions Std 16.9686 +trainer/Q2 Predictions Max -0.0609583 +trainer/Q2 Predictions Min -86.0393 +trainer/Q Targets Mean -72.0817 +trainer/Q Targets Std 17.2803 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2469 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0292677 +trainer/policy/mean Std 0.72311 +trainer/policy/mean Max 0.998769 +trainer/policy/mean Min -0.997655 +trainer/policy/std Mean 0.412364 +trainer/policy/std Std 0.020992 +trainer/policy/std Max 0.433544 +trainer/policy/std Min 0.379617 +trainer/Advantage Weights Mean 2.49666 +trainer/Advantage Weights Std 12.4723 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.46563e-26 +trainer/Advantage Score Mean -0.436297 +trainer/Advantage Score Std 0.640653 +trainer/Advantage Score Max 0.887852 +trainer/Advantage Score Min -5.78569 +trainer/V1 Predictions Mean -71.8042 +trainer/V1 Predictions Std 17.2534 +trainer/V1 Predictions Max -0.346484 +trainer/V1 Predictions Min -86.0706 +trainer/VF Loss 0.0694891 +expl/num steps total 741000 +expl/num paths total 1001 +expl/path length Mean 500 +expl/path length Std 280 +expl/path length Max 780 +expl/path length Min 220 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0101569 +expl/Actions Std 0.828569 +expl/Actions Max 2.50225 +expl/Actions Min -2.3115 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 683490 +eval/num paths total 749 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.397742 +eval/Actions Std 0.482216 +eval/Actions Max 0.996765 +eval/Actions Min -0.989546 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.49412e-05 +time/evaluation sampling (s) 5.77998 +time/exploration sampling (s) 7.0782 +time/logging (s) 0.00965476 +time/saving (s) 0.0161832 +time/training (s) 19.6509 +time/epoch (s) 32.5349 +time/total (s) 18730 +Epoch -260 +------------------------------ ---------------- +2022-05-15 23:15:11.067517 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -259 finished +------------------------------ ---------------- +epoch -259 +replay_buffer/size 999047 +trainer/num train calls 742000 +trainer/QF1 Loss 0.865341 +trainer/QF2 Loss 0.832687 +trainer/Policy Loss 20.7037 +trainer/Q1 Predictions Mean -72.6983 +trainer/Q1 Predictions Std 16.4531 +trainer/Q1 Predictions Max -0.911652 +trainer/Q1 Predictions Min -86.0412 +trainer/Q2 Predictions Mean -72.5945 +trainer/Q2 Predictions Std 16.384 +trainer/Q2 Predictions Max -1.46885 +trainer/Q2 Predictions Min -86.0597 +trainer/Q Targets Mean -72.4139 +trainer/Q Targets Std 16.7121 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.8905 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0311043 +trainer/policy/mean Std 0.734636 +trainer/policy/mean Max 0.999767 +trainer/policy/mean Min -0.999924 +trainer/policy/std Mean 0.412252 +trainer/policy/std Std 0.0211047 +trainer/policy/std Max 0.436896 +trainer/policy/std Min 0.379681 +trainer/Advantage Weights Mean 3.55356 +trainer/Advantage Weights Std 16.6768 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.71534e-19 +trainer/Advantage Score Mean -0.546378 +trainer/Advantage Score Std 0.696466 +trainer/Advantage Score Max 1.28143 +trainer/Advantage Score Min -4.21983 +trainer/V1 Predictions Mean -72.2035 +trainer/V1 Predictions Std 16.7276 +trainer/V1 Predictions Max -0.750001 +trainer/V1 Predictions Min -85.7116 +trainer/VF Loss 0.094127 +expl/num steps total 742000 +expl/num paths total 1003 +expl/path length Mean 500 +expl/path length Std 215 +expl/path length Max 715 +expl/path length Min 285 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0237229 +expl/Actions Std 0.829232 +expl/Actions Max 2.38091 +expl/Actions Min -2.45182 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 684490 +eval/num paths total 750 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0740286 +eval/Actions Std 0.688224 +eval/Actions Max 0.999865 +eval/Actions Min -0.999938 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.08844e-05 +time/evaluation sampling (s) 5.14995 +time/exploration sampling (s) 6.61246 +time/logging (s) 0.0074769 +time/saving (s) 0.0114886 +time/training (s) 19.5365 +time/epoch (s) 31.3179 +time/total (s) 18761.3 +Epoch -259 +------------------------------ ---------------- +2022-05-15 23:15:42.737885 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -258 finished +------------------------------ ---------------- +epoch -258 +replay_buffer/size 999047 +trainer/num train calls 743000 +trainer/QF1 Loss 0.859851 +trainer/QF2 Loss 0.734127 +trainer/Policy Loss 15.1514 +trainer/Q1 Predictions Mean -72.0804 +trainer/Q1 Predictions Std 17.1686 +trainer/Q1 Predictions Max -1.03671 +trainer/Q1 Predictions Min -86.4502 +trainer/Q2 Predictions Mean -71.8314 +trainer/Q2 Predictions Std 17.2691 +trainer/Q2 Predictions Max 0.00913745 +trainer/Q2 Predictions Min -86.1002 +trainer/Q Targets Mean -71.7286 +trainer/Q Targets Std 17.3026 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3856 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0205813 +trainer/policy/mean Std 0.73457 +trainer/policy/mean Max 0.999505 +trainer/policy/mean Min -0.999881 +trainer/policy/std Mean 0.410829 +trainer/policy/std Std 0.0208847 +trainer/policy/std Max 0.433753 +trainer/policy/std Min 0.380373 +trainer/Advantage Weights Mean 4.31142 +trainer/Advantage Weights Std 17.2898 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.61665e-19 +trainer/Advantage Score Mean -0.494246 +trainer/Advantage Score Std 0.682486 +trainer/Advantage Score Max 1.33437 +trainer/Advantage Score Min -4.15954 +trainer/V1 Predictions Mean -71.4467 +trainer/V1 Predictions Std 17.3998 +trainer/V1 Predictions Max -0.284433 +trainer/V1 Predictions Min -86.36 +trainer/VF Loss 0.098733 +expl/num steps total 743000 +expl/num paths total 1004 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00387617 +expl/Actions Std 0.878124 +expl/Actions Max 2.59668 +expl/Actions Min -2.3987 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 685215 +eval/num paths total 751 +eval/path length Mean 725 +eval/path length Std 0 +eval/path length Max 725 +eval/path length Min 725 +eval/Rewards Mean 0.00137931 +eval/Rewards Std 0.0371134 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.029373 +eval/Actions Std 0.743089 +eval/Actions Max 0.999484 +eval/Actions Min -0.999566 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.37822e-05 +time/evaluation sampling (s) 5.13583 +time/exploration sampling (s) 6.64751 +time/logging (s) 0.00711334 +time/saving (s) 0.015092 +time/training (s) 19.8471 +time/epoch (s) 31.6527 +time/total (s) 18792.9 +Epoch -258 +------------------------------ ---------------- +2022-05-15 23:16:15.429660 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -257 finished +------------------------------ ---------------- +epoch -257 +replay_buffer/size 999047 +trainer/num train calls 744000 +trainer/QF1 Loss 0.898716 +trainer/QF2 Loss 0.716335 +trainer/Policy Loss 19.0843 +trainer/Q1 Predictions Mean -72.8648 +trainer/Q1 Predictions Std 15.4999 +trainer/Q1 Predictions Max -5.36372 +trainer/Q1 Predictions Min -87.1859 +trainer/Q2 Predictions Mean -72.8753 +trainer/Q2 Predictions Std 15.4439 +trainer/Q2 Predictions Max -6.76023 +trainer/Q2 Predictions Min -86.7169 +trainer/Q Targets Mean -72.7606 +trainer/Q Targets Std 15.4304 +trainer/Q Targets Max -3.85275 +trainer/Q Targets Min -86.3162 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00221698 +trainer/policy/mean Std 0.733431 +trainer/policy/mean Max 0.999621 +trainer/policy/mean Min -0.998854 +trainer/policy/std Mean 0.411027 +trainer/policy/std Std 0.0216977 +trainer/policy/std Max 0.433524 +trainer/policy/std Min 0.380621 +trainer/Advantage Weights Mean 6.44313 +trainer/Advantage Weights Std 22.4486 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.53145e-17 +trainer/Advantage Score Mean -0.339607 +trainer/Advantage Score Std 0.627058 +trainer/Advantage Score Max 2.32841 +trainer/Advantage Score Min -3.76329 +trainer/V1 Predictions Mean -72.4725 +trainer/V1 Predictions Std 15.5811 +trainer/V1 Predictions Max -4.06618 +trainer/V1 Predictions Min -86.1533 +trainer/VF Loss 0.102334 +expl/num steps total 744000 +expl/num paths total 1005 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0363733 +expl/Actions Std 0.873984 +expl/Actions Max 2.36853 +expl/Actions Min -2.35749 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 686211 +eval/num paths total 753 +eval/path length Mean 498 +eval/path length Std 9 +eval/path length Max 507 +eval/path length Min 489 +eval/Rewards Mean 0.00200803 +eval/Rewards Std 0.0447661 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0303837 +eval/Actions Std 0.739641 +eval/Actions Max 0.999833 +eval/Actions Min -0.999824 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 4.39491e-06 +time/evaluation sampling (s) 4.93209 +time/exploration sampling (s) 7.88052 +time/logging (s) 0.0121308 +time/saving (s) 0.0172882 +time/training (s) 19.842 +time/epoch (s) 32.6841 +time/total (s) 18825.6 +Epoch -257 +------------------------------ ---------------- +2022-05-15 23:16:47.050550 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -256 finished +------------------------------ ---------------- +epoch -256 +replay_buffer/size 999047 +trainer/num train calls 745000 +trainer/QF1 Loss 0.799204 +trainer/QF2 Loss 0.677129 +trainer/Policy Loss 57.9408 +trainer/Q1 Predictions Mean -71.0999 +trainer/Q1 Predictions Std 19.0106 +trainer/Q1 Predictions Max -1.23184 +trainer/Q1 Predictions Min -85.8568 +trainer/Q2 Predictions Mean -71.044 +trainer/Q2 Predictions Std 19.0718 +trainer/Q2 Predictions Max -0.556312 +trainer/Q2 Predictions Min -85.9559 +trainer/Q Targets Mean -71.2662 +trainer/Q Targets Std 19.2142 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1878 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00448021 +trainer/policy/mean Std 0.725305 +trainer/policy/mean Max 0.999455 +trainer/policy/mean Min -0.999066 +trainer/policy/std Mean 0.411454 +trainer/policy/std Std 0.0209231 +trainer/policy/std Max 0.433583 +trainer/policy/std Min 0.382536 +trainer/Advantage Weights Mean 13.2136 +trainer/Advantage Weights Std 29.2135 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.4239e-17 +trainer/Advantage Score Mean -0.164311 +trainer/Advantage Score Std 0.651015 +trainer/Advantage Score Max 1.56806 +trainer/Advantage Score Min -3.87906 +trainer/V1 Predictions Mean -70.9808 +trainer/V1 Predictions Std 19.2849 +trainer/V1 Predictions Max -1.71803 +trainer/V1 Predictions Min -86.244 +trainer/VF Loss 0.107068 +expl/num steps total 745000 +expl/num paths total 1006 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.185252 +expl/Actions Std 0.805489 +expl/Actions Max 2.30349 +expl/Actions Min -2.20739 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 687211 +eval/num paths total 754 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0340159 +eval/Actions Std 0.718715 +eval/Actions Max 0.999827 +eval/Actions Min -0.999417 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.13631e-05 +time/evaluation sampling (s) 4.94036 +time/exploration sampling (s) 7.14229 +time/logging (s) 0.0103106 +time/saving (s) 0.0159985 +time/training (s) 19.4975 +time/epoch (s) 31.6065 +time/total (s) 18857.2 +Epoch -256 +------------------------------ ---------------- +2022-05-15 23:17:19.167837 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -255 finished +------------------------------ ---------------- +epoch -255 +replay_buffer/size 999047 +trainer/num train calls 746000 +trainer/QF1 Loss 0.731744 +trainer/QF2 Loss 0.907813 +trainer/Policy Loss 7.1373 +trainer/Q1 Predictions Mean -72.4074 +trainer/Q1 Predictions Std 17.4241 +trainer/Q1 Predictions Max -0.425856 +trainer/Q1 Predictions Min -88.7296 +trainer/Q2 Predictions Mean -72.4457 +trainer/Q2 Predictions Std 17.4798 +trainer/Q2 Predictions Max -0.740742 +trainer/Q2 Predictions Min -89.0857 +trainer/Q Targets Mean -72.3147 +trainer/Q Targets Std 17.2321 +trainer/Q Targets Max -0.218098 +trainer/Q Targets Min -88.106 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000306265 +trainer/policy/mean Std 0.733508 +trainer/policy/mean Max 0.999453 +trainer/policy/mean Min -0.999192 +trainer/policy/std Mean 0.411609 +trainer/policy/std Std 0.0192547 +trainer/policy/std Max 0.432747 +trainer/policy/std Min 0.382428 +trainer/Advantage Weights Mean 1.87702 +trainer/Advantage Weights Std 11.1226 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.05914e-21 +trainer/Advantage Score Mean -0.524188 +trainer/Advantage Score Std 0.647047 +trainer/Advantage Score Max 1.92053 +trainer/Advantage Score Min -4.61505 +trainer/V1 Predictions Mean -72.002 +trainer/V1 Predictions Std 17.5039 +trainer/V1 Predictions Max -0.0529935 +trainer/V1 Predictions Min -88.1129 +trainer/VF Loss 0.0855494 +expl/num steps total 746000 +expl/num paths total 1008 +expl/path length Mean 500 +expl/path length Std 42 +expl/path length Max 542 +expl/path length Min 458 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0484367 +expl/Actions Std 0.833297 +expl/Actions Max 2.34268 +expl/Actions Min -2.24382 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 688211 +eval/num paths total 755 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00674667 +eval/Actions Std 0.755668 +eval/Actions Max 0.999965 +eval/Actions Min -0.999611 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.01612e-05 +time/evaluation sampling (s) 5.14557 +time/exploration sampling (s) 7.41467 +time/logging (s) 0.0122761 +time/saving (s) 0.0177376 +time/training (s) 19.513 +time/epoch (s) 32.1033 +time/total (s) 18889.4 +Epoch -255 +------------------------------ ---------------- +2022-05-15 23:17:51.752219 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -254 finished +------------------------------ ---------------- +epoch -254 +replay_buffer/size 999047 +trainer/num train calls 747000 +trainer/QF1 Loss 1.1198 +trainer/QF2 Loss 0.977169 +trainer/Policy Loss 29.3771 +trainer/Q1 Predictions Mean -71.9599 +trainer/Q1 Predictions Std 17.0564 +trainer/Q1 Predictions Max -0.237337 +trainer/Q1 Predictions Min -85.8344 +trainer/Q2 Predictions Mean -71.9255 +trainer/Q2 Predictions Std 16.9945 +trainer/Q2 Predictions Max -0.416391 +trainer/Q2 Predictions Min -85.6374 +trainer/Q Targets Mean -72.5174 +trainer/Q Targets Std 17.2112 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.1994 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00256266 +trainer/policy/mean Std 0.725476 +trainer/policy/mean Max 0.999995 +trainer/policy/mean Min -0.999684 +trainer/policy/std Mean 0.41529 +trainer/policy/std Std 0.0198732 +trainer/policy/std Max 0.436484 +trainer/policy/std Min 0.383805 +trainer/Advantage Weights Mean 6.47434 +trainer/Advantage Weights Std 21.1313 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.74428e-16 +trainer/Advantage Score Mean -0.338193 +trainer/Advantage Score Std 0.637769 +trainer/Advantage Score Max 1.16627 +trainer/Advantage Score Min -3.6285 +trainer/V1 Predictions Mean -72.2237 +trainer/V1 Predictions Std 17.3104 +trainer/V1 Predictions Max 3.01721 +trainer/V1 Predictions Min -86.1897 +trainer/VF Loss 0.0742495 +expl/num steps total 747000 +expl/num paths total 1010 +expl/path length Mean 500 +expl/path length Std 416 +expl/path length Max 916 +expl/path length Min 84 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0420707 +expl/Actions Std 0.816892 +expl/Actions Max 2.22607 +expl/Actions Min -2.38056 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 689211 +eval/num paths total 756 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.102856 +eval/Actions Std 0.721375 +eval/Actions Max 0.999925 +eval/Actions Min -0.999859 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.08618e-06 +time/evaluation sampling (s) 5.73485 +time/exploration sampling (s) 6.98613 +time/logging (s) 0.00744335 +time/saving (s) 0.0144286 +time/training (s) 19.8163 +time/epoch (s) 32.5592 +time/total (s) 18921.9 +Epoch -254 +------------------------------ ---------------- +2022-05-15 23:18:23.919709 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -253 finished +------------------------------ ---------------- +epoch -253 +replay_buffer/size 999047 +trainer/num train calls 748000 +trainer/QF1 Loss 4.7005 +trainer/QF2 Loss 4.11322 +trainer/Policy Loss 12.4127 +trainer/Q1 Predictions Mean -69.621 +trainer/Q1 Predictions Std 19.062 +trainer/Q1 Predictions Max -1.92721 +trainer/Q1 Predictions Min -86.3959 +trainer/Q2 Predictions Mean -69.525 +trainer/Q2 Predictions Std 19.0344 +trainer/Q2 Predictions Max -1.60735 +trainer/Q2 Predictions Min -86.055 +trainer/Q Targets Mean -69.421 +trainer/Q Targets Std 18.9752 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.009 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0020746 +trainer/policy/mean Std 0.715386 +trainer/policy/mean Max 0.999139 +trainer/policy/mean Min -0.999537 +trainer/policy/std Mean 0.412821 +trainer/policy/std Std 0.0202358 +trainer/policy/std Max 0.433313 +trainer/policy/std Min 0.382013 +trainer/Advantage Weights Mean 3.48348 +trainer/Advantage Weights Std 15.6876 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 0 +trainer/Advantage Score Mean -0.564006 +trainer/Advantage Score Std 0.933416 +trainer/Advantage Score Max 0.905716 +trainer/Advantage Score Min -10.7344 +trainer/V1 Predictions Mean -69.0514 +trainer/V1 Predictions Std 19.3218 +trainer/V1 Predictions Max -1.1188 +trainer/V1 Predictions Min -85.9345 +trainer/VF Loss 0.129412 +expl/num steps total 748000 +expl/num paths total 1011 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.445611 +expl/Actions Std 0.766398 +expl/Actions Max 2.40049 +expl/Actions Min -2.37364 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 690211 +eval/num paths total 757 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.475285 +eval/Actions Std 0.592673 +eval/Actions Max 0.999914 +eval/Actions Min -0.999788 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.24471e-05 +time/evaluation sampling (s) 5.34707 +time/exploration sampling (s) 6.91656 +time/logging (s) 0.013408 +time/saving (s) 0.0207198 +time/training (s) 19.8624 +time/epoch (s) 32.1602 +time/total (s) 18954.1 +Epoch -253 +------------------------------ ---------------- +2022-05-15 23:18:56.782645 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -252 finished +------------------------------ ---------------- +epoch -252 +replay_buffer/size 999047 +trainer/num train calls 749000 +trainer/QF1 Loss 1.94646 +trainer/QF2 Loss 1.70579 +trainer/Policy Loss 3.73159 +trainer/Q1 Predictions Mean -70.4393 +trainer/Q1 Predictions Std 19.208 +trainer/Q1 Predictions Max -0.0666575 +trainer/Q1 Predictions Min -86.6315 +trainer/Q2 Predictions Mean -70.5615 +trainer/Q2 Predictions Std 19.1579 +trainer/Q2 Predictions Max -0.695847 +trainer/Q2 Predictions Min -86.7592 +trainer/Q Targets Mean -70.4945 +trainer/Q Targets Std 19.2345 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3002 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.036659 +trainer/policy/mean Std 0.715187 +trainer/policy/mean Max 0.999646 +trainer/policy/mean Min -0.998808 +trainer/policy/std Mean 0.411125 +trainer/policy/std Std 0.0201231 +trainer/policy/std Max 0.433779 +trainer/policy/std Min 0.383368 +trainer/Advantage Weights Mean 1.5035 +trainer/Advantage Weights Std 9.21026 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.18581e-23 +trainer/Advantage Score Mean -0.52033 +trainer/Advantage Score Std 0.702653 +trainer/Advantage Score Max 1.68777 +trainer/Advantage Score Min -5.11372 +trainer/V1 Predictions Mean -70.321 +trainer/V1 Predictions Std 19.3394 +trainer/V1 Predictions Max -0.1774 +trainer/V1 Predictions Min -86.1127 +trainer/VF Loss 0.0956584 +expl/num steps total 749000 +expl/num paths total 1012 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.22845 +expl/Actions Std 0.843984 +expl/Actions Max 2.35605 +expl/Actions Min -2.47643 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 691211 +eval/num paths total 758 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.110477 +eval/Actions Std 0.654656 +eval/Actions Max 0.998605 +eval/Actions Min -0.999062 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.04918e-05 +time/evaluation sampling (s) 5.75367 +time/exploration sampling (s) 7.02209 +time/logging (s) 0.0114288 +time/saving (s) 0.0154525 +time/training (s) 20.0368 +time/epoch (s) 32.8394 +time/total (s) 18986.9 +Epoch -252 +------------------------------ ---------------- +2022-05-15 23:19:28.626226 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -251 finished +------------------------------ ---------------- +epoch -251 +replay_buffer/size 999047 +trainer/num train calls 750000 +trainer/QF1 Loss 0.98166 +trainer/QF2 Loss 1.02378 +trainer/Policy Loss 36.084 +trainer/Q1 Predictions Mean -70.3644 +trainer/Q1 Predictions Std 18.6334 +trainer/Q1 Predictions Max -0.841318 +trainer/Q1 Predictions Min -85.725 +trainer/Q2 Predictions Mean -70.282 +trainer/Q2 Predictions Std 18.684 +trainer/Q2 Predictions Max -0.668642 +trainer/Q2 Predictions Min -85.659 +trainer/Q Targets Mean -70.7637 +trainer/Q Targets Std 18.8224 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0306 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0286749 +trainer/policy/mean Std 0.741306 +trainer/policy/mean Max 0.999331 +trainer/policy/mean Min -0.999304 +trainer/policy/std Mean 0.412307 +trainer/policy/std Std 0.0210688 +trainer/policy/std Max 0.436511 +trainer/policy/std Min 0.380915 +trainer/Advantage Weights Mean 7.20598 +trainer/Advantage Weights Std 21.6018 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.37586e-13 +trainer/Advantage Score Mean -0.278251 +trainer/Advantage Score Std 0.667363 +trainer/Advantage Score Max 1.32336 +trainer/Advantage Score Min -2.96145 +trainer/V1 Predictions Mean -70.4505 +trainer/V1 Predictions Std 18.9253 +trainer/V1 Predictions Max 0.0207365 +trainer/V1 Predictions Min -85.8906 +trainer/VF Loss 0.0800173 +expl/num steps total 750000 +expl/num paths total 1014 +expl/path length Mean 500 +expl/path length Std 291 +expl/path length Max 791 +expl/path length Min 209 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0509458 +expl/Actions Std 0.827213 +expl/Actions Max 2.34313 +expl/Actions Min -2.40374 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 691777 +eval/num paths total 759 +eval/path length Mean 566 +eval/path length Std 0 +eval/path length Max 566 +eval/path length Min 566 +eval/Rewards Mean 0.00176678 +eval/Rewards Std 0.041996 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.047254 +eval/Actions Std 0.732976 +eval/Actions Max 0.99972 +eval/Actions Min -0.999743 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.28848e-05 +time/evaluation sampling (s) 5.24688 +time/exploration sampling (s) 7.09134 +time/logging (s) 0.00841209 +time/saving (s) 0.0149569 +time/training (s) 19.464 +time/epoch (s) 31.8256 +time/total (s) 19018.8 +Epoch -251 +------------------------------ ---------------- +2022-05-15 23:20:00.630016 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -250 finished +------------------------------ ---------------- +epoch -250 +replay_buffer/size 999047 +trainer/num train calls 751000 +trainer/QF1 Loss 0.95319 +trainer/QF2 Loss 1.00058 +trainer/Policy Loss 20.5231 +trainer/Q1 Predictions Mean -70.3637 +trainer/Q1 Predictions Std 19.2426 +trainer/Q1 Predictions Max -0.350829 +trainer/Q1 Predictions Min -86.2988 +trainer/Q2 Predictions Mean -70.3941 +trainer/Q2 Predictions Std 19.3071 +trainer/Q2 Predictions Max 0.312142 +trainer/Q2 Predictions Min -85.9141 +trainer/Q Targets Mean -70.5544 +trainer/Q Targets Std 19.5036 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0371 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0131542 +trainer/policy/mean Std 0.727505 +trainer/policy/mean Max 0.999596 +trainer/policy/mean Min -0.999269 +trainer/policy/std Mean 0.412265 +trainer/policy/std Std 0.0206915 +trainer/policy/std Max 0.43644 +trainer/policy/std Min 0.385227 +trainer/Advantage Weights Mean 5.93675 +trainer/Advantage Weights Std 20.8152 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.33255e-18 +trainer/Advantage Score Mean -0.402803 +trainer/Advantage Score Std 0.666744 +trainer/Advantage Score Max 1.05114 +trainer/Advantage Score Min -3.93264 +trainer/V1 Predictions Mean -70.3189 +trainer/V1 Predictions Std 19.5366 +trainer/V1 Predictions Max -1.00752 +trainer/V1 Predictions Min -86.7855 +trainer/VF Loss 0.0788358 +expl/num steps total 751000 +expl/num paths total 1016 +expl/path length Mean 500 +expl/path length Std 262 +expl/path length Max 762 +expl/path length Min 238 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0162849 +expl/Actions Std 0.845967 +expl/Actions Max 2.27944 +expl/Actions Min -2.70528 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 692554 +eval/num paths total 760 +eval/path length Mean 777 +eval/path length Std 0 +eval/path length Max 777 +eval/path length Min 777 +eval/Rewards Mean 0.001287 +eval/Rewards Std 0.0358517 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0254398 +eval/Actions Std 0.735146 +eval/Actions Max 0.999973 +eval/Actions Min -0.999504 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.7998e-06 +time/evaluation sampling (s) 5.31113 +time/exploration sampling (s) 6.72036 +time/logging (s) 0.00965422 +time/saving (s) 0.0296481 +time/training (s) 19.9164 +time/epoch (s) 31.9871 +time/total (s) 19050.8 +Epoch -250 +------------------------------ ---------------- +2022-05-15 23:20:32.759754 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -249 finished +------------------------------ ---------------- +epoch -249 +replay_buffer/size 999047 +trainer/num train calls 752000 +trainer/QF1 Loss 0.473027 +trainer/QF2 Loss 0.521477 +trainer/Policy Loss 6.5219 +trainer/Q1 Predictions Mean -72.2213 +trainer/Q1 Predictions Std 16.7187 +trainer/Q1 Predictions Max -0.0278613 +trainer/Q1 Predictions Min -85.9908 +trainer/Q2 Predictions Mean -72.2331 +trainer/Q2 Predictions Std 16.6298 +trainer/Q2 Predictions Max -0.491075 +trainer/Q2 Predictions Min -85.9918 +trainer/Q Targets Mean -72.099 +trainer/Q Targets Std 16.523 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.0146 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0251019 +trainer/policy/mean Std 0.723187 +trainer/policy/mean Max 0.999642 +trainer/policy/mean Min -0.99881 +trainer/policy/std Mean 0.412982 +trainer/policy/std Std 0.0196618 +trainer/policy/std Max 0.433879 +trainer/policy/std Min 0.384733 +trainer/Advantage Weights Mean 3.05695 +trainer/Advantage Weights Std 15.537 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.70037e-17 +trainer/Advantage Score Mean -0.548141 +trainer/Advantage Score Std 0.561734 +trainer/Advantage Score Max 0.849655 +trainer/Advantage Score Min -3.81506 +trainer/V1 Predictions Mean -71.8321 +trainer/V1 Predictions Std 16.7754 +trainer/V1 Predictions Max 1.68959 +trainer/V1 Predictions Min -85.7585 +trainer/VF Loss 0.0713363 +expl/num steps total 752000 +expl/num paths total 1018 +expl/path length Mean 500 +expl/path length Std 172 +expl/path length Max 672 +expl/path length Min 328 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.00142754 +expl/Actions Std 0.831273 +expl/Actions Max 2.27095 +expl/Actions Min -2.19048 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 693554 +eval/num paths total 761 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.293781 +eval/Actions Std 0.806833 +eval/Actions Max 0.999656 +eval/Actions Min -0.999715 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.81982e-06 +time/evaluation sampling (s) 5.40854 +time/exploration sampling (s) 6.38991 +time/logging (s) 0.0112997 +time/saving (s) 0.0158879 +time/training (s) 20.2881 +time/epoch (s) 32.1138 +time/total (s) 19082.9 +Epoch -249 +------------------------------ ---------------- +2022-05-15 23:21:04.877052 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -248 finished +------------------------------ ---------------- +epoch -248 +replay_buffer/size 999047 +trainer/num train calls 753000 +trainer/QF1 Loss 2.97752 +trainer/QF2 Loss 2.95606 +trainer/Policy Loss 17.5861 +trainer/Q1 Predictions Mean -71.3955 +trainer/Q1 Predictions Std 18.9871 +trainer/Q1 Predictions Max -1.47774 +trainer/Q1 Predictions Min -87.0083 +trainer/Q2 Predictions Mean -71.3144 +trainer/Q2 Predictions Std 18.8819 +trainer/Q2 Predictions Max -0.636879 +trainer/Q2 Predictions Min -86.6516 +trainer/Q Targets Mean -71.1822 +trainer/Q Targets Std 19.183 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1513 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0209614 +trainer/policy/mean Std 0.716866 +trainer/policy/mean Max 0.999612 +trainer/policy/mean Min -0.998356 +trainer/policy/std Mean 0.41415 +trainer/policy/std Std 0.0194327 +trainer/policy/std Max 0.436761 +trainer/policy/std Min 0.386881 +trainer/Advantage Weights Mean 5.54975 +trainer/Advantage Weights Std 19.7622 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.56574e-13 +trainer/Advantage Score Mean -0.345935 +trainer/Advantage Score Std 0.647586 +trainer/Advantage Score Max 3.66252 +trainer/Advantage Score Min -2.94852 +trainer/V1 Predictions Mean -71.0538 +trainer/V1 Predictions Std 19.0875 +trainer/V1 Predictions Max -0.259091 +trainer/V1 Predictions Min -87.1611 +trainer/VF Loss 0.123142 +expl/num steps total 753000 +expl/num paths total 1019 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.052464 +expl/Actions Std 0.854927 +expl/Actions Max 2.43773 +expl/Actions Min -2.18446 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 694554 +eval/num paths total 762 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0729395 +eval/Actions Std 0.700712 +eval/Actions Max 0.999953 +eval/Actions Min -0.999965 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.04201e-05 +time/evaluation sampling (s) 5.36313 +time/exploration sampling (s) 6.90477 +time/logging (s) 0.0149353 +time/saving (s) 0.0154436 +time/training (s) 19.8045 +time/epoch (s) 32.1028 +time/total (s) 19115 +Epoch -248 +------------------------------ ---------------- +2022-05-15 23:21:36.635598 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -247 finished +------------------------------ ---------------- +epoch -247 +replay_buffer/size 999047 +trainer/num train calls 754000 +trainer/QF1 Loss 0.941801 +trainer/QF2 Loss 1.10299 +trainer/Policy Loss 50.4415 +trainer/Q1 Predictions Mean -73.0396 +trainer/Q1 Predictions Std 14.4796 +trainer/Q1 Predictions Max -2.34454 +trainer/Q1 Predictions Min -87.0998 +trainer/Q2 Predictions Mean -72.9569 +trainer/Q2 Predictions Std 14.4528 +trainer/Q2 Predictions Max -2.01719 +trainer/Q2 Predictions Min -86.7956 +trainer/Q Targets Mean -73.5643 +trainer/Q Targets Std 14.6103 +trainer/Q Targets Max -2.19819 +trainer/Q Targets Min -87.6955 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0318144 +trainer/policy/mean Std 0.726694 +trainer/policy/mean Max 0.999142 +trainer/policy/mean Min -0.998621 +trainer/policy/std Mean 0.411725 +trainer/policy/std Std 0.0198092 +trainer/policy/std Max 0.433897 +trainer/policy/std Min 0.383737 +trainer/Advantage Weights Mean 12.0109 +trainer/Advantage Weights Std 27.1088 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.24814e-25 +trainer/Advantage Score Mean -0.183863 +trainer/Advantage Score Std 0.719073 +trainer/Advantage Score Max 1.42923 +trainer/Advantage Score Min -5.59068 +trainer/V1 Predictions Mean -73.2743 +trainer/V1 Predictions Std 14.8037 +trainer/V1 Predictions Max -1.86766 +trainer/V1 Predictions Min -87.407 +trainer/VF Loss 0.104639 +expl/num steps total 754000 +expl/num paths total 1020 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.193515 +expl/Actions Std 0.814906 +expl/Actions Max 2.345 +expl/Actions Min -2.52393 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 695017 +eval/num paths total 763 +eval/path length Mean 463 +eval/path length Std 0 +eval/path length Max 463 +eval/path length Min 463 +eval/Rewards Mean 0.00215983 +eval/Rewards Std 0.0464237 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0426286 +eval/Actions Std 0.725294 +eval/Actions Max 0.999438 +eval/Actions Min -0.999764 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.06869e-05 +time/evaluation sampling (s) 5.06712 +time/exploration sampling (s) 7.17425 +time/logging (s) 0.00977021 +time/saving (s) 0.0175421 +time/training (s) 19.4673 +time/epoch (s) 31.736 +time/total (s) 19146.7 +Epoch -247 +------------------------------ ---------------- +2022-05-15 23:22:08.869271 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -246 finished +------------------------------ ---------------- +epoch -246 +replay_buffer/size 999047 +trainer/num train calls 755000 +trainer/QF1 Loss 1.65699 +trainer/QF2 Loss 1.71863 +trainer/Policy Loss 5.84902 +trainer/Q1 Predictions Mean -68.1891 +trainer/Q1 Predictions Std 22.6668 +trainer/Q1 Predictions Max 0.15673 +trainer/Q1 Predictions Min -88.9456 +trainer/Q2 Predictions Mean -68.2139 +trainer/Q2 Predictions Std 22.6835 +trainer/Q2 Predictions Max 0.851478 +trainer/Q2 Predictions Min -89.0737 +trainer/Q Targets Mean -68.0072 +trainer/Q Targets Std 22.3135 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.6727 +trainer/rewards Mean -0.972656 +trainer/rewards Std 0.163083 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0273438 +trainer/terminals Std 0.163083 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0011097 +trainer/policy/mean Std 0.730515 +trainer/policy/mean Max 0.999255 +trainer/policy/mean Min -0.999907 +trainer/policy/std Mean 0.413174 +trainer/policy/std Std 0.0199875 +trainer/policy/std Max 0.435055 +trainer/policy/std Min 0.383897 +trainer/Advantage Weights Mean 2.3509 +trainer/Advantage Weights Std 14.0911 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.86488e-24 +trainer/Advantage Score Mean -0.710893 +trainer/Advantage Score Std 0.748613 +trainer/Advantage Score Max 2.01102 +trainer/Advantage Score Min -5.368 +trainer/V1 Predictions Mean -67.6727 +trainer/V1 Predictions Std 22.3931 +trainer/V1 Predictions Max 0.864344 +trainer/V1 Predictions Min -88.4742 +trainer/VF Loss 0.128823 +expl/num steps total 755000 +expl/num paths total 1022 +expl/path length Mean 500 +expl/path length Std 79 +expl/path length Max 579 +expl/path length Min 421 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.12757 +expl/Actions Std 0.817146 +expl/Actions Max 2.33762 +expl/Actions Min -2.16767 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 696017 +eval/num paths total 764 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00156395 +eval/Actions Std 0.763236 +eval/Actions Max 0.999239 +eval/Actions Min -0.999876 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.04788e-05 +time/evaluation sampling (s) 5.25966 +time/exploration sampling (s) 7.14631 +time/logging (s) 0.0115326 +time/saving (s) 0.0160025 +time/training (s) 19.782 +time/epoch (s) 32.2155 +time/total (s) 19179 +Epoch -246 +------------------------------ ---------------- +2022-05-15 23:22:40.753963 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -245 finished +------------------------------ ---------------- +epoch -245 +replay_buffer/size 999047 +trainer/num train calls 756000 +trainer/QF1 Loss 0.843177 +trainer/QF2 Loss 0.786635 +trainer/Policy Loss 19.7169 +trainer/Q1 Predictions Mean -69.3876 +trainer/Q1 Predictions Std 19.5722 +trainer/Q1 Predictions Max -0.789822 +trainer/Q1 Predictions Min -86.2078 +trainer/Q2 Predictions Mean -69.3504 +trainer/Q2 Predictions Std 19.6248 +trainer/Q2 Predictions Max -0.717152 +trainer/Q2 Predictions Min -86.2449 +trainer/Q Targets Mean -69.2247 +trainer/Q Targets Std 19.3558 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9757 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0224858 +trainer/policy/mean Std 0.722306 +trainer/policy/mean Max 0.998026 +trainer/policy/mean Min -0.999189 +trainer/policy/std Mean 0.412838 +trainer/policy/std Std 0.0199984 +trainer/policy/std Max 0.435893 +trainer/policy/std Min 0.386764 +trainer/Advantage Weights Mean 6.35132 +trainer/Advantage Weights Std 22.6877 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.95759e-12 +trainer/Advantage Score Mean -0.395023 +trainer/Advantage Score Std 0.582875 +trainer/Advantage Score Max 2.12988 +trainer/Advantage Score Min -2.69593 +trainer/V1 Predictions Mean -68.9408 +trainer/V1 Predictions Std 19.6107 +trainer/V1 Predictions Max 0.00666958 +trainer/V1 Predictions Min -85.7592 +trainer/VF Loss 0.0953207 +expl/num steps total 756000 +expl/num paths total 1023 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.27515 +expl/Actions Std 0.781141 +expl/Actions Max 2.24044 +expl/Actions Min -2.32426 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 696469 +eval/num paths total 765 +eval/path length Mean 452 +eval/path length Std 0 +eval/path length Max 452 +eval/path length Min 452 +eval/Rewards Mean 0.00221239 +eval/Rewards Std 0.046984 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00937509 +eval/Actions Std 0.748923 +eval/Actions Max 0.999844 +eval/Actions Min -0.999726 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.38357e-05 +time/evaluation sampling (s) 5.39025 +time/exploration sampling (s) 6.84315 +time/logging (s) 0.00889264 +time/saving (s) 0.0158262 +time/training (s) 19.6028 +time/epoch (s) 31.8609 +time/total (s) 19210.8 +Epoch -245 +------------------------------ ---------------- +2022-05-15 23:23:13.381518 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -244 finished +------------------------------ ---------------- +epoch -244 +replay_buffer/size 999047 +trainer/num train calls 757000 +trainer/QF1 Loss 1.93453 +trainer/QF2 Loss 1.82647 +trainer/Policy Loss 34.2849 +trainer/Q1 Predictions Mean -71.057 +trainer/Q1 Predictions Std 19.2548 +trainer/Q1 Predictions Max 0.227022 +trainer/Q1 Predictions Min -87.8898 +trainer/Q2 Predictions Mean -71.102 +trainer/Q2 Predictions Std 19.1547 +trainer/Q2 Predictions Max -0.398571 +trainer/Q2 Predictions Min -87.7172 +trainer/Q Targets Mean -71.522 +trainer/Q Targets Std 19.0997 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5304 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0063988 +trainer/policy/mean Std 0.723788 +trainer/policy/mean Max 0.99954 +trainer/policy/mean Min -0.999637 +trainer/policy/std Mean 0.412473 +trainer/policy/std Std 0.0209995 +trainer/policy/std Max 0.435267 +trainer/policy/std Min 0.381626 +trainer/Advantage Weights Mean 7.32044 +trainer/Advantage Weights Std 23.0349 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.15454e-13 +trainer/Advantage Score Mean -0.33086 +trainer/Advantage Score Std 0.587457 +trainer/Advantage Score Max 1.61147 +trainer/Advantage Score Min -2.9166 +trainer/V1 Predictions Mean -71.2174 +trainer/V1 Predictions Std 19.1493 +trainer/V1 Predictions Max 0.157767 +trainer/V1 Predictions Min -88.5445 +trainer/VF Loss 0.0809632 +expl/num steps total 757000 +expl/num paths total 1024 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0184437 +expl/Actions Std 0.860529 +expl/Actions Max 2.30583 +expl/Actions Min -2.4702 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 697184 +eval/num paths total 766 +eval/path length Mean 715 +eval/path length Std 0 +eval/path length Max 715 +eval/path length Min 715 +eval/Rewards Mean 0.0013986 +eval/Rewards Std 0.0373717 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0206034 +eval/Actions Std 0.735866 +eval/Actions Max 0.999703 +eval/Actions Min -0.999748 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 7.95815e-06 +time/evaluation sampling (s) 5.16012 +time/exploration sampling (s) 7.46407 +time/logging (s) 0.00990519 +time/saving (s) 0.015901 +time/training (s) 19.9606 +time/epoch (s) 32.6106 +time/total (s) 19243.4 +Epoch -244 +------------------------------ ---------------- +2022-05-15 23:23:45.781325 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -243 finished +------------------------------ ---------------- +epoch -243 +replay_buffer/size 999047 +trainer/num train calls 758000 +trainer/QF1 Loss 0.865125 +trainer/QF2 Loss 0.964532 +trainer/Policy Loss 11.3044 +trainer/Q1 Predictions Mean -72.2179 +trainer/Q1 Predictions Std 17.6277 +trainer/Q1 Predictions Max -0.716562 +trainer/Q1 Predictions Min -86.2144 +trainer/Q2 Predictions Mean -72.1539 +trainer/Q2 Predictions Std 17.6302 +trainer/Q2 Predictions Max -0.379828 +trainer/Q2 Predictions Min -86.1343 +trainer/Q Targets Mean -72.2809 +trainer/Q Targets Std 17.5799 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.1846 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0192373 +trainer/policy/mean Std 0.728914 +trainer/policy/mean Max 0.999777 +trainer/policy/mean Min -0.999774 +trainer/policy/std Mean 0.412305 +trainer/policy/std Std 0.0204847 +trainer/policy/std Max 0.434191 +trainer/policy/std Min 0.379881 +trainer/Advantage Weights Mean 4.27453 +trainer/Advantage Weights Std 18.5463 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.71551e-21 +trainer/Advantage Score Mean -0.490353 +trainer/Advantage Score Std 0.681226 +trainer/Advantage Score Max 1.9632 +trainer/Advantage Score Min -4.73553 +trainer/V1 Predictions Mean -71.9656 +trainer/V1 Predictions Std 17.7026 +trainer/V1 Predictions Max 0.69308 +trainer/V1 Predictions Min -86.3006 +trainer/VF Loss 0.108389 +expl/num steps total 758000 +expl/num paths total 1026 +expl/path length Mean 500 +expl/path length Std 452 +expl/path length Max 952 +expl/path length Min 48 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0358742 +expl/Actions Std 0.831849 +expl/Actions Max 2.39003 +expl/Actions Min -2.13434 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 698184 +eval/num paths total 767 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.057558 +eval/Actions Std 0.688666 +eval/Actions Max 0.999703 +eval/Actions Min -0.999772 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.22553e-05 +time/evaluation sampling (s) 4.98597 +time/exploration sampling (s) 7.28927 +time/logging (s) 0.0125134 +time/saving (s) 0.017949 +time/training (s) 20.0782 +time/epoch (s) 32.3839 +time/total (s) 19275.8 +Epoch -243 +------------------------------ ---------------- +2022-05-15 23:24:18.968415 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -242 finished +------------------------------ ---------------- +epoch -242 +replay_buffer/size 999047 +trainer/num train calls 759000 +trainer/QF1 Loss 0.708332 +trainer/QF2 Loss 0.604461 +trainer/Policy Loss 26.4671 +trainer/Q1 Predictions Mean -71.5208 +trainer/Q1 Predictions Std 18.5877 +trainer/Q1 Predictions Max -0.785505 +trainer/Q1 Predictions Min -86.3495 +trainer/Q2 Predictions Mean -71.5252 +trainer/Q2 Predictions Std 18.632 +trainer/Q2 Predictions Max -0.783807 +trainer/Q2 Predictions Min -86.4024 +trainer/Q Targets Mean -71.2341 +trainer/Q Targets Std 18.7879 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2032 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0098434 +trainer/policy/mean Std 0.724655 +trainer/policy/mean Max 0.999124 +trainer/policy/mean Min -0.999413 +trainer/policy/std Mean 0.411955 +trainer/policy/std Std 0.0200975 +trainer/policy/std Max 0.431437 +trainer/policy/std Min 0.382672 +trainer/Advantage Weights Mean 4.06135 +trainer/Advantage Weights Std 16.2315 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.14693e-23 +trainer/Advantage Score Mean -0.42761 +trainer/Advantage Score Std 0.741659 +trainer/Advantage Score Max 0.753479 +trainer/Advantage Score Min -5.08618 +trainer/V1 Predictions Mean -70.9049 +trainer/V1 Predictions Std 19.0268 +trainer/V1 Predictions Max 1.14162 +trainer/V1 Predictions Min -86.1997 +trainer/VF Loss 0.085137 +expl/num steps total 759000 +expl/num paths total 1027 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.000108814 +expl/Actions Std 0.892194 +expl/Actions Max 2.19338 +expl/Actions Min -2.37138 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 699184 +eval/num paths total 768 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0470643 +eval/Actions Std 0.740966 +eval/Actions Max 0.999383 +eval/Actions Min -0.99943 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02269e-05 +time/evaluation sampling (s) 5.89327 +time/exploration sampling (s) 6.86562 +time/logging (s) 0.0122862 +time/saving (s) 0.0187876 +time/training (s) 20.3759 +time/epoch (s) 33.1659 +time/total (s) 19309 +Epoch -242 +------------------------------ ---------------- +2022-05-15 23:24:51.044091 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -241 finished +------------------------------ ---------------- +epoch -241 +replay_buffer/size 999047 +trainer/num train calls 760000 +trainer/QF1 Loss 0.968233 +trainer/QF2 Loss 1.02213 +trainer/Policy Loss 14.7164 +trainer/Q1 Predictions Mean -70.9297 +trainer/Q1 Predictions Std 18.7413 +trainer/Q1 Predictions Max -1.35167 +trainer/Q1 Predictions Min -88.9084 +trainer/Q2 Predictions Mean -70.8812 +trainer/Q2 Predictions Std 18.8756 +trainer/Q2 Predictions Max -0.512695 +trainer/Q2 Predictions Min -89.0052 +trainer/Q Targets Mean -70.8484 +trainer/Q Targets Std 18.6949 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9681 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00439775 +trainer/policy/mean Std 0.73542 +trainer/policy/mean Max 0.999564 +trainer/policy/mean Min -0.998741 +trainer/policy/std Mean 0.412052 +trainer/policy/std Std 0.0201201 +trainer/policy/std Max 0.434445 +trainer/policy/std Min 0.384826 +trainer/Advantage Weights Mean 3.75686 +trainer/Advantage Weights Std 16.7769 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.55486e-16 +trainer/Advantage Score Mean -0.472432 +trainer/Advantage Score Std 0.599476 +trainer/Advantage Score Max 0.982964 +trainer/Advantage Score Min -3.46949 +trainer/V1 Predictions Mean -70.5441 +trainer/V1 Predictions Std 18.7423 +trainer/V1 Predictions Max -0.579617 +trainer/V1 Predictions Min -88.016 +trainer/VF Loss 0.0720583 +expl/num steps total 760000 +expl/num paths total 1028 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0900649 +expl/Actions Std 0.843233 +expl/Actions Max 2.27882 +expl/Actions Min -2.44142 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 700184 +eval/num paths total 769 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.177017 +eval/Actions Std 0.838395 +eval/Actions Max 0.999931 +eval/Actions Min -0.995319 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 7.83429e-06 +time/evaluation sampling (s) 5.68508 +time/exploration sampling (s) 6.5493 +time/logging (s) 0.00870745 +time/saving (s) 0.0172055 +time/training (s) 19.7915 +time/epoch (s) 32.0518 +time/total (s) 19341.1 +Epoch -241 +------------------------------ ---------------- +2022-05-15 23:25:21.972395 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -240 finished +------------------------------ ---------------- +epoch -240 +replay_buffer/size 999047 +trainer/num train calls 761000 +trainer/QF1 Loss 0.466256 +trainer/QF2 Loss 0.642003 +trainer/Policy Loss 16.6758 +trainer/Q1 Predictions Mean -71.3784 +trainer/Q1 Predictions Std 18.2579 +trainer/Q1 Predictions Max -0.734283 +trainer/Q1 Predictions Min -88.3672 +trainer/Q2 Predictions Mean -71.4402 +trainer/Q2 Predictions Std 18.3791 +trainer/Q2 Predictions Max -0.637064 +trainer/Q2 Predictions Min -88.7667 +trainer/Q Targets Mean -71.4025 +trainer/Q Targets Std 18.343 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.755 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00680272 +trainer/policy/mean Std 0.707328 +trainer/policy/mean Max 0.999747 +trainer/policy/mean Min -0.998016 +trainer/policy/std Mean 0.413655 +trainer/policy/std Std 0.0210892 +trainer/policy/std Max 0.437109 +trainer/policy/std Min 0.38571 +trainer/Advantage Weights Mean 2.51749 +trainer/Advantage Weights Std 12.2914 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.34078e-18 +trainer/Advantage Score Mean -0.493075 +trainer/Advantage Score Std 0.612632 +trainer/Advantage Score Max 0.629784 +trainer/Advantage Score Min -3.94531 +trainer/V1 Predictions Mean -71.1202 +trainer/V1 Predictions Std 18.5632 +trainer/V1 Predictions Max 0.135341 +trainer/V1 Predictions Min -88.9725 +trainer/VF Loss 0.068293 +expl/num steps total 761000 +expl/num paths total 1030 +expl/path length Mean 500 +expl/path length Std 496 +expl/path length Max 996 +expl/path length Min 4 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0288672 +expl/Actions Std 0.823288 +expl/Actions Max 2.33638 +expl/Actions Min -2.54871 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 700806 +eval/num paths total 770 +eval/path length Mean 622 +eval/path length Std 0 +eval/path length Max 622 +eval/path length Min 622 +eval/Rewards Mean 0.00160772 +eval/Rewards Std 0.0400641 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.028275 +eval/Actions Std 0.737303 +eval/Actions Max 0.999891 +eval/Actions Min -0.999842 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.24401e-05 +time/evaluation sampling (s) 4.75464 +time/exploration sampling (s) 6.25392 +time/logging (s) 0.0105811 +time/saving (s) 0.0179343 +time/training (s) 19.8735 +time/epoch (s) 30.9106 +time/total (s) 19372 +Epoch -240 +------------------------------ ---------------- +2022-05-15 23:25:53.750143 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -239 finished +------------------------------ ---------------- +epoch -239 +replay_buffer/size 999047 +trainer/num train calls 762000 +trainer/QF1 Loss 0.550861 +trainer/QF2 Loss 0.606822 +trainer/Policy Loss 19.6932 +trainer/Q1 Predictions Mean -69.3851 +trainer/Q1 Predictions Std 20.107 +trainer/Q1 Predictions Max -0.350849 +trainer/Q1 Predictions Min -86.4723 +trainer/Q2 Predictions Mean -69.4557 +trainer/Q2 Predictions Std 19.9519 +trainer/Q2 Predictions Max 0.12234 +trainer/Q2 Predictions Min -86.8411 +trainer/Q Targets Mean -69.4645 +trainer/Q Targets Std 20.0643 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7458 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0175469 +trainer/policy/mean Std 0.735975 +trainer/policy/mean Max 0.999204 +trainer/policy/mean Min -0.999155 +trainer/policy/std Mean 0.415457 +trainer/policy/std Std 0.0218362 +trainer/policy/std Max 0.437798 +trainer/policy/std Min 0.383662 +trainer/Advantage Weights Mean 5.49998 +trainer/Advantage Weights Std 19.8868 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.49094e-25 +trainer/Advantage Score Mean -0.424801 +trainer/Advantage Score Std 0.739339 +trainer/Advantage Score Max 1.46007 +trainer/Advantage Score Min -5.60626 +trainer/V1 Predictions Mean -69.1087 +trainer/V1 Predictions Std 20.3002 +trainer/V1 Predictions Max 0.235786 +trainer/V1 Predictions Min -86.6578 +trainer/VF Loss 0.102702 +expl/num steps total 762000 +expl/num paths total 1032 +expl/path length Mean 500 +expl/path length Std 133 +expl/path length Max 633 +expl/path length Min 367 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0213008 +expl/Actions Std 0.828702 +expl/Actions Max 2.4497 +expl/Actions Min -2.62452 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 701806 +eval/num paths total 771 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0593693 +eval/Actions Std 0.723813 +eval/Actions Max 0.999832 +eval/Actions Min -0.999876 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.0876e-05 +time/evaluation sampling (s) 5.28689 +time/exploration sampling (s) 6.78418 +time/logging (s) 0.0107052 +time/saving (s) 0.0174058 +time/training (s) 19.6583 +time/epoch (s) 31.7575 +time/total (s) 19403.7 +Epoch -239 +------------------------------ ---------------- +2022-05-15 23:26:26.124546 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -238 finished +------------------------------ ---------------- +epoch -238 +replay_buffer/size 999047 +trainer/num train calls 763000 +trainer/QF1 Loss 1.12237 +trainer/QF2 Loss 1.28418 +trainer/Policy Loss 38.8513 +trainer/Q1 Predictions Mean -70.2875 +trainer/Q1 Predictions Std 19.4568 +trainer/Q1 Predictions Max -1.17198 +trainer/Q1 Predictions Min -86.9562 +trainer/Q2 Predictions Mean -70.2342 +trainer/Q2 Predictions Std 19.4786 +trainer/Q2 Predictions Max -1.34014 +trainer/Q2 Predictions Min -86.3225 +trainer/Q Targets Mean -70.5962 +trainer/Q Targets Std 19.8096 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2027 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.029268 +trainer/policy/mean Std 0.724856 +trainer/policy/mean Max 0.999472 +trainer/policy/mean Min -0.999373 +trainer/policy/std Mean 0.414584 +trainer/policy/std Std 0.0212617 +trainer/policy/std Max 0.435677 +trainer/policy/std Min 0.383382 +trainer/Advantage Weights Mean 9.94825 +trainer/Advantage Weights Std 26.0221 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.67572e-27 +trainer/Advantage Score Mean -0.297684 +trainer/Advantage Score Std 0.859712 +trainer/Advantage Score Max 1.76134 +trainer/Advantage Score Min -6.16536 +trainer/V1 Predictions Mean -70.2505 +trainer/V1 Predictions Std 19.9733 +trainer/V1 Predictions Max -1.02432 +trainer/V1 Predictions Min -86.2698 +trainer/VF Loss 0.14339 +expl/num steps total 763000 +expl/num paths total 1034 +expl/path length Mean 500 +expl/path length Std 180 +expl/path length Max 680 +expl/path length Min 320 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00690709 +expl/Actions Std 0.842278 +expl/Actions Max 2.18065 +expl/Actions Min -2.39627 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 702806 +eval/num paths total 772 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.338767 +eval/Actions Std 0.731402 +eval/Actions Max 0.999623 +eval/Actions Min -0.999816 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2876e-05 +time/evaluation sampling (s) 5.32885 +time/exploration sampling (s) 7.18063 +time/logging (s) 0.0125472 +time/saving (s) 0.0180555 +time/training (s) 19.8185 +time/epoch (s) 32.3586 +time/total (s) 19436.1 +Epoch -238 +------------------------------ ---------------- +2022-05-15 23:26:57.845307 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -237 finished +------------------------------ ---------------- +epoch -237 +replay_buffer/size 999047 +trainer/num train calls 764000 +trainer/QF1 Loss 0.7797 +trainer/QF2 Loss 0.817264 +trainer/Policy Loss 12.2082 +trainer/Q1 Predictions Mean -72.9096 +trainer/Q1 Predictions Std 17.7053 +trainer/Q1 Predictions Max -1.34972 +trainer/Q1 Predictions Min -87.2588 +trainer/Q2 Predictions Mean -72.9332 +trainer/Q2 Predictions Std 17.61 +trainer/Q2 Predictions Max -1.36037 +trainer/Q2 Predictions Min -87.3211 +trainer/Q Targets Mean -72.7582 +trainer/Q Targets Std 17.7533 +trainer/Q Targets Max -1.23772 +trainer/Q Targets Min -87.4071 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0155817 +trainer/policy/mean Std 0.720468 +trainer/policy/mean Max 0.999643 +trainer/policy/mean Min -0.998052 +trainer/policy/std Mean 0.414116 +trainer/policy/std Std 0.0207399 +trainer/policy/std Max 0.437294 +trainer/policy/std Min 0.383803 +trainer/Advantage Weights Mean 3.43152 +trainer/Advantage Weights Std 16.457 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.74437e-17 +trainer/Advantage Score Mean -0.469766 +trainer/Advantage Score Std 0.636627 +trainer/Advantage Score Max 1.36046 +trainer/Advantage Score Min -3.85875 +trainer/V1 Predictions Mean -72.5021 +trainer/V1 Predictions Std 17.8497 +trainer/V1 Predictions Max -0.0508868 +trainer/V1 Predictions Min -86.9546 +trainer/VF Loss 0.0799417 +expl/num steps total 764000 +expl/num paths total 1036 +expl/path length Mean 500 +expl/path length Std 241 +expl/path length Max 741 +expl/path length Min 259 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0495735 +expl/Actions Std 0.830452 +expl/Actions Max 2.95405 +expl/Actions Min -2.41844 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 703806 +eval/num paths total 773 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0442333 +eval/Actions Std 0.713668 +eval/Actions Max 0.999824 +eval/Actions Min -0.999614 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.1013e-06 +time/evaluation sampling (s) 5.17087 +time/exploration sampling (s) 6.6631 +time/logging (s) 0.0139174 +time/saving (s) 0.0228814 +time/training (s) 19.8307 +time/epoch (s) 31.7015 +time/total (s) 19467.8 +Epoch -237 +------------------------------ ---------------- +2022-05-15 23:27:28.937643 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -236 finished +------------------------------ ---------------- +epoch -236 +replay_buffer/size 999047 +trainer/num train calls 765000 +trainer/QF1 Loss 1.20114 +trainer/QF2 Loss 1.19123 +trainer/Policy Loss 35.8258 +trainer/Q1 Predictions Mean -70.6732 +trainer/Q1 Predictions Std 19.343 +trainer/Q1 Predictions Max -1.03824 +trainer/Q1 Predictions Min -86.872 +trainer/Q2 Predictions Mean -70.7955 +trainer/Q2 Predictions Std 19.2904 +trainer/Q2 Predictions Max -1.62437 +trainer/Q2 Predictions Min -87.2047 +trainer/Q Targets Mean -70.8075 +trainer/Q Targets Std 19.5033 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8599 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0134563 +trainer/policy/mean Std 0.733296 +trainer/policy/mean Max 0.999017 +trainer/policy/mean Min -0.998645 +trainer/policy/std Mean 0.412547 +trainer/policy/std Std 0.0205367 +trainer/policy/std Max 0.432635 +trainer/policy/std Min 0.385552 +trainer/Advantage Weights Mean 8.52237 +trainer/Advantage Weights Std 25.3414 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.0614e-16 +trainer/Advantage Score Mean -0.346535 +trainer/Advantage Score Std 0.72003 +trainer/Advantage Score Max 1.69178 +trainer/Advantage Score Min -3.67818 +trainer/V1 Predictions Mean -70.5335 +trainer/V1 Predictions Std 19.52 +trainer/V1 Predictions Max -0.727197 +trainer/V1 Predictions Min -86.7543 +trainer/VF Loss 0.116637 +expl/num steps total 765000 +expl/num paths total 1038 +expl/path length Mean 500 +expl/path length Std 194 +expl/path length Max 694 +expl/path length Min 306 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0469257 +expl/Actions Std 0.820107 +expl/Actions Max 2.33665 +expl/Actions Min -2.45961 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 704806 +eval/num paths total 774 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.14414 +eval/Actions Std 0.797612 +eval/Actions Max 0.999979 +eval/Actions Min -0.999154 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.73767e-06 +time/evaluation sampling (s) 5.25528 +time/exploration sampling (s) 6.10825 +time/logging (s) 0.0125022 +time/saving (s) 0.0180902 +time/training (s) 19.6812 +time/epoch (s) 31.0753 +time/total (s) 19498.9 +Epoch -236 +------------------------------ ---------------- +2022-05-15 23:27:59.921884 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -235 finished +------------------------------ ---------------- +epoch -235 +replay_buffer/size 999047 +trainer/num train calls 766000 +trainer/QF1 Loss 0.645508 +trainer/QF2 Loss 0.656261 +trainer/Policy Loss 7.1596 +trainer/Q1 Predictions Mean -72.7745 +trainer/Q1 Predictions Std 17.7676 +trainer/Q1 Predictions Max -2.38979 +trainer/Q1 Predictions Min -88.063 +trainer/Q2 Predictions Mean -72.7546 +trainer/Q2 Predictions Std 17.7881 +trainer/Q2 Predictions Max -1.64899 +trainer/Q2 Predictions Min -87.7501 +trainer/Q Targets Mean -72.5451 +trainer/Q Targets Std 17.9084 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9617 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0283204 +trainer/policy/mean Std 0.731438 +trainer/policy/mean Max 0.9996 +trainer/policy/mean Min -0.999208 +trainer/policy/std Mean 0.412479 +trainer/policy/std Std 0.0199139 +trainer/policy/std Max 0.431666 +trainer/policy/std Min 0.385826 +trainer/Advantage Weights Mean 3.09695 +trainer/Advantage Weights Std 16.355 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.68143e-18 +trainer/Advantage Score Mean -0.538536 +trainer/Advantage Score Std 0.659598 +trainer/Advantage Score Max 1.51445 +trainer/Advantage Score Min -4.09269 +trainer/V1 Predictions Mean -72.2976 +trainer/V1 Predictions Std 17.9082 +trainer/V1 Predictions Max -3.59266 +trainer/V1 Predictions Min -87.9318 +trainer/VF Loss 0.100909 +expl/num steps total 766000 +expl/num paths total 1040 +expl/path length Mean 500 +expl/path length Std 13 +expl/path length Max 513 +expl/path length Min 487 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0615572 +expl/Actions Std 0.821506 +expl/Actions Max 2.75909 +expl/Actions Min -2.28141 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 705338 +eval/num paths total 775 +eval/path length Mean 532 +eval/path length Std 0 +eval/path length Max 532 +eval/path length Min 532 +eval/Rewards Mean 0.0018797 +eval/Rewards Std 0.0433147 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0340822 +eval/Actions Std 0.744681 +eval/Actions Max 0.999806 +eval/Actions Min -0.999802 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.25901e-05 +time/evaluation sampling (s) 4.90593 +time/exploration sampling (s) 7.15684 +time/logging (s) 0.00710239 +time/saving (s) 0.01519 +time/training (s) 18.8736 +time/epoch (s) 30.9587 +time/total (s) 19529.9 +Epoch -235 +------------------------------ ---------------- +2022-05-15 23:28:31.493059 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -234 finished +------------------------------ ---------------- +epoch -234 +replay_buffer/size 999047 +trainer/num train calls 767000 +trainer/QF1 Loss 0.510819 +trainer/QF2 Loss 0.614434 +trainer/Policy Loss 7.31009 +trainer/Q1 Predictions Mean -73.2118 +trainer/Q1 Predictions Std 17.5996 +trainer/Q1 Predictions Max -1.46104 +trainer/Q1 Predictions Min -87.4022 +trainer/Q2 Predictions Mean -73.302 +trainer/Q2 Predictions Std 17.6101 +trainer/Q2 Predictions Max -1.51373 +trainer/Q2 Predictions Min -86.9937 +trainer/Q Targets Mean -73.0801 +trainer/Q Targets Std 17.6436 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9996 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0115544 +trainer/policy/mean Std 0.732484 +trainer/policy/mean Max 0.999676 +trainer/policy/mean Min -0.999532 +trainer/policy/std Mean 0.41256 +trainer/policy/std Std 0.0208681 +trainer/policy/std Max 0.434679 +trainer/policy/std Min 0.383498 +trainer/Advantage Weights Mean 2.01907 +trainer/Advantage Weights Std 12.7197 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.93343e-16 +trainer/Advantage Score Mean -0.565155 +trainer/Advantage Score Std 0.505663 +trainer/Advantage Score Max 0.719635 +trainer/Advantage Score Min -3.57652 +trainer/V1 Predictions Mean -72.8505 +trainer/V1 Predictions Std 17.6542 +trainer/V1 Predictions Max -1.20943 +trainer/V1 Predictions Min -86.788 +trainer/VF Loss 0.0635739 +expl/num steps total 767000 +expl/num paths total 1041 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0323644 +expl/Actions Std 0.844392 +expl/Actions Max 2.34106 +expl/Actions Min -2.1769 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 706226 +eval/num paths total 776 +eval/path length Mean 888 +eval/path length Std 0 +eval/path length Max 888 +eval/path length Min 888 +eval/Rewards Mean 0.00112613 +eval/Rewards Std 0.0335389 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.043083 +eval/Actions Std 0.725565 +eval/Actions Max 0.999913 +eval/Actions Min -0.999856 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.19451e-05 +time/evaluation sampling (s) 5.50739 +time/exploration sampling (s) 7.05271 +time/logging (s) 0.00768264 +time/saving (s) 0.0124364 +time/training (s) 18.9736 +time/epoch (s) 31.5539 +time/total (s) 19561.4 +Epoch -234 +------------------------------ ---------------- +2022-05-15 23:29:04.538702 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -233 finished +------------------------------ ---------------- +epoch -233 +replay_buffer/size 999047 +trainer/num train calls 768000 +trainer/QF1 Loss 0.824206 +trainer/QF2 Loss 0.786366 +trainer/Policy Loss 23.3639 +trainer/Q1 Predictions Mean -71.8859 +trainer/Q1 Predictions Std 17.3205 +trainer/Q1 Predictions Max -4.84269 +trainer/Q1 Predictions Min -86.1306 +trainer/Q2 Predictions Mean -71.8106 +trainer/Q2 Predictions Std 17.4117 +trainer/Q2 Predictions Max -5.45887 +trainer/Q2 Predictions Min -87.0474 +trainer/Q Targets Mean -72.0661 +trainer/Q Targets Std 17.3879 +trainer/Q Targets Max -5.29203 +trainer/Q Targets Min -86.9987 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0109018 +trainer/policy/mean Std 0.731072 +trainer/policy/mean Max 0.998466 +trainer/policy/mean Min -0.99962 +trainer/policy/std Mean 0.410942 +trainer/policy/std Std 0.0203532 +trainer/policy/std Max 0.433004 +trainer/policy/std Min 0.382843 +trainer/Advantage Weights Mean 5.70844 +trainer/Advantage Weights Std 20.6682 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.28289e-22 +trainer/Advantage Score Mean -0.382924 +trainer/Advantage Score Std 0.725615 +trainer/Advantage Score Max 2.45634 +trainer/Advantage Score Min -4.98314 +trainer/V1 Predictions Mean -71.8558 +trainer/V1 Predictions Std 17.4327 +trainer/V1 Predictions Max -6.17143 +trainer/V1 Predictions Min -86.4368 +trainer/VF Loss 0.128407 +expl/num steps total 768000 +expl/num paths total 1043 +expl/path length Mean 500 +expl/path length Std 67 +expl/path length Max 567 +expl/path length Min 433 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.00218971 +expl/Actions Std 0.828371 +expl/Actions Max 2.44301 +expl/Actions Min -2.21 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 707226 +eval/num paths total 777 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.120401 +eval/Actions Std 0.667595 +eval/Actions Max 0.999885 +eval/Actions Min -0.999881 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.136e-06 +time/evaluation sampling (s) 5.73778 +time/exploration sampling (s) 7.52198 +time/logging (s) 0.0122922 +time/saving (s) 0.0178959 +time/training (s) 19.747 +time/epoch (s) 33.037 +time/total (s) 19594.5 +Epoch -233 +------------------------------ ---------------- +2022-05-15 23:29:37.107888 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -232 finished +------------------------------ ---------------- +epoch -232 +replay_buffer/size 999047 +trainer/num train calls 769000 +trainer/QF1 Loss 0.918592 +trainer/QF2 Loss 0.81609 +trainer/Policy Loss 20.7181 +trainer/Q1 Predictions Mean -70.6856 +trainer/Q1 Predictions Std 16.8814 +trainer/Q1 Predictions Max -3.61728 +trainer/Q1 Predictions Min -86.8742 +trainer/Q2 Predictions Mean -70.5818 +trainer/Q2 Predictions Std 16.8555 +trainer/Q2 Predictions Max -3.83795 +trainer/Q2 Predictions Min -86.8225 +trainer/Q Targets Mean -70.4879 +trainer/Q Targets Std 16.9309 +trainer/Q Targets Max -6.29723 +trainer/Q Targets Min -86.9709 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00825645 +trainer/policy/mean Std 0.729873 +trainer/policy/mean Max 0.998218 +trainer/policy/mean Min -0.998774 +trainer/policy/std Mean 0.412164 +trainer/policy/std Std 0.0191412 +trainer/policy/std Max 0.432761 +trainer/policy/std Min 0.384042 +trainer/Advantage Weights Mean 6.65409 +trainer/Advantage Weights Std 22.3745 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.74063e-18 +trainer/Advantage Score Mean -0.453322 +trainer/Advantage Score Std 0.753755 +trainer/Advantage Score Max 0.989609 +trainer/Advantage Score Min -3.92786 +trainer/V1 Predictions Mean -70.1916 +trainer/V1 Predictions Std 17.0185 +trainer/V1 Predictions Max -4.23074 +trainer/V1 Predictions Min -86.7261 +trainer/VF Loss 0.0979989 +expl/num steps total 769000 +expl/num paths total 1045 +expl/path length Mean 500 +expl/path length Std 360 +expl/path length Max 860 +expl/path length Min 140 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0395972 +expl/Actions Std 0.839459 +expl/Actions Max 2.29586 +expl/Actions Min -2.27601 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 708226 +eval/num paths total 778 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0820149 +eval/Actions Std 0.694606 +eval/Actions Max 0.999877 +eval/Actions Min -0.999648 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.23286e-06 +time/evaluation sampling (s) 5.68532 +time/exploration sampling (s) 6.74852 +time/logging (s) 0.00749846 +time/saving (s) 0.0115462 +time/training (s) 20.0904 +time/epoch (s) 32.5433 +time/total (s) 19627 +Epoch -232 +------------------------------ ---------------- +2022-05-15 23:30:07.556370 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -231 finished +------------------------------ ---------------- +epoch -231 +replay_buffer/size 999047 +trainer/num train calls 770000 +trainer/QF1 Loss 1.0219 +trainer/QF2 Loss 0.811581 +trainer/Policy Loss 16.9906 +trainer/Q1 Predictions Mean -71.2891 +trainer/Q1 Predictions Std 18.6348 +trainer/Q1 Predictions Max -0.597467 +trainer/Q1 Predictions Min -86.5094 +trainer/Q2 Predictions Mean -71.2316 +trainer/Q2 Predictions Std 18.6061 +trainer/Q2 Predictions Max -0.85948 +trainer/Q2 Predictions Min -86.5787 +trainer/Q Targets Mean -71.1748 +trainer/Q Targets Std 18.7418 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8715 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000490653 +trainer/policy/mean Std 0.72285 +trainer/policy/mean Max 0.999392 +trainer/policy/mean Min -0.998997 +trainer/policy/std Mean 0.412017 +trainer/policy/std Std 0.0189342 +trainer/policy/std Max 0.432237 +trainer/policy/std Min 0.386449 +trainer/Advantage Weights Mean 2.17162 +trainer/Advantage Weights Std 13.8393 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.5532e-24 +trainer/Advantage Score Mean -0.628122 +trainer/Advantage Score Std 0.705937 +trainer/Advantage Score Max 0.871599 +trainer/Advantage Score Min -5.35477 +trainer/V1 Predictions Mean -70.8821 +trainer/V1 Predictions Std 18.7826 +trainer/V1 Predictions Max -1.12698 +trainer/V1 Predictions Min -86.7014 +trainer/VF Loss 0.0964919 +expl/num steps total 770000 +expl/num paths total 1047 +expl/path length Mean 500 +expl/path length Std 352 +expl/path length Max 852 +expl/path length Min 148 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.00332143 +expl/Actions Std 0.836378 +expl/Actions Max 2.31989 +expl/Actions Min -2.29785 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 708799 +eval/num paths total 779 +eval/path length Mean 573 +eval/path length Std 0 +eval/path length Max 573 +eval/path length Min 573 +eval/Rewards Mean 0.0017452 +eval/Rewards Std 0.0417391 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0283959 +eval/Actions Std 0.734214 +eval/Actions Max 0.999955 +eval/Actions Min -0.999539 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.72087e-06 +time/evaluation sampling (s) 4.67184 +time/exploration sampling (s) 6.15725 +time/logging (s) 0.0105483 +time/saving (s) 0.0192052 +time/training (s) 19.5797 +time/epoch (s) 30.4385 +time/total (s) 19657.5 +Epoch -231 +------------------------------ ---------------- +2022-05-15 23:30:39.331388 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -230 finished +------------------------------ ---------------- +epoch -230 +replay_buffer/size 999047 +trainer/num train calls 771000 +trainer/QF1 Loss 0.55535 +trainer/QF2 Loss 0.512538 +trainer/Policy Loss 10.9071 +trainer/Q1 Predictions Mean -71.4276 +trainer/Q1 Predictions Std 17.4806 +trainer/Q1 Predictions Max -2.10713 +trainer/Q1 Predictions Min -87.0606 +trainer/Q2 Predictions Mean -71.3482 +trainer/Q2 Predictions Std 17.4326 +trainer/Q2 Predictions Max -2.91021 +trainer/Q2 Predictions Min -87.1035 +trainer/Q Targets Mean -71.2673 +trainer/Q Targets Std 17.3774 +trainer/Q Targets Max -1.90299 +trainer/Q Targets Min -86.8223 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0108746 +trainer/policy/mean Std 0.723349 +trainer/policy/mean Max 0.999804 +trainer/policy/mean Min -0.999574 +trainer/policy/std Mean 0.413299 +trainer/policy/std Std 0.0203943 +trainer/policy/std Max 0.43433 +trainer/policy/std Min 0.385374 +trainer/Advantage Weights Mean 2.73368 +trainer/Advantage Weights Std 15.1671 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.57626e-20 +trainer/Advantage Score Mean -0.573304 +trainer/Advantage Score Std 0.621053 +trainer/Advantage Score Max 1.87463 +trainer/Advantage Score Min -4.51054 +trainer/V1 Predictions Mean -70.9488 +trainer/V1 Predictions Std 17.6078 +trainer/V1 Predictions Max -1.84174 +trainer/V1 Predictions Min -86.7085 +trainer/VF Loss 0.0879465 +expl/num steps total 771000 +expl/num paths total 1049 +expl/path length Mean 500 +expl/path length Std 299 +expl/path length Max 799 +expl/path length Min 201 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0132051 +expl/Actions Std 0.820463 +expl/Actions Max 2.40244 +expl/Actions Min -2.33926 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 709516 +eval/num paths total 780 +eval/path length Mean 717 +eval/path length Std 0 +eval/path length Max 717 +eval/path length Min 717 +eval/Rewards Mean 0.0013947 +eval/Rewards Std 0.0373196 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0143332 +eval/Actions Std 0.725544 +eval/Actions Max 0.999612 +eval/Actions Min -0.999862 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.00834e-05 +time/evaluation sampling (s) 5.06553 +time/exploration sampling (s) 6.72653 +time/logging (s) 0.0112845 +time/saving (s) 0.0187393 +time/training (s) 19.933 +time/epoch (s) 31.7551 +time/total (s) 19689.2 +Epoch -230 +------------------------------ ---------------- +2022-05-15 23:31:10.633697 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -229 finished +------------------------------ ---------------- +epoch -229 +replay_buffer/size 999047 +trainer/num train calls 772000 +trainer/QF1 Loss 0.992487 +trainer/QF2 Loss 0.966369 +trainer/Policy Loss 35.2623 +trainer/Q1 Predictions Mean -69.7528 +trainer/Q1 Predictions Std 20.4472 +trainer/Q1 Predictions Max -0.386842 +trainer/Q1 Predictions Min -86.2133 +trainer/Q2 Predictions Mean -69.8457 +trainer/Q2 Predictions Std 20.398 +trainer/Q2 Predictions Max 0.253586 +trainer/Q2 Predictions Min -86.1194 +trainer/Q Targets Mean -70.1766 +trainer/Q Targets Std 20.5055 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7492 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00229248 +trainer/policy/mean Std 0.738153 +trainer/policy/mean Max 0.999042 +trainer/policy/mean Min -0.999185 +trainer/policy/std Mean 0.412904 +trainer/policy/std Std 0.0201804 +trainer/policy/std Max 0.435764 +trainer/policy/std Min 0.384058 +trainer/Advantage Weights Mean 8.40414 +trainer/Advantage Weights Std 22.7104 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.40203e-14 +trainer/Advantage Score Mean -0.205719 +trainer/Advantage Score Std 0.604466 +trainer/Advantage Score Max 2.07084 +trainer/Advantage Score Min -3.18983 +trainer/V1 Predictions Mean -70.0308 +trainer/V1 Predictions Std 20.4089 +trainer/V1 Predictions Max -0.460305 +trainer/V1 Predictions Min -86.6792 +trainer/VF Loss 0.0919512 +expl/num steps total 772000 +expl/num paths total 1050 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.310654 +expl/Actions Std 0.814717 +expl/Actions Max 2.4612 +expl/Actions Min -2.26856 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 710321 +eval/num paths total 781 +eval/path length Mean 805 +eval/path length Std 0 +eval/path length Max 805 +eval/path length Min 805 +eval/Rewards Mean 0.00124224 +eval/Rewards Std 0.0352235 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0322974 +eval/Actions Std 0.729064 +eval/Actions Max 0.999984 +eval/Actions Min -0.999688 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.90178e-06 +time/evaluation sampling (s) 4.97033 +time/exploration sampling (s) 6.94272 +time/logging (s) 0.0114777 +time/saving (s) 0.0177446 +time/training (s) 19.3395 +time/epoch (s) 31.2818 +time/total (s) 19720.5 +Epoch -229 +------------------------------ ---------------- +2022-05-15 23:31:43.245551 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -228 finished +------------------------------ ---------------- +epoch -228 +replay_buffer/size 999047 +trainer/num train calls 773000 +trainer/QF1 Loss 2.7792 +trainer/QF2 Loss 2.62606 +trainer/Policy Loss 24.5268 +trainer/Q1 Predictions Mean -69.5068 +trainer/Q1 Predictions Std 20.5374 +trainer/Q1 Predictions Max -0.115961 +trainer/Q1 Predictions Min -86.865 +trainer/Q2 Predictions Mean -69.3599 +trainer/Q2 Predictions Std 20.4344 +trainer/Q2 Predictions Max -0.379837 +trainer/Q2 Predictions Min -86.8656 +trainer/Q Targets Mean -69.2962 +trainer/Q Targets Std 20.4224 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7259 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00292825 +trainer/policy/mean Std 0.740402 +trainer/policy/mean Max 0.999248 +trainer/policy/mean Min -0.999357 +trainer/policy/std Mean 0.412393 +trainer/policy/std Std 0.020537 +trainer/policy/std Max 0.43563 +trainer/policy/std Min 0.386087 +trainer/Advantage Weights Mean 5.89896 +trainer/Advantage Weights Std 20.7642 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.38823e-26 +trainer/Advantage Score Mean -0.44993 +trainer/Advantage Score Std 0.795342 +trainer/Advantage Score Max 2.36646 +trainer/Advantage Score Min -5.89967 +trainer/V1 Predictions Mean -69.112 +trainer/V1 Predictions Std 20.5083 +trainer/V1 Predictions Max 0.0822999 +trainer/V1 Predictions Min -86.5205 +trainer/VF Loss 0.133024 +expl/num steps total 773000 +expl/num paths total 1052 +expl/path length Mean 500 +expl/path length Std 81 +expl/path length Max 581 +expl/path length Min 419 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0422079 +expl/Actions Std 0.826276 +expl/Actions Max 2.03165 +expl/Actions Min -2.72332 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 711321 +eval/num paths total 782 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0706428 +eval/Actions Std 0.729078 +eval/Actions Max 0.999907 +eval/Actions Min -0.999752 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2557e-05 +time/evaluation sampling (s) 5.21461 +time/exploration sampling (s) 7.24698 +time/logging (s) 0.0129019 +time/saving (s) 0.0196022 +time/training (s) 20.0981 +time/epoch (s) 32.5922 +time/total (s) 19753.1 +Epoch -228 +------------------------------ ---------------- +2022-05-15 23:32:15.406212 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -227 finished +------------------------------ ---------------- +epoch -227 +replay_buffer/size 999047 +trainer/num train calls 774000 +trainer/QF1 Loss 0.570919 +trainer/QF2 Loss 0.611874 +trainer/Policy Loss 29.2999 +trainer/Q1 Predictions Mean -71.9482 +trainer/Q1 Predictions Std 16.783 +trainer/Q1 Predictions Max -4.68414 +trainer/Q1 Predictions Min -86.4908 +trainer/Q2 Predictions Mean -71.965 +trainer/Q2 Predictions Std 16.8309 +trainer/Q2 Predictions Max -4.28237 +trainer/Q2 Predictions Min -86.5226 +trainer/Q Targets Mean -72.0909 +trainer/Q Targets Std 16.6005 +trainer/Q Targets Max -6.27725 +trainer/Q Targets Min -87.1721 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0245183 +trainer/policy/mean Std 0.733911 +trainer/policy/mean Max 0.999813 +trainer/policy/mean Min -0.997966 +trainer/policy/std Mean 0.412264 +trainer/policy/std Std 0.0200111 +trainer/policy/std Max 0.437327 +trainer/policy/std Min 0.386041 +trainer/Advantage Weights Mean 6.4212 +trainer/Advantage Weights Std 20.7575 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.26718e-23 +trainer/Advantage Score Mean -0.289026 +trainer/Advantage Score Std 0.681243 +trainer/Advantage Score Max 2.72402 +trainer/Advantage Score Min -5.21409 +trainer/V1 Predictions Mean -71.8564 +trainer/V1 Predictions Std 16.6627 +trainer/V1 Predictions Max -3.7624 +trainer/V1 Predictions Min -86.9075 +trainer/VF Loss 0.116375 +expl/num steps total 774000 +expl/num paths total 1054 +expl/path length Mean 500 +expl/path length Std 491 +expl/path length Max 991 +expl/path length Min 9 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.00124627 +expl/Actions Std 0.843931 +expl/Actions Max 2.3678 +expl/Actions Min -2.27291 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 712321 +eval/num paths total 783 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0310371 +eval/Actions Std 0.675629 +eval/Actions Max 0.99991 +eval/Actions Min -0.99943 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.05682e-05 +time/evaluation sampling (s) 5.1151 +time/exploration sampling (s) 7.13028 +time/logging (s) 0.00776876 +time/saving (s) 0.0140375 +time/training (s) 19.8669 +time/epoch (s) 32.1341 +time/total (s) 19785.3 +Epoch -227 +------------------------------ ---------------- +2022-05-15 23:32:47.419643 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -226 finished +------------------------------ ---------------- +epoch -226 +replay_buffer/size 999047 +trainer/num train calls 775000 +trainer/QF1 Loss 0.890637 +trainer/QF2 Loss 0.876189 +trainer/Policy Loss 32.9292 +trainer/Q1 Predictions Mean -73.1623 +trainer/Q1 Predictions Std 16.8628 +trainer/Q1 Predictions Max -0.402036 +trainer/Q1 Predictions Min -87.3976 +trainer/Q2 Predictions Mean -73.2651 +trainer/Q2 Predictions Std 16.7745 +trainer/Q2 Predictions Max -0.815416 +trainer/Q2 Predictions Min -87.7361 +trainer/Q Targets Mean -73.2174 +trainer/Q Targets Std 17.0733 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7708 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.014254 +trainer/policy/mean Std 0.726639 +trainer/policy/mean Max 0.999413 +trainer/policy/mean Min -0.99762 +trainer/policy/std Mean 0.412164 +trainer/policy/std Std 0.01989 +trainer/policy/std Max 0.435332 +trainer/policy/std Min 0.381395 +trainer/Advantage Weights Mean 6.44129 +trainer/Advantage Weights Std 21.7639 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.14513e-15 +trainer/Advantage Score Mean -0.357598 +trainer/Advantage Score Std 0.680705 +trainer/Advantage Score Max 1.77202 +trainer/Advantage Score Min -3.37756 +trainer/V1 Predictions Mean -72.964 +trainer/V1 Predictions Std 17.1219 +trainer/V1 Predictions Max -0.613094 +trainer/V1 Predictions Min -87.7567 +trainer/VF Loss 0.0905423 +expl/num steps total 775000 +expl/num paths total 1056 +expl/path length Mean 500 +expl/path length Std 144 +expl/path length Max 644 +expl/path length Min 356 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00416728 +expl/Actions Std 0.824826 +expl/Actions Max 2.3199 +expl/Actions Min -2.16673 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 713309 +eval/num paths total 785 +eval/path length Mean 494 +eval/path length Std 73 +eval/path length Max 567 +eval/path length Min 421 +eval/Rewards Mean 0.00202429 +eval/Rewards Std 0.0449466 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0197997 +eval/Actions Std 0.744712 +eval/Actions Max 0.999867 +eval/Actions Min -0.999711 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 4.03589e-06 +time/evaluation sampling (s) 5.13123 +time/exploration sampling (s) 6.87701 +time/logging (s) 0.0136576 +time/saving (s) 0.0131592 +time/training (s) 19.9694 +time/epoch (s) 32.0044 +time/total (s) 19817.3 +Epoch -226 +------------------------------ ---------------- +2022-05-15 23:33:20.413394 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -225 finished +------------------------------ ---------------- +epoch -225 +replay_buffer/size 999047 +trainer/num train calls 776000 +trainer/QF1 Loss 2.08373 +trainer/QF2 Loss 2.24559 +trainer/Policy Loss 69.0938 +trainer/Q1 Predictions Mean -71.5581 +trainer/Q1 Predictions Std 16.9062 +trainer/Q1 Predictions Max -1.19043 +trainer/Q1 Predictions Min -90.3966 +trainer/Q2 Predictions Mean -71.4351 +trainer/Q2 Predictions Std 16.8594 +trainer/Q2 Predictions Max -1.5449 +trainer/Q2 Predictions Min -90.3249 +trainer/Q Targets Mean -72.3441 +trainer/Q Targets Std 16.7743 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.3362 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00564582 +trainer/policy/mean Std 0.730406 +trainer/policy/mean Max 0.999797 +trainer/policy/mean Min -0.99845 +trainer/policy/std Mean 0.409379 +trainer/policy/std Std 0.0202961 +trainer/policy/std Max 0.43214 +trainer/policy/std Min 0.380146 +trainer/Advantage Weights Mean 19.2517 +trainer/Advantage Weights Std 35.8332 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.41746e-32 +trainer/Advantage Score Mean -0.0527255 +trainer/Advantage Score Std 0.826726 +trainer/Advantage Score Max 2.84564 +trainer/Advantage Score Min -7.16789 +trainer/V1 Predictions Mean -71.9787 +trainer/V1 Predictions Std 16.8422 +trainer/V1 Predictions Max -2.23873 +trainer/V1 Predictions Min -90.291 +trainer/VF Loss 0.231885 +expl/num steps total 776000 +expl/num paths total 1058 +expl/path length Mean 500 +expl/path length Std 333 +expl/path length Max 833 +expl/path length Min 167 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0469699 +expl/Actions Std 0.822358 +expl/Actions Max 2.5833 +expl/Actions Min -2.5005 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 714309 +eval/num paths total 786 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.189018 +eval/Actions Std 0.724734 +eval/Actions Max 0.99937 +eval/Actions Min -0.999662 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.24336e-05 +time/evaluation sampling (s) 5.70035 +time/exploration sampling (s) 7.92206 +time/logging (s) 0.00838037 +time/saving (s) 0.0126893 +time/training (s) 19.3249 +time/epoch (s) 32.9684 +time/total (s) 19850.2 +Epoch -225 +------------------------------ ---------------- +2022-05-15 23:33:53.212381 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -224 finished +------------------------------ ---------------- +epoch -224 +replay_buffer/size 999047 +trainer/num train calls 777000 +trainer/QF1 Loss 0.902106 +trainer/QF2 Loss 0.701871 +trainer/Policy Loss 35.8715 +trainer/Q1 Predictions Mean -72.2624 +trainer/Q1 Predictions Std 15.8814 +trainer/Q1 Predictions Max -11.7621 +trainer/Q1 Predictions Min -86.0283 +trainer/Q2 Predictions Mean -72.3691 +trainer/Q2 Predictions Std 15.8004 +trainer/Q2 Predictions Max -13.337 +trainer/Q2 Predictions Min -86.055 +trainer/Q Targets Mean -72.782 +trainer/Q Targets Std 15.9362 +trainer/Q Targets Max -9.95895 +trainer/Q Targets Min -86.6832 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0124143 +trainer/policy/mean Std 0.725855 +trainer/policy/mean Max 0.99978 +trainer/policy/mean Min -0.99915 +trainer/policy/std Mean 0.411622 +trainer/policy/std Std 0.0200397 +trainer/policy/std Max 0.433881 +trainer/policy/std Min 0.380513 +trainer/Advantage Weights Mean 8.94053 +trainer/Advantage Weights Std 24.7473 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.98067e-13 +trainer/Advantage Score Mean -0.197868 +trainer/Advantage Score Std 0.572845 +trainer/Advantage Score Max 1.40535 +trainer/Advantage Score Min -2.88415 +trainer/V1 Predictions Mean -72.5878 +trainer/V1 Predictions Std 15.9957 +trainer/V1 Predictions Max -10.8231 +trainer/V1 Predictions Min -86.6277 +trainer/VF Loss 0.080289 +expl/num steps total 777000 +expl/num paths total 1060 +expl/path length Mean 500 +expl/path length Std 350 +expl/path length Max 850 +expl/path length Min 150 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0365297 +expl/Actions Std 0.841146 +expl/Actions Max 2.7102 +expl/Actions Min -2.38307 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 715209 +eval/num paths total 788 +eval/path length Mean 450 +eval/path length Std 31 +eval/path length Max 481 +eval/path length Min 419 +eval/Rewards Mean 0.00222222 +eval/Rewards Std 0.047088 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0248384 +eval/Actions Std 0.743452 +eval/Actions Max 0.999863 +eval/Actions Min -0.999774 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 1.29268e-05 +time/evaluation sampling (s) 5.83532 +time/exploration sampling (s) 6.78377 +time/logging (s) 0.0116156 +time/saving (s) 0.0167352 +time/training (s) 20.141 +time/epoch (s) 32.7885 +time/total (s) 19883 +Epoch -224 +------------------------------ ---------------- +2022-05-15 23:34:25.031222 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -223 finished +------------------------------ ---------------- +epoch -223 +replay_buffer/size 999047 +trainer/num train calls 778000 +trainer/QF1 Loss 0.774028 +trainer/QF2 Loss 0.623985 +trainer/Policy Loss 27.026 +trainer/Q1 Predictions Mean -71.5127 +trainer/Q1 Predictions Std 18.7895 +trainer/Q1 Predictions Max 0.820551 +trainer/Q1 Predictions Min -89.6755 +trainer/Q2 Predictions Mean -71.418 +trainer/Q2 Predictions Std 18.8601 +trainer/Q2 Predictions Max -0.38031 +trainer/Q2 Predictions Min -89.301 +trainer/Q Targets Mean -71.5041 +trainer/Q Targets Std 18.842 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.4479 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00878082 +trainer/policy/mean Std 0.731727 +trainer/policy/mean Max 0.998164 +trainer/policy/mean Min -0.999166 +trainer/policy/std Mean 0.410415 +trainer/policy/std Std 0.0205255 +trainer/policy/std Max 0.433456 +trainer/policy/std Min 0.380914 +trainer/Advantage Weights Mean 4.60842 +trainer/Advantage Weights Std 18.815 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.58197e-16 +trainer/Advantage Score Mean -0.435694 +trainer/Advantage Score Std 0.658714 +trainer/Advantage Score Max 1.68281 +trainer/Advantage Score Min -3.55654 +trainer/V1 Predictions Mean -71.1855 +trainer/V1 Predictions Std 19.0693 +trainer/V1 Predictions Max 3.17624 +trainer/V1 Predictions Min -89.1865 +trainer/VF Loss 0.0880916 +expl/num steps total 778000 +expl/num paths total 1062 +expl/path length Mean 500 +expl/path length Std 266 +expl/path length Max 766 +expl/path length Min 234 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0296098 +expl/Actions Std 0.823024 +expl/Actions Max 2.42188 +expl/Actions Min -2.57542 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 716209 +eval/num paths total 789 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0888291 +eval/Actions Std 0.630972 +eval/Actions Max 0.999723 +eval/Actions Min -0.999763 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.16231e-06 +time/evaluation sampling (s) 5.10078 +time/exploration sampling (s) 6.96637 +time/logging (s) 0.0115231 +time/saving (s) 0.0150736 +time/training (s) 19.7052 +time/epoch (s) 31.799 +time/total (s) 19914.8 +Epoch -223 +------------------------------ ---------------- +2022-05-15 23:34:57.174630 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -222 finished +------------------------------ ---------------- +epoch -222 +replay_buffer/size 999047 +trainer/num train calls 779000 +trainer/QF1 Loss 1.37296 +trainer/QF2 Loss 1.35472 +trainer/Policy Loss 32.6086 +trainer/Q1 Predictions Mean -70.5851 +trainer/Q1 Predictions Std 18.8845 +trainer/Q1 Predictions Max -1.34268 +trainer/Q1 Predictions Min -86.6497 +trainer/Q2 Predictions Mean -70.592 +trainer/Q2 Predictions Std 18.9617 +trainer/Q2 Predictions Max -1.53597 +trainer/Q2 Predictions Min -86.7124 +trainer/Q Targets Mean -70.5225 +trainer/Q Targets Std 19.3751 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7845 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00799848 +trainer/policy/mean Std 0.723973 +trainer/policy/mean Max 0.999929 +trainer/policy/mean Min -0.999112 +trainer/policy/std Mean 0.41327 +trainer/policy/std Std 0.0218052 +trainer/policy/std Max 0.436781 +trainer/policy/std Min 0.38212 +trainer/Advantage Weights Mean 6.74741 +trainer/Advantage Weights Std 19.7478 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.32404e-19 +trainer/Advantage Score Mean -0.267037 +trainer/Advantage Score Std 0.702161 +trainer/Advantage Score Max 2.53288 +trainer/Advantage Score Min -4.29058 +trainer/V1 Predictions Mean -70.3589 +trainer/V1 Predictions Std 19.3179 +trainer/V1 Predictions Max -1.23768 +trainer/V1 Predictions Min -86.7453 +trainer/VF Loss 0.105048 +expl/num steps total 779000 +expl/num paths total 1064 +expl/path length Mean 500 +expl/path length Std 114 +expl/path length Max 614 +expl/path length Min 386 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0297211 +expl/Actions Std 0.832075 +expl/Actions Max 2.21565 +expl/Actions Min -2.35166 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 716969 +eval/num paths total 790 +eval/path length Mean 760 +eval/path length Std 0 +eval/path length Max 760 +eval/path length Min 760 +eval/Rewards Mean 0.00131579 +eval/Rewards Std 0.0362499 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0321476 +eval/Actions Std 0.73181 +eval/Actions Max 0.999803 +eval/Actions Min -0.99977 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.2164e-05 +time/evaluation sampling (s) 5.1778 +time/exploration sampling (s) 7.01239 +time/logging (s) 0.0107577 +time/saving (s) 0.0201727 +time/training (s) 19.9021 +time/epoch (s) 32.1232 +time/total (s) 19947 +Epoch -222 +------------------------------ ---------------- +2022-05-15 23:35:30.019033 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -221 finished +------------------------------ ---------------- +epoch -221 +replay_buffer/size 999047 +trainer/num train calls 780000 +trainer/QF1 Loss 4.15127 +trainer/QF2 Loss 3.96106 +trainer/Policy Loss 17.7155 +trainer/Q1 Predictions Mean -74.6028 +trainer/Q1 Predictions Std 14.2005 +trainer/Q1 Predictions Max -0.723013 +trainer/Q1 Predictions Min -88.0861 +trainer/Q2 Predictions Mean -74.5542 +trainer/Q2 Predictions Std 14.3135 +trainer/Q2 Predictions Max -0.680987 +trainer/Q2 Predictions Min -88.1501 +trainer/Q Targets Mean -74.4313 +trainer/Q Targets Std 14.7013 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.0756 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0208078 +trainer/policy/mean Std 0.735278 +trainer/policy/mean Max 0.998294 +trainer/policy/mean Min -0.99935 +trainer/policy/std Mean 0.413282 +trainer/policy/std Std 0.0215413 +trainer/policy/std Max 0.43546 +trainer/policy/std Min 0.384814 +trainer/Advantage Weights Mean 2.33543 +trainer/Advantage Weights Std 11.4657 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.21164e-13 +trainer/Advantage Score Mean -0.398033 +trainer/Advantage Score Std 0.486724 +trainer/Advantage Score Max 1.01138 +trainer/Advantage Score Min -2.97416 +trainer/V1 Predictions Mean -74.3432 +trainer/V1 Predictions Std 14.5335 +trainer/V1 Predictions Max -0.936885 +trainer/V1 Predictions Min -87.8295 +trainer/VF Loss 0.0511665 +expl/num steps total 780000 +expl/num paths total 1066 +expl/path length Mean 500 +expl/path length Std 331 +expl/path length Max 831 +expl/path length Min 169 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0351209 +expl/Actions Std 0.817604 +expl/Actions Max 2.6705 +expl/Actions Min -2.40648 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 717817 +eval/num paths total 791 +eval/path length Mean 848 +eval/path length Std 0 +eval/path length Max 848 +eval/path length Min 848 +eval/Rewards Mean 0.00117925 +eval/Rewards Std 0.0343199 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0347743 +eval/Actions Std 0.722481 +eval/Actions Max 0.999211 +eval/Actions Min -0.999782 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.01738e-05 +time/evaluation sampling (s) 5.46092 +time/exploration sampling (s) 7.22208 +time/logging (s) 0.0113569 +time/saving (s) 0.0176125 +time/training (s) 20.114 +time/epoch (s) 32.826 +time/total (s) 19979.8 +Epoch -221 +------------------------------ ---------------- +2022-05-15 23:36:01.747240 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -220 finished +------------------------------ ---------------- +epoch -220 +replay_buffer/size 999047 +trainer/num train calls 781000 +trainer/QF1 Loss 2.76186 +trainer/QF2 Loss 2.8996 +trainer/Policy Loss 70.3998 +trainer/Q1 Predictions Mean -68.6546 +trainer/Q1 Predictions Std 20.4226 +trainer/Q1 Predictions Max 0.549437 +trainer/Q1 Predictions Min -88.67 +trainer/Q2 Predictions Mean -68.5406 +trainer/Q2 Predictions Std 20.4612 +trainer/Q2 Predictions Max -0.00787872 +trainer/Q2 Predictions Min -88.2903 +trainer/Q Targets Mean -69.0208 +trainer/Q Targets Std 20.5173 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4435 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00997193 +trainer/policy/mean Std 0.74085 +trainer/policy/mean Max 0.999422 +trainer/policy/mean Min -0.999626 +trainer/policy/std Mean 0.412948 +trainer/policy/std Std 0.0207724 +trainer/policy/std Max 0.434234 +trainer/policy/std Min 0.38532 +trainer/Advantage Weights Mean 13.249 +trainer/Advantage Weights Std 29.8013 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.55806e-29 +trainer/Advantage Score Mean -0.23023 +trainer/Advantage Score Std 0.858772 +trainer/Advantage Score Max 4.09415 +trainer/Advantage Score Min -6.55058 +trainer/V1 Predictions Mean -68.7152 +trainer/V1 Predictions Std 20.6531 +trainer/V1 Predictions Max -0.385236 +trainer/V1 Predictions Min -88.1006 +trainer/VF Loss 0.193408 +expl/num steps total 781000 +expl/num paths total 1068 +expl/path length Mean 500 +expl/path length Std 240 +expl/path length Max 740 +expl/path length Min 260 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0481767 +expl/Actions Std 0.837447 +expl/Actions Max 2.31917 +expl/Actions Min -2.26684 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 718817 +eval/num paths total 792 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0784184 +eval/Actions Std 0.700933 +eval/Actions Max 0.999387 +eval/Actions Min -0.999504 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.33477e-05 +time/evaluation sampling (s) 4.87986 +time/exploration sampling (s) 7.10759 +time/logging (s) 0.011425 +time/saving (s) 0.0155692 +time/training (s) 19.6946 +time/epoch (s) 31.709 +time/total (s) 20011.5 +Epoch -220 +------------------------------ ---------------- +2022-05-15 23:36:33.083762 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -219 finished +------------------------------ ---------------- +epoch -219 +replay_buffer/size 999047 +trainer/num train calls 782000 +trainer/QF1 Loss 0.833605 +trainer/QF2 Loss 1.02746 +trainer/Policy Loss 46.0916 +trainer/Q1 Predictions Mean -71.8089 +trainer/Q1 Predictions Std 16.8969 +trainer/Q1 Predictions Max -1.644 +trainer/Q1 Predictions Min -85.8858 +trainer/Q2 Predictions Mean -71.7623 +trainer/Q2 Predictions Std 16.8796 +trainer/Q2 Predictions Max -1.8696 +trainer/Q2 Predictions Min -85.7476 +trainer/Q Targets Mean -72.4568 +trainer/Q Targets Std 16.9462 +trainer/Q Targets Max -2.36417 +trainer/Q Targets Min -86.4917 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0100056 +trainer/policy/mean Std 0.718504 +trainer/policy/mean Max 0.999313 +trainer/policy/mean Min -0.999308 +trainer/policy/std Mean 0.41228 +trainer/policy/std Std 0.0211369 +trainer/policy/std Max 0.434344 +trainer/policy/std Min 0.382922 +trainer/Advantage Weights Mean 11.8566 +trainer/Advantage Weights Std 25.8196 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.1188e-11 +trainer/Advantage Score Mean -0.162648 +trainer/Advantage Score Std 0.616702 +trainer/Advantage Score Max 1.63832 +trainer/Advantage Score Min -2.52162 +trainer/V1 Predictions Mean -72.1596 +trainer/V1 Predictions Std 17.1714 +trainer/V1 Predictions Max -0.717795 +trainer/V1 Predictions Min -86.2974 +trainer/VF Loss 0.089558 +expl/num steps total 782000 +expl/num paths total 1070 +expl/path length Mean 500 +expl/path length Std 223 +expl/path length Max 723 +expl/path length Min 277 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0341508 +expl/Actions Std 0.828375 +expl/Actions Max 2.29501 +expl/Actions Min -2.29498 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 719619 +eval/num paths total 793 +eval/path length Mean 802 +eval/path length Std 0 +eval/path length Max 802 +eval/path length Min 802 +eval/Rewards Mean 0.00124688 +eval/Rewards Std 0.0352892 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0138051 +eval/Actions Std 0.739767 +eval/Actions Max 0.999768 +eval/Actions Min -0.999744 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.10469e-05 +time/evaluation sampling (s) 5.06695 +time/exploration sampling (s) 7.13598 +time/logging (s) 0.0105219 +time/saving (s) 0.0152166 +time/training (s) 19.0872 +time/epoch (s) 31.3159 +time/total (s) 20042.8 +Epoch -219 +------------------------------ ---------------- +2022-05-15 23:37:05.078547 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -218 finished +------------------------------ ---------------- +epoch -218 +replay_buffer/size 999047 +trainer/num train calls 783000 +trainer/QF1 Loss 0.51307 +trainer/QF2 Loss 0.630259 +trainer/Policy Loss 32.2156 +trainer/Q1 Predictions Mean -71.5289 +trainer/Q1 Predictions Std 18.2542 +trainer/Q1 Predictions Max -1.28452 +trainer/Q1 Predictions Min -86.6063 +trainer/Q2 Predictions Mean -71.3843 +trainer/Q2 Predictions Std 18.1978 +trainer/Q2 Predictions Max -1.43652 +trainer/Q2 Predictions Min -86.3784 +trainer/Q Targets Mean -71.6787 +trainer/Q Targets Std 18.3022 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8363 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.02916 +trainer/policy/mean Std 0.720198 +trainer/policy/mean Max 0.999301 +trainer/policy/mean Min -0.999683 +trainer/policy/std Mean 0.414198 +trainer/policy/std Std 0.0205815 +trainer/policy/std Max 0.435443 +trainer/policy/std Min 0.383416 +trainer/Advantage Weights Mean 6.73276 +trainer/Advantage Weights Std 22.6486 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.14231e-18 +trainer/Advantage Score Mean -0.32909 +trainer/Advantage Score Std 0.682243 +trainer/Advantage Score Max 1.37515 +trainer/Advantage Score Min -4.13135 +trainer/V1 Predictions Mean -71.3621 +trainer/V1 Predictions Std 18.5134 +trainer/V1 Predictions Max -0.0698733 +trainer/V1 Predictions Min -86.4672 +trainer/VF Loss 0.0938752 +expl/num steps total 783000 +expl/num paths total 1071 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0551933 +expl/Actions Std 0.834324 +expl/Actions Max 2.34699 +expl/Actions Min -2.25143 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 720619 +eval/num paths total 794 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0961741 +eval/Actions Std 0.815531 +eval/Actions Max 0.999805 +eval/Actions Min -0.999417 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.91509e-06 +time/evaluation sampling (s) 5.09557 +time/exploration sampling (s) 7.47218 +time/logging (s) 0.0123072 +time/saving (s) 0.0175573 +time/training (s) 19.3797 +time/epoch (s) 31.9773 +time/total (s) 20074.8 +Epoch -218 +------------------------------ ---------------- +2022-05-15 23:37:38.700704 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -217 finished +------------------------------ ---------------- +epoch -217 +replay_buffer/size 999047 +trainer/num train calls 784000 +trainer/QF1 Loss 1.68101 +trainer/QF2 Loss 1.29853 +trainer/Policy Loss 31.4519 +trainer/Q1 Predictions Mean -70.1624 +trainer/Q1 Predictions Std 18.4196 +trainer/Q1 Predictions Max -0.825672 +trainer/Q1 Predictions Min -86.1675 +trainer/Q2 Predictions Mean -70.1359 +trainer/Q2 Predictions Std 18.4006 +trainer/Q2 Predictions Max -0.170003 +trainer/Q2 Predictions Min -86.2572 +trainer/Q Targets Mean -70.641 +trainer/Q Targets Std 18.3104 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.358 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00950923 +trainer/policy/mean Std 0.733197 +trainer/policy/mean Max 0.999676 +trainer/policy/mean Min -0.999674 +trainer/policy/std Mean 0.411653 +trainer/policy/std Std 0.0201362 +trainer/policy/std Max 0.431186 +trainer/policy/std Min 0.38372 +trainer/Advantage Weights Mean 8.38814 +trainer/Advantage Weights Std 23.2924 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.2141e-12 +trainer/Advantage Score Mean -0.250814 +trainer/Advantage Score Std 0.56897 +trainer/Advantage Score Max 1.90678 +trainer/Advantage Score Min -2.5655 +trainer/V1 Predictions Mean -70.4487 +trainer/V1 Predictions Std 18.4128 +trainer/V1 Predictions Max 0.843773 +trainer/V1 Predictions Min -86.536 +trainer/VF Loss 0.0830006 +expl/num steps total 784000 +expl/num paths total 1072 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0167367 +expl/Actions Std 0.845725 +expl/Actions Max 2.25537 +expl/Actions Min -2.39104 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 721619 +eval/num paths total 795 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0953059 +eval/Actions Std 0.731333 +eval/Actions Max 0.999631 +eval/Actions Min -0.999776 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.26064e-05 +time/evaluation sampling (s) 6.16583 +time/exploration sampling (s) 7.8323 +time/logging (s) 0.0106952 +time/saving (s) 0.0159642 +time/training (s) 19.575 +time/epoch (s) 33.5998 +time/total (s) 20108.4 +Epoch -217 +------------------------------ ---------------- +2022-05-15 23:38:11.461432 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -216 finished +------------------------------ ---------------- +epoch -216 +replay_buffer/size 999047 +trainer/num train calls 785000 +trainer/QF1 Loss 1.5313 +trainer/QF2 Loss 1.80583 +trainer/Policy Loss 53.081 +trainer/Q1 Predictions Mean -69.7378 +trainer/Q1 Predictions Std 19.1653 +trainer/Q1 Predictions Max 0.885049 +trainer/Q1 Predictions Min -85.8847 +trainer/Q2 Predictions Mean -69.6164 +trainer/Q2 Predictions Std 19.2088 +trainer/Q2 Predictions Max -0.380762 +trainer/Q2 Predictions Min -86.0598 +trainer/Q Targets Mean -70.3322 +trainer/Q Targets Std 19.1465 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9108 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00765974 +trainer/policy/mean Std 0.726525 +trainer/policy/mean Max 0.998454 +trainer/policy/mean Min -0.99824 +trainer/policy/std Mean 0.411404 +trainer/policy/std Std 0.020335 +trainer/policy/std Max 0.43295 +trainer/policy/std Min 0.382807 +trainer/Advantage Weights Mean 15.2376 +trainer/Advantage Weights Std 29.8675 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.59541e-15 +trainer/Advantage Score Mean -0.166896 +trainer/Advantage Score Std 0.697491 +trainer/Advantage Score Max 1.71081 +trainer/Advantage Score Min -3.3585 +trainer/V1 Predictions Mean -70.0851 +trainer/V1 Predictions Std 19.3357 +trainer/V1 Predictions Max 2.53877 +trainer/V1 Predictions Min -86.7562 +trainer/VF Loss 0.120876 +expl/num steps total 785000 +expl/num paths total 1073 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0145365 +expl/Actions Std 0.814818 +expl/Actions Max 2.15058 +expl/Actions Min -2.7903 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 722096 +eval/num paths total 796 +eval/path length Mean 477 +eval/path length Std 0 +eval/path length Max 477 +eval/path length Min 477 +eval/Rewards Mean 0.00209644 +eval/Rewards Std 0.0457388 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0286583 +eval/Actions Std 0.737452 +eval/Actions Max 0.998139 +eval/Actions Min -0.999745 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.6771e-06 +time/evaluation sampling (s) 5.46999 +time/exploration sampling (s) 7.50057 +time/logging (s) 0.00893644 +time/saving (s) 0.0160786 +time/training (s) 19.7484 +time/epoch (s) 32.7439 +time/total (s) 20141.2 +Epoch -216 +------------------------------ ---------------- +2022-05-15 23:38:43.730904 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -215 finished +------------------------------ ---------------- +epoch -215 +replay_buffer/size 999047 +trainer/num train calls 786000 +trainer/QF1 Loss 0.761868 +trainer/QF2 Loss 0.74804 +trainer/Policy Loss 18.4238 +trainer/Q1 Predictions Mean -71.7639 +trainer/Q1 Predictions Std 19.0667 +trainer/Q1 Predictions Max -1.86963 +trainer/Q1 Predictions Min -86.0705 +trainer/Q2 Predictions Mean -71.8823 +trainer/Q2 Predictions Std 19.0441 +trainer/Q2 Predictions Max 0.622637 +trainer/Q2 Predictions Min -86.3322 +trainer/Q Targets Mean -71.8961 +trainer/Q Targets Std 18.9031 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5684 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00668444 +trainer/policy/mean Std 0.734063 +trainer/policy/mean Max 0.999991 +trainer/policy/mean Min -0.999712 +trainer/policy/std Mean 0.412609 +trainer/policy/std Std 0.0210411 +trainer/policy/std Max 0.432964 +trainer/policy/std Min 0.38219 +trainer/Advantage Weights Mean 6.53065 +trainer/Advantage Weights Std 21.8899 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.8713e-17 +trainer/Advantage Score Mean -0.29179 +trainer/Advantage Score Std 0.592605 +trainer/Advantage Score Max 1.51191 +trainer/Advantage Score Min -3.80892 +trainer/V1 Predictions Mean -71.6098 +trainer/V1 Predictions Std 19.1811 +trainer/V1 Predictions Max -0.385416 +trainer/V1 Predictions Min -86.3395 +trainer/VF Loss 0.0774702 +expl/num steps total 786000 +expl/num paths total 1075 +expl/path length Mean 500 +expl/path length Std 173 +expl/path length Max 673 +expl/path length Min 327 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0306243 +expl/Actions Std 0.827799 +expl/Actions Max 2.40696 +expl/Actions Min -2.17516 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 722779 +eval/num paths total 797 +eval/path length Mean 683 +eval/path length Std 0 +eval/path length Max 683 +eval/path length Min 683 +eval/Rewards Mean 0.00146413 +eval/Rewards Std 0.0382359 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0256789 +eval/Actions Std 0.72389 +eval/Actions Max 0.999893 +eval/Actions Min -0.99962 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.01421e-05 +time/evaluation sampling (s) 5.25393 +time/exploration sampling (s) 7.36196 +time/logging (s) 0.0102836 +time/saving (s) 0.0138805 +time/training (s) 19.6113 +time/epoch (s) 32.2513 +time/total (s) 20173.4 +Epoch -215 +------------------------------ ---------------- +2022-05-15 23:39:16.458067 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -214 finished +------------------------------ ---------------- +epoch -214 +replay_buffer/size 999047 +trainer/num train calls 787000 +trainer/QF1 Loss 0.766188 +trainer/QF2 Loss 0.910354 +trainer/Policy Loss 10.0766 +trainer/Q1 Predictions Mean -72.7473 +trainer/Q1 Predictions Std 16.9649 +trainer/Q1 Predictions Max -0.888869 +trainer/Q1 Predictions Min -86.6447 +trainer/Q2 Predictions Mean -72.7291 +trainer/Q2 Predictions Std 16.8764 +trainer/Q2 Predictions Max -1.04317 +trainer/Q2 Predictions Min -86.5864 +trainer/Q Targets Mean -72.4348 +trainer/Q Targets Std 16.9944 +trainer/Q Targets Max -0.356965 +trainer/Q Targets Min -86.3982 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0240003 +trainer/policy/mean Std 0.74611 +trainer/policy/mean Max 0.999782 +trainer/policy/mean Min -0.999895 +trainer/policy/std Mean 0.412542 +trainer/policy/std Std 0.0210132 +trainer/policy/std Max 0.432039 +trainer/policy/std Min 0.38258 +trainer/Advantage Weights Mean 2.86276 +trainer/Advantage Weights Std 14.5604 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.71163e-23 +trainer/Advantage Score Mean -0.547107 +trainer/Advantage Score Std 0.759836 +trainer/Advantage Score Max 2.10223 +trainer/Advantage Score Min -5.19619 +trainer/V1 Predictions Mean -72.1406 +trainer/V1 Predictions Std 17.1952 +trainer/V1 Predictions Max -0.310618 +trainer/V1 Predictions Min -86.306 +trainer/VF Loss 0.119309 +expl/num steps total 787000 +expl/num paths total 1077 +expl/path length Mean 500 +expl/path length Std 344 +expl/path length Max 844 +expl/path length Min 156 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.027668 +expl/Actions Std 0.822425 +expl/Actions Max 2.28761 +expl/Actions Min -2.27341 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 723413 +eval/num paths total 798 +eval/path length Mean 634 +eval/path length Std 0 +eval/path length Max 634 +eval/path length Min 634 +eval/Rewards Mean 0.00157729 +eval/Rewards Std 0.0396837 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0310478 +eval/Actions Std 0.735154 +eval/Actions Max 0.999729 +eval/Actions Min -0.999588 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.22334e-05 +time/evaluation sampling (s) 5.01688 +time/exploration sampling (s) 7.84219 +time/logging (s) 0.0101315 +time/saving (s) 0.015804 +time/training (s) 19.8226 +time/epoch (s) 32.7076 +time/total (s) 20206.2 +Epoch -214 +------------------------------ ---------------- +2022-05-15 23:39:49.475800 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -213 finished +------------------------------ ---------------- +epoch -213 +replay_buffer/size 999047 +trainer/num train calls 788000 +trainer/QF1 Loss 0.843181 +trainer/QF2 Loss 0.673136 +trainer/Policy Loss 16.4989 +trainer/Q1 Predictions Mean -72.0379 +trainer/Q1 Predictions Std 18.0355 +trainer/Q1 Predictions Max -0.450168 +trainer/Q1 Predictions Min -86.3162 +trainer/Q2 Predictions Mean -71.9762 +trainer/Q2 Predictions Std 17.9914 +trainer/Q2 Predictions Max 0.299569 +trainer/Q2 Predictions Min -86.3003 +trainer/Q Targets Mean -71.7866 +trainer/Q Targets Std 17.9075 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.6988 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0280633 +trainer/policy/mean Std 0.732885 +trainer/policy/mean Max 0.99925 +trainer/policy/mean Min -0.999325 +trainer/policy/std Mean 0.412 +trainer/policy/std Std 0.0214026 +trainer/policy/std Max 0.434641 +trainer/policy/std Min 0.38055 +trainer/Advantage Weights Mean 3.75976 +trainer/Advantage Weights Std 17.5693 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.15673e-19 +trainer/Advantage Score Mean -0.467694 +trainer/Advantage Score Std 0.6272 +trainer/Advantage Score Max 0.956835 +trainer/Advantage Score Min -4.36035 +trainer/V1 Predictions Mean -71.5264 +trainer/V1 Predictions Std 18.0704 +trainer/V1 Predictions Max -0.393312 +trainer/V1 Predictions Min -85.6043 +trainer/VF Loss 0.0742968 +expl/num steps total 788000 +expl/num paths total 1078 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.013685 +expl/Actions Std 0.825884 +expl/Actions Max 2.3018 +expl/Actions Min -2.2472 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 724413 +eval/num paths total 799 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00989822 +eval/Actions Std 0.727472 +eval/Actions Max 0.99959 +eval/Actions Min -0.999492 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.33134e-06 +time/evaluation sampling (s) 5.52312 +time/exploration sampling (s) 7.21519 +time/logging (s) 0.0123028 +time/saving (s) 0.0144565 +time/training (s) 20.2364 +time/epoch (s) 33.0015 +time/total (s) 20239.2 +Epoch -213 +------------------------------ ---------------- +2022-05-15 23:40:20.848067 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -212 finished +------------------------------ ---------------- +epoch -212 +replay_buffer/size 999047 +trainer/num train calls 789000 +trainer/QF1 Loss 1.34059 +trainer/QF2 Loss 1.40472 +trainer/Policy Loss 29.464 +trainer/Q1 Predictions Mean -71.3715 +trainer/Q1 Predictions Std 18.655 +trainer/Q1 Predictions Max -0.0960382 +trainer/Q1 Predictions Min -88.9521 +trainer/Q2 Predictions Mean -71.4057 +trainer/Q2 Predictions Std 18.6273 +trainer/Q2 Predictions Max 0.213021 +trainer/Q2 Predictions Min -88.6057 +trainer/Q Targets Mean -71.5988 +trainer/Q Targets Std 18.4759 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.185 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0106687 +trainer/policy/mean Std 0.734692 +trainer/policy/mean Max 0.998714 +trainer/policy/mean Min -0.999359 +trainer/policy/std Mean 0.411749 +trainer/policy/std Std 0.021176 +trainer/policy/std Max 0.435316 +trainer/policy/std Min 0.380934 +trainer/Advantage Weights Mean 6.31363 +trainer/Advantage Weights Std 22.0662 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.14038e-11 +trainer/Advantage Score Mean -0.317876 +trainer/Advantage Score Std 0.586452 +trainer/Advantage Score Max 2.46301 +trainer/Advantage Score Min -2.51971 +trainer/V1 Predictions Mean -71.3796 +trainer/V1 Predictions Std 18.4688 +trainer/V1 Predictions Max -0.0285958 +trainer/V1 Predictions Min -88.9727 +trainer/VF Loss 0.108217 +expl/num steps total 789000 +expl/num paths total 1079 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0270847 +expl/Actions Std 0.825641 +expl/Actions Max 2.31482 +expl/Actions Min -2.52987 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 724944 +eval/num paths total 800 +eval/path length Mean 531 +eval/path length Std 0 +eval/path length Max 531 +eval/path length Min 531 +eval/Rewards Mean 0.00188324 +eval/Rewards Std 0.0433554 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0159233 +eval/Actions Std 0.721007 +eval/Actions Max 0.999618 +eval/Actions Min -0.999102 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.22902e-05 +time/evaluation sampling (s) 4.80225 +time/exploration sampling (s) 6.90646 +time/logging (s) 0.00655009 +time/saving (s) 0.0179307 +time/training (s) 19.6165 +time/epoch (s) 31.3497 +time/total (s) 20270.5 +Epoch -212 +------------------------------ ---------------- +2022-05-15 23:40:54.024407 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -211 finished +------------------------------ ---------------- +epoch -211 +replay_buffer/size 999047 +trainer/num train calls 790000 +trainer/QF1 Loss 0.773596 +trainer/QF2 Loss 0.808706 +trainer/Policy Loss 26.4441 +trainer/Q1 Predictions Mean -69.7235 +trainer/Q1 Predictions Std 19.9472 +trainer/Q1 Predictions Max -0.0870738 +trainer/Q1 Predictions Min -86.4912 +trainer/Q2 Predictions Mean -69.6862 +trainer/Q2 Predictions Std 19.8013 +trainer/Q2 Predictions Max -0.0738714 +trainer/Q2 Predictions Min -86.499 +trainer/Q Targets Mean -69.5565 +trainer/Q Targets Std 20.0707 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5434 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0179868 +trainer/policy/mean Std 0.732821 +trainer/policy/mean Max 0.999717 +trainer/policy/mean Min -0.998579 +trainer/policy/std Mean 0.413202 +trainer/policy/std Std 0.0221946 +trainer/policy/std Max 0.434276 +trainer/policy/std Min 0.379568 +trainer/Advantage Weights Mean 4.20078 +trainer/Advantage Weights Std 18.3325 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.80477e-21 +trainer/Advantage Score Mean -0.509069 +trainer/Advantage Score Std 0.751437 +trainer/Advantage Score Max 2.49018 +trainer/Advantage Score Min -4.65956 +trainer/V1 Predictions Mean -69.2702 +trainer/V1 Predictions Std 20.1435 +trainer/V1 Predictions Max 0.695247 +trainer/V1 Predictions Min -86.4468 +trainer/VF Loss 0.115116 +expl/num steps total 790000 +expl/num paths total 1081 +expl/path length Mean 500 +expl/path length Std 211 +expl/path length Max 711 +expl/path length Min 289 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0411021 +expl/Actions Std 0.821886 +expl/Actions Max 2.43367 +expl/Actions Min -2.26078 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 725944 +eval/num paths total 801 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0459129 +eval/Actions Std 0.743047 +eval/Actions Max 0.99964 +eval/Actions Min -0.999742 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.3062e-06 +time/evaluation sampling (s) 5.71833 +time/exploration sampling (s) 7.62556 +time/logging (s) 0.00851398 +time/saving (s) 0.013468 +time/training (s) 19.7966 +time/epoch (s) 33.1625 +time/total (s) 20303.7 +Epoch -211 +------------------------------ ---------------- +2022-05-15 23:41:26.824215 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -210 finished +------------------------------ ---------------- +epoch -210 +replay_buffer/size 999047 +trainer/num train calls 791000 +trainer/QF1 Loss 0.785309 +trainer/QF2 Loss 0.820786 +trainer/Policy Loss 13.9938 +trainer/Q1 Predictions Mean -72.3189 +trainer/Q1 Predictions Std 17.2925 +trainer/Q1 Predictions Max -0.447106 +trainer/Q1 Predictions Min -86.2141 +trainer/Q2 Predictions Mean -72.4911 +trainer/Q2 Predictions Std 17.2297 +trainer/Q2 Predictions Max -0.537177 +trainer/Q2 Predictions Min -86.495 +trainer/Q Targets Mean -72.4377 +trainer/Q Targets Std 17.1342 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2749 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0149587 +trainer/policy/mean Std 0.718007 +trainer/policy/mean Max 0.999511 +trainer/policy/mean Min -0.999553 +trainer/policy/std Mean 0.413197 +trainer/policy/std Std 0.0217927 +trainer/policy/std Max 0.435648 +trainer/policy/std Min 0.382004 +trainer/Advantage Weights Mean 3.66237 +trainer/Advantage Weights Std 16.3216 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.94465e-16 +trainer/Advantage Score Mean -0.407343 +trainer/Advantage Score Std 0.569646 +trainer/Advantage Score Max 1.98515 +trainer/Advantage Score Min -3.45443 +trainer/V1 Predictions Mean -72.1478 +trainer/V1 Predictions Std 17.3442 +trainer/V1 Predictions Max -0.0498423 +trainer/V1 Predictions Min -86.2791 +trainer/VF Loss 0.0735302 +expl/num steps total 791000 +expl/num paths total 1082 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0919902 +expl/Actions Std 0.80418 +expl/Actions Max 2.16227 +expl/Actions Min -2.38062 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 726523 +eval/num paths total 802 +eval/path length Mean 579 +eval/path length Std 0 +eval/path length Max 579 +eval/path length Min 579 +eval/Rewards Mean 0.00172712 +eval/Rewards Std 0.0415227 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0128081 +eval/Actions Std 0.751463 +eval/Actions Max 0.999768 +eval/Actions Min -0.999912 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.0693e-05 +time/evaluation sampling (s) 5.81332 +time/exploration sampling (s) 7.18553 +time/logging (s) 0.0104542 +time/saving (s) 0.0149284 +time/training (s) 19.7624 +time/epoch (s) 32.7866 +time/total (s) 20336.5 +Epoch -210 +------------------------------ ---------------- +2022-05-15 23:41:58.855266 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -209 finished +------------------------------ ---------------- +epoch -209 +replay_buffer/size 999047 +trainer/num train calls 792000 +trainer/QF1 Loss 0.681959 +trainer/QF2 Loss 0.602954 +trainer/Policy Loss 8.27505 +trainer/Q1 Predictions Mean -71.7531 +trainer/Q1 Predictions Std 18.05 +trainer/Q1 Predictions Max -0.303105 +trainer/Q1 Predictions Min -86.9331 +trainer/Q2 Predictions Mean -71.7091 +trainer/Q2 Predictions Std 17.9793 +trainer/Q2 Predictions Max -1.39356 +trainer/Q2 Predictions Min -87.0991 +trainer/Q Targets Mean -71.4574 +trainer/Q Targets Std 18.1822 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8235 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0088574 +trainer/policy/mean Std 0.735172 +trainer/policy/mean Max 0.999158 +trainer/policy/mean Min -0.999893 +trainer/policy/std Mean 0.412827 +trainer/policy/std Std 0.0205501 +trainer/policy/std Max 0.435767 +trainer/policy/std Min 0.381953 +trainer/Advantage Weights Mean 2.23658 +trainer/Advantage Weights Std 12.6372 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.21174e-21 +trainer/Advantage Score Mean -0.534666 +trainer/Advantage Score Std 0.764158 +trainer/Advantage Score Max 1.69768 +trainer/Advantage Score Min -4.81622 +trainer/V1 Predictions Mean -71.2076 +trainer/V1 Predictions Std 18.1883 +trainer/V1 Predictions Max 0.0600357 +trainer/V1 Predictions Min -86.3076 +trainer/VF Loss 0.110678 +expl/num steps total 792000 +expl/num paths total 1083 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0314528 +expl/Actions Std 0.883175 +expl/Actions Max 2.34839 +expl/Actions Min -2.56486 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 727263 +eval/num paths total 803 +eval/path length Mean 740 +eval/path length Std 0 +eval/path length Max 740 +eval/path length Min 740 +eval/Rewards Mean 0.00135135 +eval/Rewards Std 0.0367359 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0113124 +eval/Actions Std 0.741346 +eval/Actions Max 0.999651 +eval/Actions Min -0.999885 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.21859e-05 +time/evaluation sampling (s) 4.94686 +time/exploration sampling (s) 7.44114 +time/logging (s) 0.00664647 +time/saving (s) 0.0152662 +time/training (s) 19.6007 +time/epoch (s) 32.0106 +time/total (s) 20368.5 +Epoch -209 +------------------------------ ---------------- +2022-05-15 23:42:31.850397 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -208 finished +------------------------------ ---------------- +epoch -208 +replay_buffer/size 999047 +trainer/num train calls 793000 +trainer/QF1 Loss 5.30176 +trainer/QF2 Loss 5.18787 +trainer/Policy Loss 45.3823 +trainer/Q1 Predictions Mean -71.7411 +trainer/Q1 Predictions Std 18.5623 +trainer/Q1 Predictions Max -1.13197 +trainer/Q1 Predictions Min -90.533 +trainer/Q2 Predictions Mean -71.9007 +trainer/Q2 Predictions Std 18.5751 +trainer/Q2 Predictions Max -1.07126 +trainer/Q2 Predictions Min -90.6049 +trainer/Q Targets Mean -72.1917 +trainer/Q Targets Std 18.6398 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.8654 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000936009 +trainer/policy/mean Std 0.734487 +trainer/policy/mean Max 0.999348 +trainer/policy/mean Min -0.999073 +trainer/policy/std Mean 0.411063 +trainer/policy/std Std 0.0207403 +trainer/policy/std Max 0.434509 +trainer/policy/std Min 0.380687 +trainer/Advantage Weights Mean 10.6367 +trainer/Advantage Weights Std 25.5761 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.4854e-15 +trainer/Advantage Score Mean -0.140498 +trainer/Advantage Score Std 0.568233 +trainer/Advantage Score Max 1.89285 +trainer/Advantage Score Min -3.30379 +trainer/V1 Predictions Mean -72.0825 +trainer/V1 Predictions Std 18.5386 +trainer/V1 Predictions Max -0.624958 +trainer/V1 Predictions Min -90.7348 +trainer/VF Loss 0.0826654 +expl/num steps total 793000 +expl/num paths total 1084 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0262508 +expl/Actions Std 0.844092 +expl/Actions Max 2.19268 +expl/Actions Min -2.28648 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 727847 +eval/num paths total 804 +eval/path length Mean 584 +eval/path length Std 0 +eval/path length Max 584 +eval/path length Min 584 +eval/Rewards Mean 0.00171233 +eval/Rewards Std 0.0413449 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.027781 +eval/Actions Std 0.745883 +eval/Actions Max 0.999549 +eval/Actions Min -0.999934 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.02082e-05 +time/evaluation sampling (s) 5.1281 +time/exploration sampling (s) 7.5572 +time/logging (s) 0.0101821 +time/saving (s) 0.015525 +time/training (s) 20.2695 +time/epoch (s) 32.9806 +time/total (s) 20401.5 +Epoch -208 +------------------------------ ---------------- +2022-05-15 23:43:04.668631 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -207 finished +------------------------------ ---------------- +epoch -207 +replay_buffer/size 999047 +trainer/num train calls 794000 +trainer/QF1 Loss 0.605944 +trainer/QF2 Loss 0.806639 +trainer/Policy Loss 37.4362 +trainer/Q1 Predictions Mean -70.9383 +trainer/Q1 Predictions Std 19.2154 +trainer/Q1 Predictions Max -0.648719 +trainer/Q1 Predictions Min -88.8072 +trainer/Q2 Predictions Mean -70.8914 +trainer/Q2 Predictions Std 19.3588 +trainer/Q2 Predictions Max 0.441259 +trainer/Q2 Predictions Min -89.0704 +trainer/Q Targets Mean -71.1386 +trainer/Q Targets Std 19.5001 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.6591 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00335877 +trainer/policy/mean Std 0.721215 +trainer/policy/mean Max 0.998288 +trainer/policy/mean Min -0.99902 +trainer/policy/std Mean 0.412753 +trainer/policy/std Std 0.0209799 +trainer/policy/std Max 0.43737 +trainer/policy/std Min 0.379472 +trainer/Advantage Weights Mean 6.62771 +trainer/Advantage Weights Std 21.9617 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.68261e-17 +trainer/Advantage Score Mean -0.354122 +trainer/Advantage Score Std 0.708282 +trainer/Advantage Score Max 2.74374 +trainer/Advantage Score Min -3.81572 +trainer/V1 Predictions Mean -70.9237 +trainer/V1 Predictions Std 19.5653 +trainer/V1 Predictions Max -0.18653 +trainer/V1 Predictions Min -89.0486 +trainer/VF Loss 0.117115 +expl/num steps total 794000 +expl/num paths total 1086 +expl/path length Mean 500 +expl/path length Std 456 +expl/path length Max 956 +expl/path length Min 44 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0390178 +expl/Actions Std 0.829197 +expl/Actions Max 2.36529 +expl/Actions Min -2.4069 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 728512 +eval/num paths total 805 +eval/path length Mean 665 +eval/path length Std 0 +eval/path length Max 665 +eval/path length Min 665 +eval/Rewards Mean 0.00150376 +eval/Rewards Std 0.0387492 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0343204 +eval/Actions Std 0.73284 +eval/Actions Max 0.999941 +eval/Actions Min -0.999727 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.00718e-05 +time/evaluation sampling (s) 5.0285 +time/exploration sampling (s) 7.69118 +time/logging (s) 0.0109123 +time/saving (s) 0.0191791 +time/training (s) 20.0501 +time/epoch (s) 32.7999 +time/total (s) 20434.3 +Epoch -207 +------------------------------ ---------------- +2022-05-15 23:43:37.916683 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -206 finished +------------------------------ ---------------- +epoch -206 +replay_buffer/size 999047 +trainer/num train calls 795000 +trainer/QF1 Loss 1.15877 +trainer/QF2 Loss 0.891691 +trainer/Policy Loss 43.7114 +trainer/Q1 Predictions Mean -72.3028 +trainer/Q1 Predictions Std 16.4372 +trainer/Q1 Predictions Max -0.166809 +trainer/Q1 Predictions Min -85.9951 +trainer/Q2 Predictions Mean -72.3929 +trainer/Q2 Predictions Std 16.3994 +trainer/Q2 Predictions Max -0.382013 +trainer/Q2 Predictions Min -85.9823 +trainer/Q Targets Mean -72.9292 +trainer/Q Targets Std 16.4833 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5024 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0168669 +trainer/policy/mean Std 0.724635 +trainer/policy/mean Max 0.999734 +trainer/policy/mean Min -0.99936 +trainer/policy/std Mean 0.414222 +trainer/policy/std Std 0.0202018 +trainer/policy/std Max 0.434804 +trainer/policy/std Min 0.385245 +trainer/Advantage Weights Mean 9.64646 +trainer/Advantage Weights Std 23.794 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.4561e-14 +trainer/Advantage Score Mean -0.144264 +trainer/Advantage Score Std 0.583255 +trainer/Advantage Score Max 1.83527 +trainer/Advantage Score Min -3.18604 +trainer/V1 Predictions Mean -72.6589 +trainer/V1 Predictions Std 16.608 +trainer/V1 Predictions Max 1.67304 +trainer/V1 Predictions Min -86.3443 +trainer/VF Loss 0.0846466 +expl/num steps total 795000 +expl/num paths total 1088 +expl/path length Mean 500 +expl/path length Std 384 +expl/path length Max 884 +expl/path length Min 116 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0235448 +expl/Actions Std 0.825747 +expl/Actions Max 2.42547 +expl/Actions Min -2.52218 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 729052 +eval/num paths total 806 +eval/path length Mean 540 +eval/path length Std 0 +eval/path length Max 540 +eval/path length Min 540 +eval/Rewards Mean 0.00185185 +eval/Rewards Std 0.0429933 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0237725 +eval/Actions Std 0.74591 +eval/Actions Max 0.999924 +eval/Actions Min -0.999695 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.03968e-05 +time/evaluation sampling (s) 5.86568 +time/exploration sampling (s) 7.32994 +time/logging (s) 0.00634136 +time/saving (s) 0.0166908 +time/training (s) 20.0034 +time/epoch (s) 33.2221 +time/total (s) 20467.5 +Epoch -206 +------------------------------ ---------------- +2022-05-15 23:44:10.835087 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -205 finished +------------------------------ ---------------- +epoch -205 +replay_buffer/size 999047 +trainer/num train calls 796000 +trainer/QF1 Loss 0.579141 +trainer/QF2 Loss 0.702791 +trainer/Policy Loss 23.9111 +trainer/Q1 Predictions Mean -72.205 +trainer/Q1 Predictions Std 18.7496 +trainer/Q1 Predictions Max 1.42216 +trainer/Q1 Predictions Min -88.1109 +trainer/Q2 Predictions Mean -72.1993 +trainer/Q2 Predictions Std 18.6404 +trainer/Q2 Predictions Max -0.382234 +trainer/Q2 Predictions Min -87.8602 +trainer/Q Targets Mean -72.2631 +trainer/Q Targets Std 18.9293 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5258 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0228975 +trainer/policy/mean Std 0.725708 +trainer/policy/mean Max 0.999887 +trainer/policy/mean Min -0.999363 +trainer/policy/std Mean 0.411876 +trainer/policy/std Std 0.0196019 +trainer/policy/std Max 0.432212 +trainer/policy/std Min 0.385334 +trainer/Advantage Weights Mean 4.62125 +trainer/Advantage Weights Std 16.1584 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.93031e-31 +trainer/Advantage Score Mean -0.420345 +trainer/Advantage Score Std 0.801104 +trainer/Advantage Score Max 0.692638 +trainer/Advantage Score Min -6.97847 +trainer/V1 Predictions Mean -71.939 +trainer/V1 Predictions Std 19.1325 +trainer/V1 Predictions Max 2.11148 +trainer/V1 Predictions Min -87.0724 +trainer/VF Loss 0.0946139 +expl/num steps total 796000 +expl/num paths total 1090 +expl/path length Mean 500 +expl/path length Std 250 +expl/path length Max 750 +expl/path length Min 250 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0247561 +expl/Actions Std 0.838369 +expl/Actions Max 2.47398 +expl/Actions Min -2.49167 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 730052 +eval/num paths total 807 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0237771 +eval/Actions Std 0.725592 +eval/Actions Max 0.999879 +eval/Actions Min -0.999874 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.15998e-06 +time/evaluation sampling (s) 5.17046 +time/exploration sampling (s) 7.25974 +time/logging (s) 0.0134227 +time/saving (s) 0.0235362 +time/training (s) 20.4417 +time/epoch (s) 32.9089 +time/total (s) 20500.4 +Epoch -205 +------------------------------ ---------------- +2022-05-15 23:44:42.362101 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -204 finished +------------------------------ ---------------- +epoch -204 +replay_buffer/size 999047 +trainer/num train calls 797000 +trainer/QF1 Loss 1.21798 +trainer/QF2 Loss 1.42516 +trainer/Policy Loss 45.3705 +trainer/Q1 Predictions Mean -69.2057 +trainer/Q1 Predictions Std 19.2042 +trainer/Q1 Predictions Max -0.533754 +trainer/Q1 Predictions Min -89.214 +trainer/Q2 Predictions Mean -69.1213 +trainer/Q2 Predictions Std 19.1851 +trainer/Q2 Predictions Max -0.370101 +trainer/Q2 Predictions Min -88.7181 +trainer/Q Targets Mean -69.6632 +trainer/Q Targets Std 19.0919 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.506 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0187341 +trainer/policy/mean Std 0.737133 +trainer/policy/mean Max 0.999821 +trainer/policy/mean Min -0.998665 +trainer/policy/std Mean 0.412378 +trainer/policy/std Std 0.0209229 +trainer/policy/std Max 0.432623 +trainer/policy/std Min 0.383572 +trainer/Advantage Weights Mean 9.55095 +trainer/Advantage Weights Std 26.1631 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.21675e-33 +trainer/Advantage Score Mean -0.300777 +trainer/Advantage Score Std 0.796737 +trainer/Advantage Score Max 1.75119 +trainer/Advantage Score Min -7.40089 +trainer/V1 Predictions Mean -69.311 +trainer/V1 Predictions Std 19.2793 +trainer/V1 Predictions Max -0.512079 +trainer/V1 Predictions Min -88.78 +trainer/VF Loss 0.124429 +expl/num steps total 797000 +expl/num paths total 1091 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0370296 +expl/Actions Std 0.824062 +expl/Actions Max 2.2856 +expl/Actions Min -2.54182 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 731052 +eval/num paths total 808 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0387547 +eval/Actions Std 0.730917 +eval/Actions Max 0.999442 +eval/Actions Min -0.999321 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.2496e-05 +time/evaluation sampling (s) 5.21166 +time/exploration sampling (s) 6.85224 +time/logging (s) 0.0167792 +time/saving (s) 0.0189338 +time/training (s) 19.4079 +time/epoch (s) 31.5076 +time/total (s) 20532 +Epoch -204 +------------------------------ ---------------- +2022-05-15 23:45:10.270408 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -203 finished +------------------------------ ---------------- +epoch -203 +replay_buffer/size 999047 +trainer/num train calls 798000 +trainer/QF1 Loss 0.948493 +trainer/QF2 Loss 0.960521 +trainer/Policy Loss 6.39166 +trainer/Q1 Predictions Mean -71.7453 +trainer/Q1 Predictions Std 18.169 +trainer/Q1 Predictions Max -1.26391 +trainer/Q1 Predictions Min -89.8226 +trainer/Q2 Predictions Mean -71.7176 +trainer/Q2 Predictions Std 18.1731 +trainer/Q2 Predictions Max -0.22578 +trainer/Q2 Predictions Min -90.2736 +trainer/Q Targets Mean -71.3914 +trainer/Q Targets Std 18.4045 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.6548 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0164219 +trainer/policy/mean Std 0.733811 +trainer/policy/mean Max 0.998663 +trainer/policy/mean Min -0.999668 +trainer/policy/std Mean 0.409589 +trainer/policy/std Std 0.0207539 +trainer/policy/std Max 0.429867 +trainer/policy/std Min 0.379722 +trainer/Advantage Weights Mean 2.15714 +trainer/Advantage Weights Std 11.2659 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.67982e-28 +trainer/Advantage Score Mean -0.583829 +trainer/Advantage Score Std 0.833519 +trainer/Advantage Score Max 0.461412 +trainer/Advantage Score Min -6.34866 +trainer/V1 Predictions Mean -71.0419 +trainer/V1 Predictions Std 18.5614 +trainer/V1 Predictions Max 0.0458025 +trainer/V1 Predictions Min -88.9732 +trainer/VF Loss 0.108348 +expl/num steps total 798000 +expl/num paths total 1093 +expl/path length Mean 500 +expl/path length Std 304 +expl/path length Max 804 +expl/path length Min 196 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0331412 +expl/Actions Std 0.823003 +expl/Actions Max 2.31409 +expl/Actions Min -2.11527 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 731788 +eval/num paths total 809 +eval/path length Mean 736 +eval/path length Std 0 +eval/path length Max 736 +eval/path length Min 736 +eval/Rewards Mean 0.0013587 +eval/Rewards Std 0.0368354 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.00105159 +eval/Actions Std 0.744285 +eval/Actions Max 0.999893 +eval/Actions Min -0.999817 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.1486e-05 +time/evaluation sampling (s) 4.5823 +time/exploration sampling (s) 5.55613 +time/logging (s) 0.0107304 +time/saving (s) 0.0157506 +time/training (s) 17.7158 +time/epoch (s) 27.8807 +time/total (s) 20559.8 +Epoch -203 +------------------------------ ---------------- +2022-05-15 23:45:38.087058 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -202 finished +------------------------------ ---------------- +epoch -202 +replay_buffer/size 999047 +trainer/num train calls 799000 +trainer/QF1 Loss 0.786437 +trainer/QF2 Loss 0.746344 +trainer/Policy Loss 16.4868 +trainer/Q1 Predictions Mean -73.0045 +trainer/Q1 Predictions Std 16.2141 +trainer/Q1 Predictions Max -0.775355 +trainer/Q1 Predictions Min -89.2784 +trainer/Q2 Predictions Mean -72.9959 +trainer/Q2 Predictions Std 16.283 +trainer/Q2 Predictions Max 0.00605413 +trainer/Q2 Predictions Min -90.2242 +trainer/Q Targets Mean -73.1415 +trainer/Q Targets Std 16.2722 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.9749 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0270879 +trainer/policy/mean Std 0.722099 +trainer/policy/mean Max 0.999172 +trainer/policy/mean Min -0.99948 +trainer/policy/std Mean 0.412325 +trainer/policy/std Std 0.020277 +trainer/policy/std Max 0.43218 +trainer/policy/std Min 0.384422 +trainer/Advantage Weights Mean 4.05124 +trainer/Advantage Weights Std 14.8693 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.04375e-19 +trainer/Advantage Score Mean -0.411411 +trainer/Advantage Score Std 0.671649 +trainer/Advantage Score Max 1.0019 +trainer/Advantage Score Min -4.30343 +trainer/V1 Predictions Mean -72.7816 +trainer/V1 Predictions Std 16.5891 +trainer/V1 Predictions Max -1.06696 +trainer/V1 Predictions Min -89.9132 +trainer/VF Loss 0.0768166 +expl/num steps total 799000 +expl/num paths total 1095 +expl/path length Mean 500 +expl/path length Std 269 +expl/path length Max 769 +expl/path length Min 231 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0225176 +expl/Actions Std 0.807582 +expl/Actions Max 2.24382 +expl/Actions Min -2.29923 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 732788 +eval/num paths total 810 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.348411 +eval/Actions Std 0.635621 +eval/Actions Max 0.997901 +eval/Actions Min -0.999329 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.1388e-06 +time/evaluation sampling (s) 4.3431 +time/exploration sampling (s) 6.23753 +time/logging (s) 0.0124948 +time/saving (s) 0.0192251 +time/training (s) 17.1867 +time/epoch (s) 27.799 +time/total (s) 20587.7 +Epoch -202 +------------------------------ ---------------- +2022-05-15 23:46:03.789197 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -201 finished +------------------------------ ---------------- +epoch -201 +replay_buffer/size 999047 +trainer/num train calls 800000 +trainer/QF1 Loss 1.21035 +trainer/QF2 Loss 1.16479 +trainer/Policy Loss 52.9225 +trainer/Q1 Predictions Mean -72.5772 +trainer/Q1 Predictions Std 16.7895 +trainer/Q1 Predictions Max -1.24157 +trainer/Q1 Predictions Min -86.5508 +trainer/Q2 Predictions Mean -72.5786 +trainer/Q2 Predictions Std 16.8033 +trainer/Q2 Predictions Max -1.37951 +trainer/Q2 Predictions Min -86.2488 +trainer/Q Targets Mean -72.9521 +trainer/Q Targets Std 17.0148 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2819 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00598947 +trainer/policy/mean Std 0.732306 +trainer/policy/mean Max 0.998942 +trainer/policy/mean Min -0.999517 +trainer/policy/std Mean 0.412214 +trainer/policy/std Std 0.0201808 +trainer/policy/std Max 0.433315 +trainer/policy/std Min 0.385373 +trainer/Advantage Weights Mean 8.71325 +trainer/Advantage Weights Std 24.4839 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.10334e-17 +trainer/Advantage Score Mean -0.249045 +trainer/Advantage Score Std 0.662396 +trainer/Advantage Score Max 1.20924 +trainer/Advantage Score Min -3.84004 +trainer/V1 Predictions Mean -72.7434 +trainer/V1 Predictions Std 16.9064 +trainer/V1 Predictions Max -0.511932 +trainer/V1 Predictions Min -86.2597 +trainer/VF Loss 0.0851094 +expl/num steps total 800000 +expl/num paths total 1096 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0669147 +expl/Actions Std 0.888471 +expl/Actions Max 2.38996 +expl/Actions Min -2.46872 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 733372 +eval/num paths total 811 +eval/path length Mean 584 +eval/path length Std 0 +eval/path length Max 584 +eval/path length Min 584 +eval/Rewards Mean 0.00171233 +eval/Rewards Std 0.0413449 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0150937 +eval/Actions Std 0.755487 +eval/Actions Max 0.99996 +eval/Actions Min -0.999934 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.61027e-06 +time/evaluation sampling (s) 4.05535 +time/exploration sampling (s) 4.74519 +time/logging (s) 0.00974557 +time/saving (s) 0.0148497 +time/training (s) 16.8529 +time/epoch (s) 25.678 +time/total (s) 20613.3 +Epoch -201 +------------------------------ ---------------- +2022-05-15 23:46:29.692189 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -200 finished +------------------------------ ---------------- +epoch -200 +replay_buffer/size 999047 +trainer/num train calls 801000 +trainer/QF1 Loss 0.876434 +trainer/QF2 Loss 0.881401 +trainer/Policy Loss 20.3509 +trainer/Q1 Predictions Mean -71.7105 +trainer/Q1 Predictions Std 18.2892 +trainer/Q1 Predictions Max -0.587775 +trainer/Q1 Predictions Min -89.531 +trainer/Q2 Predictions Mean -71.6268 +trainer/Q2 Predictions Std 18.2759 +trainer/Q2 Predictions Max -0.337951 +trainer/Q2 Predictions Min -89.4501 +trainer/Q Targets Mean -71.7566 +trainer/Q Targets Std 18.0595 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.7799 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00364285 +trainer/policy/mean Std 0.731517 +trainer/policy/mean Max 0.999769 +trainer/policy/mean Min -0.998491 +trainer/policy/std Mean 0.412527 +trainer/policy/std Std 0.0203249 +trainer/policy/std Max 0.431376 +trainer/policy/std Min 0.38619 +trainer/Advantage Weights Mean 5.47891 +trainer/Advantage Weights Std 19.405 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.26376e-17 +trainer/Advantage Score Mean -0.38524 +trainer/Advantage Score Std 0.568676 +trainer/Advantage Score Max 1.21579 +trainer/Advantage Score Min -3.76938 +trainer/V1 Predictions Mean -71.4266 +trainer/V1 Predictions Std 18.2598 +trainer/V1 Predictions Max -0.706656 +trainer/V1 Predictions Min -88.9179 +trainer/VF Loss 0.071235 +expl/num steps total 801000 +expl/num paths total 1097 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0446079 +expl/Actions Std 0.832066 +expl/Actions Max 2.27906 +expl/Actions Min -2.35545 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 734372 +eval/num paths total 812 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0392162 +eval/Actions Std 0.784212 +eval/Actions Max 0.999133 +eval/Actions Min -0.999829 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.30764e-06 +time/evaluation sampling (s) 3.80541 +time/exploration sampling (s) 5.18832 +time/logging (s) 0.0085864 +time/saving (s) 0.0267469 +time/training (s) 16.8547 +time/epoch (s) 25.8838 +time/total (s) 20639.2 +Epoch -200 +------------------------------ ---------------- +2022-05-15 23:46:54.818673 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -199 finished +------------------------------ ---------------- +epoch -199 +replay_buffer/size 999047 +trainer/num train calls 802000 +trainer/QF1 Loss 0.646736 +trainer/QF2 Loss 0.610581 +trainer/Policy Loss 15.8167 +trainer/Q1 Predictions Mean -72.8125 +trainer/Q1 Predictions Std 16.8626 +trainer/Q1 Predictions Max 0.204554 +trainer/Q1 Predictions Min -91.0641 +trainer/Q2 Predictions Mean -72.7253 +trainer/Q2 Predictions Std 16.8957 +trainer/Q2 Predictions Max -0.918702 +trainer/Q2 Predictions Min -91.3924 +trainer/Q Targets Mean -72.6442 +trainer/Q Targets Std 16.7659 +trainer/Q Targets Max 0 +trainer/Q Targets Min -91.1455 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0203498 +trainer/policy/mean Std 0.731181 +trainer/policy/mean Max 0.999923 +trainer/policy/mean Min -0.999148 +trainer/policy/std Mean 0.412451 +trainer/policy/std Std 0.0213229 +trainer/policy/std Max 0.433188 +trainer/policy/std Min 0.383448 +trainer/Advantage Weights Mean 3.36338 +trainer/Advantage Weights Std 15.7536 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.37777e-19 +trainer/Advantage Score Mean -0.415287 +trainer/Advantage Score Std 0.595535 +trainer/Advantage Score Max 2.43279 +trainer/Advantage Score Min -4.16235 +trainer/V1 Predictions Mean -72.3666 +trainer/V1 Predictions Std 16.8785 +trainer/V1 Predictions Max 2.23904 +trainer/V1 Predictions Min -90.458 +trainer/VF Loss 0.0943155 +expl/num steps total 802000 +expl/num paths total 1099 +expl/path length Mean 500 +expl/path length Std 473 +expl/path length Max 973 +expl/path length Min 27 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0341598 +expl/Actions Std 0.821313 +expl/Actions Max 2.70946 +expl/Actions Min -2.30994 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 735372 +eval/num paths total 813 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.214132 +eval/Actions Std 0.708127 +eval/Actions Max 0.999964 +eval/Actions Min -0.999557 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.86009e-06 +time/evaluation sampling (s) 4.04606 +time/exploration sampling (s) 4.13877 +time/logging (s) 0.0114533 +time/saving (s) 0.0154544 +time/training (s) 16.9027 +time/epoch (s) 25.1144 +time/total (s) 20664.3 +Epoch -199 +------------------------------ ---------------- +2022-05-15 23:47:20.474161 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -198 finished +------------------------------ ---------------- +epoch -198 +replay_buffer/size 999047 +trainer/num train calls 803000 +trainer/QF1 Loss 1.09397 +trainer/QF2 Loss 0.984116 +trainer/Policy Loss 59.4685 +trainer/Q1 Predictions Mean -69.4655 +trainer/Q1 Predictions Std 20.348 +trainer/Q1 Predictions Max -0.811154 +trainer/Q1 Predictions Min -87.0756 +trainer/Q2 Predictions Mean -69.4333 +trainer/Q2 Predictions Std 20.5387 +trainer/Q2 Predictions Max -0.434849 +trainer/Q2 Predictions Min -87.1022 +trainer/Q Targets Mean -69.6915 +trainer/Q Targets Std 20.263 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3083 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0323382 +trainer/policy/mean Std 0.717458 +trainer/policy/mean Max 0.998923 +trainer/policy/mean Min -0.998427 +trainer/policy/std Mean 0.41367 +trainer/policy/std Std 0.0206335 +trainer/policy/std Max 0.434687 +trainer/policy/std Min 0.385627 +trainer/Advantage Weights Mean 13.1973 +trainer/Advantage Weights Std 30.5832 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.51953e-16 +trainer/Advantage Score Mean -0.180594 +trainer/Advantage Score Std 0.609864 +trainer/Advantage Score Max 1.63397 +trainer/Advantage Score Min -3.4588 +trainer/V1 Predictions Mean -69.4178 +trainer/V1 Predictions Std 20.4159 +trainer/V1 Predictions Max 0.655991 +trainer/V1 Predictions Min -86.0607 +trainer/VF Loss 0.114766 +expl/num steps total 803000 +expl/num paths total 1100 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0481719 +expl/Actions Std 0.835218 +expl/Actions Max 2.37642 +expl/Actions Min -2.5926 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 735979 +eval/num paths total 814 +eval/path length Mean 607 +eval/path length Std 0 +eval/path length Max 607 +eval/path length Min 607 +eval/Rewards Mean 0.00164745 +eval/Rewards Std 0.0405553 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0216824 +eval/Actions Std 0.72865 +eval/Actions Max 0.999788 +eval/Actions Min -0.999729 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.2899e-06 +time/evaluation sampling (s) 4.15402 +time/exploration sampling (s) 4.61846 +time/logging (s) 0.00970675 +time/saving (s) 0.0137886 +time/training (s) 16.8387 +time/epoch (s) 25.6347 +time/total (s) 20690 +Epoch -198 +------------------------------ ---------------- +2022-05-15 23:47:46.218039 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -197 finished +------------------------------ ---------------- +epoch -197 +replay_buffer/size 999047 +trainer/num train calls 804000 +trainer/QF1 Loss 0.598013 +trainer/QF2 Loss 0.572986 +trainer/Policy Loss 8.24632 +trainer/Q1 Predictions Mean -72.1389 +trainer/Q1 Predictions Std 17.6019 +trainer/Q1 Predictions Max -0.744762 +trainer/Q1 Predictions Min -86.851 +trainer/Q2 Predictions Mean -72.2044 +trainer/Q2 Predictions Std 17.6311 +trainer/Q2 Predictions Max -0.99431 +trainer/Q2 Predictions Min -87.0119 +trainer/Q Targets Mean -71.9277 +trainer/Q Targets Std 17.6183 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2444 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0180838 +trainer/policy/mean Std 0.722935 +trainer/policy/mean Max 0.998486 +trainer/policy/mean Min -0.998447 +trainer/policy/std Mean 0.416036 +trainer/policy/std Std 0.0204047 +trainer/policy/std Max 0.437551 +trainer/policy/std Min 0.385263 +trainer/Advantage Weights Mean 3.21524 +trainer/Advantage Weights Std 13.5247 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.13536e-18 +trainer/Advantage Score Mean -0.392957 +trainer/Advantage Score Std 0.621076 +trainer/Advantage Score Max 1.63987 +trainer/Advantage Score Min -3.94815 +trainer/V1 Predictions Mean -71.574 +trainer/V1 Predictions Std 17.8923 +trainer/V1 Predictions Max -0.523905 +trainer/V1 Predictions Min -86.3027 +trainer/VF Loss 0.0710966 +expl/num steps total 804000 +expl/num paths total 1101 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.247327 +expl/Actions Std 0.86769 +expl/Actions Max 2.35776 +expl/Actions Min -2.22699 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 736979 +eval/num paths total 815 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0498428 +eval/Actions Std 0.66325 +eval/Actions Max 0.999795 +eval/Actions Min -0.999896 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.59816e-06 +time/evaluation sampling (s) 3.75836 +time/exploration sampling (s) 4.75113 +time/logging (s) 0.00787535 +time/saving (s) 0.0141039 +time/training (s) 17.1919 +time/epoch (s) 25.7233 +time/total (s) 20715.7 +Epoch -197 +------------------------------ ---------------- +2022-05-15 23:48:11.672419 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -196 finished +------------------------------ ---------------- +epoch -196 +replay_buffer/size 999047 +trainer/num train calls 805000 +trainer/QF1 Loss 0.72574 +trainer/QF2 Loss 0.776893 +trainer/Policy Loss 24.9409 +trainer/Q1 Predictions Mean -70.5586 +trainer/Q1 Predictions Std 19.0186 +trainer/Q1 Predictions Max -0.43506 +trainer/Q1 Predictions Min -88.8777 +trainer/Q2 Predictions Mean -70.44 +trainer/Q2 Predictions Std 18.9105 +trainer/Q2 Predictions Max -0.558792 +trainer/Q2 Predictions Min -88.6779 +trainer/Q Targets Mean -70.8799 +trainer/Q Targets Std 19.1219 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.7048 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00874082 +trainer/policy/mean Std 0.722572 +trainer/policy/mean Max 0.998773 +trainer/policy/mean Min -0.998996 +trainer/policy/std Mean 0.413014 +trainer/policy/std Std 0.0208144 +trainer/policy/std Max 0.434175 +trainer/policy/std Min 0.380586 +trainer/Advantage Weights Mean 6.15698 +trainer/Advantage Weights Std 19.4003 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.07491e-20 +trainer/Advantage Score Mean -0.402562 +trainer/Advantage Score Std 0.800188 +trainer/Advantage Score Max 0.829144 +trainer/Advantage Score Min -4.59795 +trainer/V1 Predictions Mean -70.4627 +trainer/V1 Predictions Std 19.3703 +trainer/V1 Predictions Max 0.486175 +trainer/V1 Predictions Min -89.5498 +trainer/VF Loss 0.0985758 +expl/num steps total 805000 +expl/num paths total 1102 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00937941 +expl/Actions Std 0.809892 +expl/Actions Max 2.2531 +expl/Actions Min -2.37636 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 737979 +eval/num paths total 816 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.121508 +eval/Actions Std 0.732459 +eval/Actions Max 0.998803 +eval/Actions Min -0.999846 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.59513e-06 +time/evaluation sampling (s) 4.15028 +time/exploration sampling (s) 4.56387 +time/logging (s) 0.00838914 +time/saving (s) 0.0125037 +time/training (s) 16.7067 +time/epoch (s) 25.4418 +time/total (s) 20741.2 +Epoch -196 +------------------------------ ---------------- +2022-05-15 23:48:37.532564 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -195 finished +------------------------------ ---------------- +epoch -195 +replay_buffer/size 999047 +trainer/num train calls 806000 +trainer/QF1 Loss 0.831307 +trainer/QF2 Loss 0.672745 +trainer/Policy Loss 5.14535 +trainer/Q1 Predictions Mean -72.6286 +trainer/Q1 Predictions Std 17.8064 +trainer/Q1 Predictions Max -0.65234 +trainer/Q1 Predictions Min -86.2752 +trainer/Q2 Predictions Mean -72.6489 +trainer/Q2 Predictions Std 17.9233 +trainer/Q2 Predictions Max -0.0691495 +trainer/Q2 Predictions Min -86.3547 +trainer/Q Targets Mean -72.286 +trainer/Q Targets Std 17.7145 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9334 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0119281 +trainer/policy/mean Std 0.741236 +trainer/policy/mean Max 0.999892 +trainer/policy/mean Min -0.998761 +trainer/policy/std Mean 0.413174 +trainer/policy/std Std 0.020329 +trainer/policy/std Max 0.434514 +trainer/policy/std Min 0.38325 +trainer/Advantage Weights Mean 1.63056 +trainer/Advantage Weights Std 11.5632 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.3906e-28 +trainer/Advantage Score Mean -0.81122 +trainer/Advantage Score Std 0.825474 +trainer/Advantage Score Max 1.06766 +trainer/Advantage Score Min -6.41426 +trainer/V1 Predictions Mean -71.9453 +trainer/V1 Predictions Std 18.0782 +trainer/V1 Predictions Max -0.246879 +trainer/V1 Predictions Min -85.6831 +trainer/VF Loss 0.142759 +expl/num steps total 806000 +expl/num paths total 1104 +expl/path length Mean 500 +expl/path length Std 468 +expl/path length Max 968 +expl/path length Min 32 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0355144 +expl/Actions Std 0.815366 +expl/Actions Max 2.42915 +expl/Actions Min -2.46497 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 738948 +eval/num paths total 817 +eval/path length Mean 969 +eval/path length Std 0 +eval/path length Max 969 +eval/path length Min 969 +eval/Rewards Mean 0.00103199 +eval/Rewards Std 0.032108 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0300658 +eval/Actions Std 0.730387 +eval/Actions Max 0.999937 +eval/Actions Min -0.999876 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.21936e-06 +time/evaluation sampling (s) 4.14592 +time/exploration sampling (s) 4.7379 +time/logging (s) 0.0113774 +time/saving (s) 0.0155512 +time/training (s) 16.9389 +time/epoch (s) 25.8496 +time/total (s) 20767 +Epoch -195 +------------------------------ ---------------- +2022-05-15 23:49:02.532600 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -194 finished +------------------------------ ---------------- +epoch -194 +replay_buffer/size 999047 +trainer/num train calls 807000 +trainer/QF1 Loss 0.957245 +trainer/QF2 Loss 0.737878 +trainer/Policy Loss 12.1671 +trainer/Q1 Predictions Mean -70.9957 +trainer/Q1 Predictions Std 20.0636 +trainer/Q1 Predictions Max -1.22454 +trainer/Q1 Predictions Min -90.3518 +trainer/Q2 Predictions Mean -71.0201 +trainer/Q2 Predictions Std 20.1733 +trainer/Q2 Predictions Max 0.162034 +trainer/Q2 Predictions Min -90.817 +trainer/Q Targets Mean -71.0731 +trainer/Q Targets Std 20.2629 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.9547 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0121264 +trainer/policy/mean Std 0.726697 +trainer/policy/mean Max 0.99852 +trainer/policy/mean Min -0.999968 +trainer/policy/std Mean 0.412285 +trainer/policy/std Std 0.0200924 +trainer/policy/std Max 0.433661 +trainer/policy/std Min 0.381717 +trainer/Advantage Weights Mean 3.63163 +trainer/Advantage Weights Std 16.7149 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.26185e-20 +trainer/Advantage Score Mean -0.428473 +trainer/Advantage Score Std 0.620034 +trainer/Advantage Score Max 1.64222 +trainer/Advantage Score Min -4.52355 +trainer/V1 Predictions Mean -70.8876 +trainer/V1 Predictions Std 20.2172 +trainer/V1 Predictions Max -2.12383 +trainer/V1 Predictions Min -90.0327 +trainer/VF Loss 0.0878651 +expl/num steps total 807000 +expl/num paths total 1106 +expl/path length Mean 500 +expl/path length Std 462 +expl/path length Max 962 +expl/path length Min 38 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.015244 +expl/Actions Std 0.816335 +expl/Actions Max 2.16831 +expl/Actions Min -2.39847 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 739448 +eval/num paths total 818 +eval/path length Mean 500 +eval/path length Std 0 +eval/path length Max 500 +eval/path length Min 500 +eval/Rewards Mean 0.002 +eval/Rewards Std 0.0446766 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.0267443 +eval/Actions Std 0.725192 +eval/Actions Max 0.99973 +eval/Actions Min -0.999555 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.20993e-05 +time/evaluation sampling (s) 3.81915 +time/exploration sampling (s) 4.05414 +time/logging (s) 0.00679555 +time/saving (s) 0.0134022 +time/training (s) 17.0821 +time/epoch (s) 24.9756 +time/total (s) 20792 +Epoch -194 +------------------------------ ---------------- +2022-05-15 23:49:29.133762 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -193 finished +------------------------------ ---------------- +epoch -193 +replay_buffer/size 999047 +trainer/num train calls 808000 +trainer/QF1 Loss 1.00598 +trainer/QF2 Loss 1.03322 +trainer/Policy Loss 17.2281 +trainer/Q1 Predictions Mean -74.4157 +trainer/Q1 Predictions Std 16.0431 +trainer/Q1 Predictions Max -0.82816 +trainer/Q1 Predictions Min -89.4425 +trainer/Q2 Predictions Mean -74.4824 +trainer/Q2 Predictions Std 16.14 +trainer/Q2 Predictions Max -0.776894 +trainer/Q2 Predictions Min -89.5611 +trainer/Q Targets Mean -74.2947 +trainer/Q Targets Std 15.968 +trainer/Q Targets Max -1.07788 +trainer/Q Targets Min -89.2616 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00312693 +trainer/policy/mean Std 0.723651 +trainer/policy/mean Max 0.998437 +trainer/policy/mean Min -0.998687 +trainer/policy/std Mean 0.412704 +trainer/policy/std Std 0.0207938 +trainer/policy/std Max 0.433753 +trainer/policy/std Min 0.382419 +trainer/Advantage Weights Mean 4.22214 +trainer/Advantage Weights Std 16.987 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.06781e-13 +trainer/Advantage Score Mean -0.352958 +trainer/Advantage Score Std 0.559764 +trainer/Advantage Score Max 1.08544 +trainer/Advantage Score Min -2.85305 +trainer/V1 Predictions Mean -73.9696 +trainer/V1 Predictions Std 16.1384 +trainer/V1 Predictions Max -0.196086 +trainer/V1 Predictions Min -89.4364 +trainer/VF Loss 0.0607587 +expl/num steps total 808000 +expl/num paths total 1107 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0521136 +expl/Actions Std 0.81816 +expl/Actions Max 2.36725 +expl/Actions Min -2.18382 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 740395 +eval/num paths total 819 +eval/path length Mean 947 +eval/path length Std 0 +eval/path length Max 947 +eval/path length Min 947 +eval/Rewards Mean 0.00105597 +eval/Rewards Std 0.0324785 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0569944 +eval/Actions Std 0.748293 +eval/Actions Max 0.999973 +eval/Actions Min -0.99983 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.37303e-06 +time/evaluation sampling (s) 4.39377 +time/exploration sampling (s) 5.4378 +time/logging (s) 0.00876591 +time/saving (s) 0.0121101 +time/training (s) 16.7368 +time/epoch (s) 26.5893 +time/total (s) 20818.6 +Epoch -193 +------------------------------ ---------------- +2022-05-15 23:49:54.426660 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -192 finished +------------------------------ ---------------- +epoch -192 +replay_buffer/size 999047 +trainer/num train calls 809000 +trainer/QF1 Loss 0.955863 +trainer/QF2 Loss 0.915451 +trainer/Policy Loss 35.3165 +trainer/Q1 Predictions Mean -71.23 +trainer/Q1 Predictions Std 19.0745 +trainer/Q1 Predictions Max -0.996845 +trainer/Q1 Predictions Min -87.2234 +trainer/Q2 Predictions Mean -71.2327 +trainer/Q2 Predictions Std 19.1258 +trainer/Q2 Predictions Max -0.420184 +trainer/Q2 Predictions Min -87.9591 +trainer/Q Targets Mean -71.6236 +trainer/Q Targets Std 19.07 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.7006 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0179279 +trainer/policy/mean Std 0.736036 +trainer/policy/mean Max 0.999825 +trainer/policy/mean Min -0.999559 +trainer/policy/std Mean 0.412713 +trainer/policy/std Std 0.0200346 +trainer/policy/std Max 0.433696 +trainer/policy/std Min 0.385091 +trainer/Advantage Weights Mean 6.34048 +trainer/Advantage Weights Std 21.1572 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.8771e-35 +trainer/Advantage Score Mean -0.402929 +trainer/Advantage Score Std 0.894526 +trainer/Advantage Score Max 1.219 +trainer/Advantage Score Min -7.90059 +trainer/V1 Predictions Mean -71.3039 +trainer/V1 Predictions Std 19.2285 +trainer/V1 Predictions Max 0.386877 +trainer/V1 Predictions Min -87.983 +trainer/VF Loss 0.122639 +expl/num steps total 809000 +expl/num paths total 1109 +expl/path length Mean 500 +expl/path length Std 59 +expl/path length Max 559 +expl/path length Min 441 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.036545 +expl/Actions Std 0.83123 +expl/Actions Max 2.23898 +expl/Actions Min -2.27273 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 741395 +eval/num paths total 820 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.23965 +eval/Actions Std 0.755184 +eval/Actions Max 0.999771 +eval/Actions Min -0.999694 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.17093e-06 +time/evaluation sampling (s) 4.28848 +time/exploration sampling (s) 3.7797 +time/logging (s) 0.00747496 +time/saving (s) 0.0113573 +time/training (s) 17.1932 +time/epoch (s) 25.2803 +time/total (s) 20843.9 +Epoch -192 +------------------------------ ---------------- +2022-05-15 23:50:20.381454 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -191 finished +------------------------------ ---------------- +epoch -191 +replay_buffer/size 999047 +trainer/num train calls 810000 +trainer/QF1 Loss 1.73151 +trainer/QF2 Loss 1.71273 +trainer/Policy Loss 35.7819 +trainer/Q1 Predictions Mean -71.2822 +trainer/Q1 Predictions Std 18.6297 +trainer/Q1 Predictions Max -3.45506 +trainer/Q1 Predictions Min -86.2786 +trainer/Q2 Predictions Mean -71.1946 +trainer/Q2 Predictions Std 18.6262 +trainer/Q2 Predictions Max -2.80884 +trainer/Q2 Predictions Min -86.1203 +trainer/Q Targets Mean -71.7887 +trainer/Q Targets Std 18.5424 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5941 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00992077 +trainer/policy/mean Std 0.735285 +trainer/policy/mean Max 0.99973 +trainer/policy/mean Min -0.999861 +trainer/policy/std Mean 0.412142 +trainer/policy/std Std 0.0193378 +trainer/policy/std Max 0.431676 +trainer/policy/std Min 0.385098 +trainer/Advantage Weights Mean 7.12407 +trainer/Advantage Weights Std 22.5566 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.09936e-22 +trainer/Advantage Score Mean -0.312703 +trainer/Advantage Score Std 0.714498 +trainer/Advantage Score Max 1.65577 +trainer/Advantage Score Min -4.99152 +trainer/V1 Predictions Mean -71.5238 +trainer/V1 Predictions Std 18.629 +trainer/V1 Predictions Max -2.73804 +trainer/V1 Predictions Min -86.4984 +trainer/VF Loss 0.111019 +expl/num steps total 810000 +expl/num paths total 1110 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0308113 +expl/Actions Std 0.833334 +expl/Actions Max 2.49803 +expl/Actions Min -2.32123 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 742032 +eval/num paths total 821 +eval/path length Mean 637 +eval/path length Std 0 +eval/path length Max 637 +eval/path length Min 637 +eval/Rewards Mean 0.00156986 +eval/Rewards Std 0.0395903 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0241193 +eval/Actions Std 0.72475 +eval/Actions Max 0.999498 +eval/Actions Min -0.99944 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.22013e-05 +time/evaluation sampling (s) 3.60023 +time/exploration sampling (s) 5.06648 +time/logging (s) 0.0096857 +time/saving (s) 0.0150681 +time/training (s) 17.2518 +time/epoch (s) 25.9433 +time/total (s) 20869.8 +Epoch -191 +------------------------------ ---------------- +2022-05-15 23:50:45.717197 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -190 finished +------------------------------ ---------------- +epoch -190 +replay_buffer/size 999047 +trainer/num train calls 811000 +trainer/QF1 Loss 13.1308 +trainer/QF2 Loss 12.7793 +trainer/Policy Loss 7.66831 +trainer/Q1 Predictions Mean -70.7949 +trainer/Q1 Predictions Std 20.3265 +trainer/Q1 Predictions Max -0.61317 +trainer/Q1 Predictions Min -86.9562 +trainer/Q2 Predictions Mean -70.813 +trainer/Q2 Predictions Std 20.2537 +trainer/Q2 Predictions Max -0.349055 +trainer/Q2 Predictions Min -87.1008 +trainer/Q Targets Mean -70.9005 +trainer/Q Targets Std 20.0385 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.372 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0153891 +trainer/policy/mean Std 0.745595 +trainer/policy/mean Max 0.999088 +trainer/policy/mean Min -0.999746 +trainer/policy/std Mean 0.412447 +trainer/policy/std Std 0.0195544 +trainer/policy/std Max 0.43032 +trainer/policy/std Min 0.385694 +trainer/Advantage Weights Mean 3.2883 +trainer/Advantage Weights Std 15.0343 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.43585e-26 +trainer/Advantage Score Mean -0.508347 +trainer/Advantage Score Std 0.773339 +trainer/Advantage Score Max 1.56505 +trainer/Advantage Score Min -5.89769 +trainer/V1 Predictions Mean -70.3718 +trainer/V1 Predictions Std 20.4919 +trainer/V1 Predictions Max -0.540054 +trainer/V1 Predictions Min -86.2354 +trainer/VF Loss 0.107095 +expl/num steps total 811000 +expl/num paths total 1112 +expl/path length Mean 500 +expl/path length Std 234 +expl/path length Max 734 +expl/path length Min 266 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0401007 +expl/Actions Std 0.822119 +expl/Actions Max 2.36373 +expl/Actions Min -2.29191 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 743032 +eval/num paths total 822 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.104786 +eval/Actions Std 0.713714 +eval/Actions Max 0.999844 +eval/Actions Min -0.999867 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.79678e-06 +time/evaluation sampling (s) 4.15395 +time/exploration sampling (s) 4.10981 +time/logging (s) 0.0125464 +time/saving (s) 0.0182651 +time/training (s) 17.025 +time/epoch (s) 25.3196 +time/total (s) 20895.2 +Epoch -190 +------------------------------ ---------------- +2022-05-15 23:51:10.567299 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -189 finished +------------------------------ ---------------- +epoch -189 +replay_buffer/size 999047 +trainer/num train calls 812000 +trainer/QF1 Loss 0.745746 +trainer/QF2 Loss 0.714204 +trainer/Policy Loss 19.1585 +trainer/Q1 Predictions Mean -71.1521 +trainer/Q1 Predictions Std 18.0976 +trainer/Q1 Predictions Max -2.84008 +trainer/Q1 Predictions Min -86.5618 +trainer/Q2 Predictions Mean -71.1654 +trainer/Q2 Predictions Std 18.2002 +trainer/Q2 Predictions Max -2.65107 +trainer/Q2 Predictions Min -86.2107 +trainer/Q Targets Mean -71.4684 +trainer/Q Targets Std 17.9996 +trainer/Q Targets Max -4.44209 +trainer/Q Targets Min -86.6424 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00384338 +trainer/policy/mean Std 0.739663 +trainer/policy/mean Max 0.998957 +trainer/policy/mean Min -0.998274 +trainer/policy/std Mean 0.410976 +trainer/policy/std Std 0.0203107 +trainer/policy/std Max 0.430674 +trainer/policy/std Min 0.384857 +trainer/Advantage Weights Mean 5.56521 +trainer/Advantage Weights Std 19.8497 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.9667e-18 +trainer/Advantage Score Mean -0.35009 +trainer/Advantage Score Std 0.593806 +trainer/Advantage Score Max 1.03815 +trainer/Advantage Score Min -3.93713 +trainer/V1 Predictions Mean -71.2297 +trainer/V1 Predictions Std 18.088 +trainer/V1 Predictions Max -3.06398 +trainer/V1 Predictions Min -86.6221 +trainer/VF Loss 0.0688729 +expl/num steps total 812000 +expl/num paths total 1114 +expl/path length Mean 500 +expl/path length Std 168 +expl/path length Max 668 +expl/path length Min 332 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0071868 +expl/Actions Std 0.820316 +expl/Actions Max 2.23179 +expl/Actions Min -2.38012 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 743556 +eval/num paths total 823 +eval/path length Mean 524 +eval/path length Std 0 +eval/path length Max 524 +eval/path length Min 524 +eval/Rewards Mean 0.0019084 +eval/Rewards Std 0.0436435 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.046492 +eval/Actions Std 0.733778 +eval/Actions Max 0.999559 +eval/Actions Min -0.999693 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.50596e-06 +time/evaluation sampling (s) 3.73437 +time/exploration sampling (s) 4.14492 +time/logging (s) 0.00597244 +time/saving (s) 0.0101754 +time/training (s) 16.927 +time/epoch (s) 24.8224 +time/total (s) 20920 +Epoch -189 +------------------------------ ---------------- +2022-05-15 23:51:35.554039 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -188 finished +------------------------------ ---------------- +epoch -188 +replay_buffer/size 999047 +trainer/num train calls 813000 +trainer/QF1 Loss 0.852854 +trainer/QF2 Loss 0.882987 +trainer/Policy Loss 29.5463 +trainer/Q1 Predictions Mean -73.8023 +trainer/Q1 Predictions Std 15.6001 +trainer/Q1 Predictions Max -1.48742 +trainer/Q1 Predictions Min -87.0445 +trainer/Q2 Predictions Mean -73.7588 +trainer/Q2 Predictions Std 15.6148 +trainer/Q2 Predictions Max -0.588065 +trainer/Q2 Predictions Min -86.8435 +trainer/Q Targets Mean -73.8012 +trainer/Q Targets Std 15.9605 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2318 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00109834 +trainer/policy/mean Std 0.720139 +trainer/policy/mean Max 0.999309 +trainer/policy/mean Min -0.99932 +trainer/policy/std Mean 0.411353 +trainer/policy/std Std 0.0192346 +trainer/policy/std Max 0.432806 +trainer/policy/std Min 0.383194 +trainer/Advantage Weights Mean 5.90899 +trainer/Advantage Weights Std 19.6034 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.08049e-11 +trainer/Advantage Score Mean -0.281672 +trainer/Advantage Score Std 0.503702 +trainer/Advantage Score Max 1.23641 +trainer/Advantage Score Min -2.3239 +trainer/V1 Predictions Mean -73.6489 +trainer/V1 Predictions Std 15.7021 +trainer/V1 Predictions Max 0.148075 +trainer/V1 Predictions Min -86.7614 +trainer/VF Loss 0.0554482 +expl/num steps total 813000 +expl/num paths total 1116 +expl/path length Mean 500 +expl/path length Std 274 +expl/path length Max 774 +expl/path length Min 226 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0068235 +expl/Actions Std 0.820663 +expl/Actions Max 2.20076 +expl/Actions Min -2.38246 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 744319 +eval/num paths total 825 +eval/path length Mean 381.5 +eval/path length Std 2.5 +eval/path length Max 384 +eval/path length Min 379 +eval/Rewards Mean 0.00262123 +eval/Rewards Std 0.0511308 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0178108 +eval/Actions Std 0.756065 +eval/Actions Max 0.999475 +eval/Actions Min -0.999789 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 3.49013e-06 +time/evaluation sampling (s) 3.94648 +time/exploration sampling (s) 3.96323 +time/logging (s) 0.00752057 +time/saving (s) 0.0153591 +time/training (s) 17.0445 +time/epoch (s) 24.9771 +time/total (s) 20945 +Epoch -188 +------------------------------ ---------------- +2022-05-15 23:52:00.077075 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -187 finished +------------------------------ ---------------- +epoch -187 +replay_buffer/size 999047 +trainer/num train calls 814000 +trainer/QF1 Loss 1.34032 +trainer/QF2 Loss 1.36716 +trainer/Policy Loss 13.6729 +trainer/Q1 Predictions Mean -69.2145 +trainer/Q1 Predictions Std 20.723 +trainer/Q1 Predictions Max -0.925813 +trainer/Q1 Predictions Min -86.2498 +trainer/Q2 Predictions Mean -69.181 +trainer/Q2 Predictions Std 20.6806 +trainer/Q2 Predictions Max 0.728174 +trainer/Q2 Predictions Min -86.0729 +trainer/Q Targets Mean -69.5765 +trainer/Q Targets Std 20.2865 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6138 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0180921 +trainer/policy/mean Std 0.72563 +trainer/policy/mean Max 0.999479 +trainer/policy/mean Min -0.999445 +trainer/policy/std Mean 0.410701 +trainer/policy/std Std 0.0192257 +trainer/policy/std Max 0.430039 +trainer/policy/std Min 0.381414 +trainer/Advantage Weights Mean 3.81431 +trainer/Advantage Weights Std 16.5239 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.79375e-16 +trainer/Advantage Score Mean -0.459822 +trainer/Advantage Score Std 0.59509 +trainer/Advantage Score Max 0.959579 +trainer/Advantage Score Min -3.5274 +trainer/V1 Predictions Mean -69.2025 +trainer/V1 Predictions Std 20.6745 +trainer/V1 Predictions Max 0.306844 +trainer/V1 Predictions Min -86.4629 +trainer/VF Loss 0.0707259 +expl/num steps total 814000 +expl/num paths total 1118 +expl/path length Mean 500 +expl/path length Std 149 +expl/path length Max 649 +expl/path length Min 351 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0323426 +expl/Actions Std 0.8414 +expl/Actions Max 2.31671 +expl/Actions Min -2.37224 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 745319 +eval/num paths total 826 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0716236 +eval/Actions Std 0.646183 +eval/Actions Max 0.999694 +eval/Actions Min -0.998861 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.79583e-06 +time/evaluation sampling (s) 4.16469 +time/exploration sampling (s) 3.80636 +time/logging (s) 0.00856061 +time/saving (s) 0.0122827 +time/training (s) 16.5169 +time/epoch (s) 24.5088 +time/total (s) 20969.5 +Epoch -187 +------------------------------ ---------------- +2022-05-15 23:52:24.142062 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -186 finished +------------------------------ ---------------- +epoch -186 +replay_buffer/size 999047 +trainer/num train calls 815000 +trainer/QF1 Loss 3.5488 +trainer/QF2 Loss 3.35467 +trainer/Policy Loss 11.9725 +trainer/Q1 Predictions Mean -69.9857 +trainer/Q1 Predictions Std 18.2299 +trainer/Q1 Predictions Max -4.20485 +trainer/Q1 Predictions Min -85.9755 +trainer/Q2 Predictions Mean -70.0353 +trainer/Q2 Predictions Std 18.1576 +trainer/Q2 Predictions Max -4.45858 +trainer/Q2 Predictions Min -86.618 +trainer/Q Targets Mean -69.9664 +trainer/Q Targets Std 17.96 +trainer/Q Targets Max -4.80089 +trainer/Q Targets Min -86.5312 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00554232 +trainer/policy/mean Std 0.717981 +trainer/policy/mean Max 0.999054 +trainer/policy/mean Min -0.998274 +trainer/policy/std Mean 0.413537 +trainer/policy/std Std 0.0197731 +trainer/policy/std Max 0.433795 +trainer/policy/std Min 0.385128 +trainer/Advantage Weights Mean 2.69664 +trainer/Advantage Weights Std 15.2439 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.71004e-22 +trainer/Advantage Score Mean -0.701507 +trainer/Advantage Score Std 0.695489 +trainer/Advantage Score Max 1.22361 +trainer/Advantage Score Min -5.01204 +trainer/V1 Predictions Mean -69.5245 +trainer/V1 Predictions Std 18.319 +trainer/V1 Predictions Max -5.46489 +trainer/V1 Predictions Min -86.2828 +trainer/VF Loss 0.110186 +expl/num steps total 815000 +expl/num paths total 1119 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0973612 +expl/Actions Std 0.831618 +expl/Actions Max 2.11672 +expl/Actions Min -2.24306 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 746319 +eval/num paths total 827 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0150788 +eval/Actions Std 0.74793 +eval/Actions Max 0.999289 +eval/Actions Min -0.99993 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.20328e-06 +time/evaluation sampling (s) 3.59973 +time/exploration sampling (s) 4.45728 +time/logging (s) 0.0117085 +time/saving (s) 0.0151656 +time/training (s) 15.9724 +time/epoch (s) 24.0562 +time/total (s) 20993.5 +Epoch -186 +------------------------------ ---------------- +2022-05-15 23:52:47.408326 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -185 finished +------------------------------ ---------------- +epoch -185 +replay_buffer/size 999047 +trainer/num train calls 816000 +trainer/QF1 Loss 0.711758 +trainer/QF2 Loss 0.642716 +trainer/Policy Loss 38.9916 +trainer/Q1 Predictions Mean -70.9275 +trainer/Q1 Predictions Std 19.2063 +trainer/Q1 Predictions Max -0.837667 +trainer/Q1 Predictions Min -87.077 +trainer/Q2 Predictions Mean -70.9323 +trainer/Q2 Predictions Std 19.1047 +trainer/Q2 Predictions Max -0.308155 +trainer/Q2 Predictions Min -86.8297 +trainer/Q Targets Mean -71.1887 +trainer/Q Targets Std 19.2016 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.088 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00425662 +trainer/policy/mean Std 0.721956 +trainer/policy/mean Max 0.999908 +trainer/policy/mean Min -0.99974 +trainer/policy/std Mean 0.413096 +trainer/policy/std Std 0.020586 +trainer/policy/std Max 0.435869 +trainer/policy/std Min 0.384052 +trainer/Advantage Weights Mean 4.48183 +trainer/Advantage Weights Std 18.4812 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.1806e-35 +trainer/Advantage Score Mean -0.414515 +trainer/Advantage Score Std 0.74008 +trainer/Advantage Score Max 1.93257 +trainer/Advantage Score Min -7.86191 +trainer/V1 Predictions Mean -70.8971 +trainer/V1 Predictions Std 19.4329 +trainer/V1 Predictions Max -0.418703 +trainer/V1 Predictions Min -86.9531 +trainer/VF Loss 0.0986524 +expl/num steps total 816000 +expl/num paths total 1121 +expl/path length Mean 500 +expl/path length Std 304 +expl/path length Max 804 +expl/path length Min 196 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0339797 +expl/Actions Std 0.841047 +expl/Actions Max 2.42456 +expl/Actions Min -2.25099 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 747053 +eval/num paths total 828 +eval/path length Mean 734 +eval/path length Std 0 +eval/path length Max 734 +eval/path length Min 734 +eval/Rewards Mean 0.0013624 +eval/Rewards Std 0.0368855 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0405862 +eval/Actions Std 0.743238 +eval/Actions Max 0.999806 +eval/Actions Min -0.999827 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.17309e-05 +time/evaluation sampling (s) 3.48506 +time/exploration sampling (s) 3.61592 +time/logging (s) 0.00752818 +time/saving (s) 0.0134526 +time/training (s) 16.1239 +time/epoch (s) 23.2458 +time/total (s) 21016.8 +Epoch -185 +------------------------------ ---------------- +2022-05-15 23:53:10.616761 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -184 finished +------------------------------ ---------------- +epoch -184 +replay_buffer/size 999047 +trainer/num train calls 817000 +trainer/QF1 Loss 0.75468 +trainer/QF2 Loss 0.881218 +trainer/Policy Loss 18.2683 +trainer/Q1 Predictions Mean -72.7469 +trainer/Q1 Predictions Std 17.9462 +trainer/Q1 Predictions Max -1.08821 +trainer/Q1 Predictions Min -90.6272 +trainer/Q2 Predictions Mean -72.6654 +trainer/Q2 Predictions Std 18.0176 +trainer/Q2 Predictions Max 0.0241035 +trainer/Q2 Predictions Min -90.3945 +trainer/Q Targets Mean -72.4153 +trainer/Q Targets Std 17.9097 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.2119 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.041822 +trainer/policy/mean Std 0.718342 +trainer/policy/mean Max 0.998232 +trainer/policy/mean Min -0.9989 +trainer/policy/std Mean 0.413806 +trainer/policy/std Std 0.0183904 +trainer/policy/std Max 0.433862 +trainer/policy/std Min 0.388301 +trainer/Advantage Weights Mean 4.3251 +trainer/Advantage Weights Std 18.1916 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.49898e-21 +trainer/Advantage Score Mean -0.477063 +trainer/Advantage Score Std 0.662321 +trainer/Advantage Score Max 2.17696 +trainer/Advantage Score Min -4.71018 +trainer/V1 Predictions Mean -72.1287 +trainer/V1 Predictions Std 18.018 +trainer/V1 Predictions Max -0.51248 +trainer/V1 Predictions Min -89.1408 +trainer/VF Loss 0.111681 +expl/num steps total 817000 +expl/num paths total 1123 +expl/path length Mean 500 +expl/path length Std 230 +expl/path length Max 730 +expl/path length Min 270 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0577015 +expl/Actions Std 0.834996 +expl/Actions Max 2.42755 +expl/Actions Min -2.12435 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 747743 +eval/num paths total 829 +eval/path length Mean 690 +eval/path length Std 0 +eval/path length Max 690 +eval/path length Min 690 +eval/Rewards Mean 0.00144928 +eval/Rewards Std 0.0380418 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0353038 +eval/Actions Std 0.73895 +eval/Actions Max 0.999817 +eval/Actions Min -0.999867 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.5027e-06 +time/evaluation sampling (s) 2.89477 +time/exploration sampling (s) 4.10067 +time/logging (s) 0.0066169 +time/saving (s) 0.0106901 +time/training (s) 16.1806 +time/epoch (s) 23.1933 +time/total (s) 21040 +Epoch -184 +------------------------------ ---------------- +2022-05-15 23:53:33.589351 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -183 finished +------------------------------ ---------------- +epoch -183 +replay_buffer/size 999047 +trainer/num train calls 818000 +trainer/QF1 Loss 0.729245 +trainer/QF2 Loss 0.882231 +trainer/Policy Loss 30.4222 +trainer/Q1 Predictions Mean -73.1967 +trainer/Q1 Predictions Std 17.4098 +trainer/Q1 Predictions Max -5.16578 +trainer/Q1 Predictions Min -86.8421 +trainer/Q2 Predictions Mean -73.241 +trainer/Q2 Predictions Std 17.4856 +trainer/Q2 Predictions Max -4.61606 +trainer/Q2 Predictions Min -87.2228 +trainer/Q Targets Mean -73.264 +trainer/Q Targets Std 17.4376 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6779 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00649946 +trainer/policy/mean Std 0.724538 +trainer/policy/mean Max 0.999883 +trainer/policy/mean Min -0.999662 +trainer/policy/std Mean 0.411458 +trainer/policy/std Std 0.0198034 +trainer/policy/std Max 0.433997 +trainer/policy/std Min 0.382042 +trainer/Advantage Weights Mean 6.44608 +trainer/Advantage Weights Std 22.1674 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.79655e-30 +trainer/Advantage Score Mean -0.344971 +trainer/Advantage Score Std 0.761764 +trainer/Advantage Score Max 1.59828 +trainer/Advantage Score Min -6.73203 +trainer/V1 Predictions Mean -72.9466 +trainer/V1 Predictions Std 17.6267 +trainer/V1 Predictions Max -2.747 +trainer/V1 Predictions Min -86.6824 +trainer/VF Loss 0.097329 +expl/num steps total 818000 +expl/num paths total 1124 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0952127 +expl/Actions Std 0.831951 +expl/Actions Max 2.413 +expl/Actions Min -2.33022 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 748743 +eval/num paths total 830 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.296611 +eval/Actions Std 0.720657 +eval/Actions Max 0.999665 +eval/Actions Min -0.999937 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.58768e-06 +time/evaluation sampling (s) 3.58018 +time/exploration sampling (s) 3.68257 +time/logging (s) 0.0147522 +time/saving (s) 0.0217737 +time/training (s) 15.6702 +time/epoch (s) 22.9695 +time/total (s) 21063 +Epoch -183 +------------------------------ ---------------- +2022-05-15 23:53:57.353499 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -182 finished +------------------------------ ---------------- +epoch -182 +replay_buffer/size 999047 +trainer/num train calls 819000 +trainer/QF1 Loss 0.55669 +trainer/QF2 Loss 0.581744 +trainer/Policy Loss 29.1564 +trainer/Q1 Predictions Mean -72.7952 +trainer/Q1 Predictions Std 16.9657 +trainer/Q1 Predictions Max -0.882179 +trainer/Q1 Predictions Min -85.6793 +trainer/Q2 Predictions Mean -72.7845 +trainer/Q2 Predictions Std 16.9641 +trainer/Q2 Predictions Max -0.785246 +trainer/Q2 Predictions Min -85.7477 +trainer/Q Targets Mean -73.1181 +trainer/Q Targets Std 17.073 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.2163 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00484845 +trainer/policy/mean Std 0.708911 +trainer/policy/mean Max 0.998779 +trainer/policy/mean Min -0.998879 +trainer/policy/std Mean 0.412086 +trainer/policy/std Std 0.0212688 +trainer/policy/std Max 0.436066 +trainer/policy/std Min 0.381963 +trainer/Advantage Weights Mean 6.12569 +trainer/Advantage Weights Std 21.7962 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.13065e-17 +trainer/Advantage Score Mean -0.339867 +trainer/Advantage Score Std 0.590214 +trainer/Advantage Score Max 2.40021 +trainer/Advantage Score Min -3.83875 +trainer/V1 Predictions Mean -72.8918 +trainer/V1 Predictions Std 17.0041 +trainer/V1 Predictions Max -0.4243 +trainer/V1 Predictions Min -86.0363 +trainer/VF Loss 0.0971696 +expl/num steps total 819000 +expl/num paths total 1126 +expl/path length Mean 500 +expl/path length Std 437 +expl/path length Max 937 +expl/path length Min 63 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00953363 +expl/Actions Std 0.806835 +expl/Actions Max 2.42595 +expl/Actions Min -2.12712 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 749743 +eval/num paths total 831 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.123774 +eval/Actions Std 0.540836 +eval/Actions Max 0.999216 +eval/Actions Min -0.999645 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.40631e-06 +time/evaluation sampling (s) 3.57892 +time/exploration sampling (s) 4.3055 +time/logging (s) 0.00920354 +time/saving (s) 0.0128832 +time/training (s) 15.829 +time/epoch (s) 23.7355 +time/total (s) 21086.7 +Epoch -182 +------------------------------ ---------------- +2022-05-15 23:54:19.742560 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -181 finished +------------------------------ ---------------- +epoch -181 +replay_buffer/size 999047 +trainer/num train calls 820000 +trainer/QF1 Loss 0.840851 +trainer/QF2 Loss 0.882619 +trainer/Policy Loss 14.6456 +trainer/Q1 Predictions Mean -72.7944 +trainer/Q1 Predictions Std 17.3573 +trainer/Q1 Predictions Max -1.25476 +trainer/Q1 Predictions Min -86.8358 +trainer/Q2 Predictions Mean -72.6975 +trainer/Q2 Predictions Std 17.4385 +trainer/Q2 Predictions Max -0.771671 +trainer/Q2 Predictions Min -86.5901 +trainer/Q Targets Mean -72.7187 +trainer/Q Targets Std 17.1641 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4581 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00776016 +trainer/policy/mean Std 0.727193 +trainer/policy/mean Max 0.999479 +trainer/policy/mean Min -0.999162 +trainer/policy/std Mean 0.410751 +trainer/policy/std Std 0.020063 +trainer/policy/std Max 0.430028 +trainer/policy/std Min 0.379272 +trainer/Advantage Weights Mean 3.82123 +trainer/Advantage Weights Std 15.5843 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.59256e-28 +trainer/Advantage Score Mean -0.396207 +trainer/Advantage Score Std 0.712967 +trainer/Advantage Score Max 1.93683 +trainer/Advantage Score Min -6.25864 +trainer/V1 Predictions Mean -72.416 +trainer/V1 Predictions Std 17.3563 +trainer/V1 Predictions Max -1.05734 +trainer/V1 Predictions Min -86.3229 +trainer/VF Loss 0.0869992 +expl/num steps total 820000 +expl/num paths total 1127 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0652126 +expl/Actions Std 0.841888 +expl/Actions Max 2.23694 +expl/Actions Min -2.15036 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 750743 +eval/num paths total 832 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.027692 +eval/Actions Std 0.758216 +eval/Actions Max 0.999887 +eval/Actions Min -0.999905 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.81492e-06 +time/evaluation sampling (s) 3.43493 +time/exploration sampling (s) 3.3311 +time/logging (s) 0.008096 +time/saving (s) 0.0113859 +time/training (s) 15.5902 +time/epoch (s) 22.3757 +time/total (s) 21109.1 +Epoch -181 +------------------------------ ---------------- +2022-05-15 23:54:43.262416 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -180 finished +------------------------------ ---------------- +epoch -180 +replay_buffer/size 999047 +trainer/num train calls 821000 +trainer/QF1 Loss 0.874376 +trainer/QF2 Loss 0.796221 +trainer/Policy Loss 6.14174 +trainer/Q1 Predictions Mean -70.4043 +trainer/Q1 Predictions Std 18.3569 +trainer/Q1 Predictions Max -0.890907 +trainer/Q1 Predictions Min -86.8484 +trainer/Q2 Predictions Mean -70.3873 +trainer/Q2 Predictions Std 18.4533 +trainer/Q2 Predictions Max 0.368738 +trainer/Q2 Predictions Min -86.859 +trainer/Q Targets Mean -70.3495 +trainer/Q Targets Std 18.556 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.026 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0156658 +trainer/policy/mean Std 0.738769 +trainer/policy/mean Max 0.999689 +trainer/policy/mean Min -0.999402 +trainer/policy/std Mean 0.411863 +trainer/policy/std Std 0.0200641 +trainer/policy/std Max 0.434308 +trainer/policy/std Min 0.382922 +trainer/Advantage Weights Mean 2.19639 +trainer/Advantage Weights Std 10.1511 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.41162e-26 +trainer/Advantage Score Mean -0.536465 +trainer/Advantage Score Std 0.790453 +trainer/Advantage Score Max 1.16614 +trainer/Advantage Score Min -5.95225 +trainer/V1 Predictions Mean -70.0554 +trainer/V1 Predictions Std 18.736 +trainer/V1 Predictions Max -0.0687711 +trainer/V1 Predictions Min -86.8631 +trainer/VF Loss 0.100663 +expl/num steps total 821000 +expl/num paths total 1129 +expl/path length Mean 500 +expl/path length Std 312 +expl/path length Max 812 +expl/path length Min 188 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0126174 +expl/Actions Std 0.825577 +expl/Actions Max 2.34118 +expl/Actions Min -2.31739 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 751743 +eval/num paths total 833 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0451121 +eval/Actions Std 0.867816 +eval/Actions Max 0.99888 +eval/Actions Min -0.999265 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.4282e-06 +time/evaluation sampling (s) 3.47731 +time/exploration sampling (s) 4.24068 +time/logging (s) 0.00772354 +time/saving (s) 0.0132045 +time/training (s) 15.7692 +time/epoch (s) 23.5082 +time/total (s) 21132.6 +Epoch -180 +------------------------------ ---------------- +2022-05-15 23:55:05.465378 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -179 finished +------------------------------ ---------------- +epoch -179 +replay_buffer/size 999047 +trainer/num train calls 822000 +trainer/QF1 Loss 0.797158 +trainer/QF2 Loss 0.707432 +trainer/Policy Loss 13.958 +trainer/Q1 Predictions Mean -72.5858 +trainer/Q1 Predictions Std 17.1929 +trainer/Q1 Predictions Max -0.107565 +trainer/Q1 Predictions Min -87.1484 +trainer/Q2 Predictions Mean -72.4922 +trainer/Q2 Predictions Std 17.1139 +trainer/Q2 Predictions Max -0.647119 +trainer/Q2 Predictions Min -86.9791 +trainer/Q Targets Mean -72.3405 +trainer/Q Targets Std 17.2968 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.177 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.015451 +trainer/policy/mean Std 0.728441 +trainer/policy/mean Max 0.999 +trainer/policy/mean Min -0.999135 +trainer/policy/std Mean 0.411511 +trainer/policy/std Std 0.0210978 +trainer/policy/std Max 0.433995 +trainer/policy/std Min 0.38329 +trainer/Advantage Weights Mean 3.75998 +trainer/Advantage Weights Std 16.581 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.84967e-19 +trainer/Advantage Score Mean -0.521024 +trainer/Advantage Score Std 0.712338 +trainer/Advantage Score Max 2.96872 +trainer/Advantage Score Min -4.27019 +trainer/V1 Predictions Mean -72.0341 +trainer/V1 Predictions Std 17.3474 +trainer/V1 Predictions Max 1.08561 +trainer/V1 Predictions Min -86.8947 +trainer/VF Loss 0.127172 +expl/num steps total 822000 +expl/num paths total 1130 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00196313 +expl/Actions Std 0.83438 +expl/Actions Max 2.51066 +expl/Actions Min -2.30561 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 752516 +eval/num paths total 834 +eval/path length Mean 773 +eval/path length Std 0 +eval/path length Max 773 +eval/path length Min 773 +eval/Rewards Mean 0.00129366 +eval/Rewards Std 0.0359442 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0135827 +eval/Actions Std 0.730031 +eval/Actions Max 0.999873 +eval/Actions Min -0.999875 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.06219e-06 +time/evaluation sampling (s) 3.23268 +time/exploration sampling (s) 3.65065 +time/logging (s) 0.00982783 +time/saving (s) 0.0142144 +time/training (s) 15.283 +time/epoch (s) 22.1904 +time/total (s) 21154.8 +Epoch -179 +------------------------------ ---------------- +2022-05-15 23:55:28.358943 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -178 finished +------------------------------ ---------------- +epoch -178 +replay_buffer/size 999047 +trainer/num train calls 823000 +trainer/QF1 Loss 0.837041 +trainer/QF2 Loss 0.902977 +trainer/Policy Loss 13.6562 +trainer/Q1 Predictions Mean -71.5117 +trainer/Q1 Predictions Std 20.2348 +trainer/Q1 Predictions Max -1.86684 +trainer/Q1 Predictions Min -88.6244 +trainer/Q2 Predictions Mean -71.3797 +trainer/Q2 Predictions Std 20.3525 +trainer/Q2 Predictions Max -0.720788 +trainer/Q2 Predictions Min -88.4334 +trainer/Q Targets Mean -71.2374 +trainer/Q Targets Std 20.1614 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7345 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0217574 +trainer/policy/mean Std 0.728173 +trainer/policy/mean Max 0.999471 +trainer/policy/mean Min -0.999336 +trainer/policy/std Mean 0.411353 +trainer/policy/std Std 0.0199856 +trainer/policy/std Max 0.433707 +trainer/policy/std Min 0.384568 +trainer/Advantage Weights Mean 4.16273 +trainer/Advantage Weights Std 17.1413 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19639e-17 +trainer/Advantage Score Mean -0.416539 +trainer/Advantage Score Std 0.633728 +trainer/Advantage Score Max 0.714058 +trainer/Advantage Score Min -3.89646 +trainer/V1 Predictions Mean -70.9705 +trainer/V1 Predictions Std 20.3889 +trainer/V1 Predictions Max -0.388424 +trainer/V1 Predictions Min -87.8439 +trainer/VF Loss 0.0697262 +expl/num steps total 823000 +expl/num paths total 1132 +expl/path length Mean 500 +expl/path length Std 298 +expl/path length Max 798 +expl/path length Min 202 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0343395 +expl/Actions Std 0.837772 +expl/Actions Max 2.23894 +expl/Actions Min -2.33985 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 753455 +eval/num paths total 835 +eval/path length Mean 939 +eval/path length Std 0 +eval/path length Max 939 +eval/path length Min 939 +eval/Rewards Mean 0.00106496 +eval/Rewards Std 0.0326164 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0416687 +eval/Actions Std 0.728109 +eval/Actions Max 0.999809 +eval/Actions Min -0.999701 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.80887e-06 +time/evaluation sampling (s) 3.52578 +time/exploration sampling (s) 4.0717 +time/logging (s) 0.00657232 +time/saving (s) 0.0099099 +time/training (s) 15.2574 +time/epoch (s) 22.8713 +time/total (s) 21177.7 +Epoch -178 +------------------------------ ---------------- +2022-05-15 23:55:51.139428 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -177 finished +------------------------------ ---------------- +epoch -177 +replay_buffer/size 999047 +trainer/num train calls 824000 +trainer/QF1 Loss 0.609001 +trainer/QF2 Loss 0.674274 +trainer/Policy Loss 24.2306 +trainer/Q1 Predictions Mean -72.7662 +trainer/Q1 Predictions Std 16.4693 +trainer/Q1 Predictions Max -1.21443 +trainer/Q1 Predictions Min -86.3289 +trainer/Q2 Predictions Mean -72.7636 +trainer/Q2 Predictions Std 16.4224 +trainer/Q2 Predictions Max -0.578182 +trainer/Q2 Predictions Min -86.6804 +trainer/Q Targets Mean -72.8712 +trainer/Q Targets Std 16.4265 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6274 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0329767 +trainer/policy/mean Std 0.740911 +trainer/policy/mean Max 0.999661 +trainer/policy/mean Min -0.999805 +trainer/policy/std Mean 0.408948 +trainer/policy/std Std 0.0182858 +trainer/policy/std Max 0.428123 +trainer/policy/std Min 0.384051 +trainer/Advantage Weights Mean 3.39591 +trainer/Advantage Weights Std 15.4427 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.069e-20 +trainer/Advantage Score Mean -0.468524 +trainer/Advantage Score Std 0.651131 +trainer/Advantage Score Max 0.952171 +trainer/Advantage Score Min -4.44286 +trainer/V1 Predictions Mean -72.5842 +trainer/V1 Predictions Std 16.6716 +trainer/V1 Predictions Max -0.607519 +trainer/V1 Predictions Min -86.4447 +trainer/VF Loss 0.0768524 +expl/num steps total 824000 +expl/num paths total 1133 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0639755 +expl/Actions Std 0.887267 +expl/Actions Max 2.54658 +expl/Actions Min -2.56338 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 753927 +eval/num paths total 836 +eval/path length Mean 472 +eval/path length Std 0 +eval/path length Max 472 +eval/path length Min 472 +eval/Rewards Mean 0.00211864 +eval/Rewards Std 0.0459799 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0292807 +eval/Actions Std 0.745463 +eval/Actions Max 0.999975 +eval/Actions Min -0.999932 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.29781e-06 +time/evaluation sampling (s) 3.42281 +time/exploration sampling (s) 3.71408 +time/logging (s) 0.00663165 +time/saving (s) 0.0139825 +time/training (s) 15.611 +time/epoch (s) 22.7685 +time/total (s) 21200.4 +Epoch -177 +------------------------------ ---------------- +2022-05-15 23:56:14.963523 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -176 finished +------------------------------ ---------------- +epoch -176 +replay_buffer/size 999047 +trainer/num train calls 825000 +trainer/QF1 Loss 1.15504 +trainer/QF2 Loss 1.17334 +trainer/Policy Loss 24.8576 +trainer/Q1 Predictions Mean -70.2736 +trainer/Q1 Predictions Std 21.8541 +trainer/Q1 Predictions Max -1.03353 +trainer/Q1 Predictions Min -87.1822 +trainer/Q2 Predictions Mean -70.2496 +trainer/Q2 Predictions Std 21.8142 +trainer/Q2 Predictions Max -0.582473 +trainer/Q2 Predictions Min -87.2597 +trainer/Q Targets Mean -69.9158 +trainer/Q Targets Std 21.7201 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8937 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0157434 +trainer/policy/mean Std 0.72928 +trainer/policy/mean Max 0.997549 +trainer/policy/mean Min -0.999457 +trainer/policy/std Mean 0.410862 +trainer/policy/std Std 0.0206148 +trainer/policy/std Max 0.432019 +trainer/policy/std Min 0.379078 +trainer/Advantage Weights Mean 4.83588 +trainer/Advantage Weights Std 19.2015 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.40127e-16 +trainer/Advantage Score Mean -0.466018 +trainer/Advantage Score Std 0.655318 +trainer/Advantage Score Max 1.8703 +trainer/Advantage Score Min -3.6504 +trainer/V1 Predictions Mean -69.6394 +trainer/V1 Predictions Std 21.7779 +trainer/V1 Predictions Max -1.93319 +trainer/V1 Predictions Min -86.7022 +trainer/VF Loss 0.0964456 +expl/num steps total 825000 +expl/num paths total 1134 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0245 +expl/Actions Std 0.834836 +expl/Actions Max 2.37218 +expl/Actions Min -2.34527 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 754927 +eval/num paths total 837 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0613425 +eval/Actions Std 0.735286 +eval/Actions Max 0.99991 +eval/Actions Min -0.999542 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93786e-06 +time/evaluation sampling (s) 3.94808 +time/exploration sampling (s) 4.28325 +time/logging (s) 0.00684317 +time/saving (s) 0.013446 +time/training (s) 15.5567 +time/epoch (s) 23.8083 +time/total (s) 21224.3 +Epoch -176 +------------------------------ ---------------- +2022-05-15 23:56:37.859880 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -175 finished +------------------------------ ---------------- +epoch -175 +replay_buffer/size 999047 +trainer/num train calls 826000 +trainer/QF1 Loss 0.785739 +trainer/QF2 Loss 0.792631 +trainer/Policy Loss 19.7643 +trainer/Q1 Predictions Mean -71.5193 +trainer/Q1 Predictions Std 19.9368 +trainer/Q1 Predictions Max -0.0899083 +trainer/Q1 Predictions Min -90.5703 +trainer/Q2 Predictions Mean -71.5086 +trainer/Q2 Predictions Std 19.9085 +trainer/Q2 Predictions Max -0.202277 +trainer/Q2 Predictions Min -90.5032 +trainer/Q Targets Mean -71.4954 +trainer/Q Targets Std 19.9513 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.1545 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00461237 +trainer/policy/mean Std 0.727067 +trainer/policy/mean Max 0.998572 +trainer/policy/mean Min -0.999605 +trainer/policy/std Mean 0.410797 +trainer/policy/std Std 0.0201544 +trainer/policy/std Max 0.432861 +trainer/policy/std Min 0.380909 +trainer/Advantage Weights Mean 3.24001 +trainer/Advantage Weights Std 13.3283 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.97218e-13 +trainer/Advantage Score Mean -0.408776 +trainer/Advantage Score Std 0.620908 +trainer/Advantage Score Max 0.698334 +trainer/Advantage Score Min -2.92545 +trainer/V1 Predictions Mean -71.1327 +trainer/V1 Predictions Std 20.1883 +trainer/V1 Predictions Max 0.549074 +trainer/V1 Predictions Min -90.2805 +trainer/VF Loss 0.0644763 +expl/num steps total 826000 +expl/num paths total 1135 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0237121 +expl/Actions Std 0.908116 +expl/Actions Max 2.31317 +expl/Actions Min -2.45678 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 755519 +eval/num paths total 838 +eval/path length Mean 592 +eval/path length Std 0 +eval/path length Max 592 +eval/path length Min 592 +eval/Rewards Mean 0.00168919 +eval/Rewards Std 0.041065 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0217706 +eval/Actions Std 0.737393 +eval/Actions Max 0.999525 +eval/Actions Min -0.999311 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.9502e-06 +time/evaluation sampling (s) 2.97666 +time/exploration sampling (s) 4.32271 +time/logging (s) 0.00923844 +time/saving (s) 0.0184418 +time/training (s) 15.5569 +time/epoch (s) 22.8839 +time/total (s) 21247.2 +Epoch -175 +------------------------------ ---------------- +2022-05-15 23:57:00.439911 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -174 finished +------------------------------ ---------------- +epoch -174 +replay_buffer/size 999047 +trainer/num train calls 827000 +trainer/QF1 Loss 1.24302 +trainer/QF2 Loss 1.33563 +trainer/Policy Loss 25.1407 +trainer/Q1 Predictions Mean -71.2057 +trainer/Q1 Predictions Std 19.7556 +trainer/Q1 Predictions Max -0.947474 +trainer/Q1 Predictions Min -86.9432 +trainer/Q2 Predictions Mean -71.1961 +trainer/Q2 Predictions Std 19.8094 +trainer/Q2 Predictions Max -1.40139 +trainer/Q2 Predictions Min -86.8132 +trainer/Q Targets Mean -71.2404 +trainer/Q Targets Std 19.5278 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8036 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000107118 +trainer/policy/mean Std 0.727295 +trainer/policy/mean Max 0.998753 +trainer/policy/mean Min -0.998819 +trainer/policy/std Mean 0.412481 +trainer/policy/std Std 0.0214831 +trainer/policy/std Max 0.435139 +trainer/policy/std Min 0.380925 +trainer/Advantage Weights Mean 6.25878 +trainer/Advantage Weights Std 21.0849 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.07824e-28 +trainer/Advantage Score Mean -0.354656 +trainer/Advantage Score Std 0.74257 +trainer/Advantage Score Max 2.46741 +trainer/Advantage Score Min -6.43971 +trainer/V1 Predictions Mean -70.9512 +trainer/V1 Predictions Std 19.8204 +trainer/V1 Predictions Max -0.184039 +trainer/V1 Predictions Min -86.839 +trainer/VF Loss 0.119789 +expl/num steps total 827000 +expl/num paths total 1136 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0163099 +expl/Actions Std 0.819355 +expl/Actions Max 2.3172 +expl/Actions Min -2.35868 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 756519 +eval/num paths total 839 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0515069 +eval/Actions Std 0.708324 +eval/Actions Max 0.999856 +eval/Actions Min -0.99947 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.86195e-06 +time/evaluation sampling (s) 3.59853 +time/exploration sampling (s) 3.72335 +time/logging (s) 0.0113226 +time/saving (s) 0.0149585 +time/training (s) 15.212 +time/epoch (s) 22.5602 +time/total (s) 21269.7 +Epoch -174 +------------------------------ ---------------- +2022-05-15 23:57:23.644815 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -173 finished +------------------------------ --------------- +epoch -173 +replay_buffer/size 999047 +trainer/num train calls 828000 +trainer/QF1 Loss 1.11322 +trainer/QF2 Loss 1.0882 +trainer/Policy Loss 25.1223 +trainer/Q1 Predictions Mean -70.7291 +trainer/Q1 Predictions Std 18.4754 +trainer/Q1 Predictions Max -0.870808 +trainer/Q1 Predictions Min -87.82 +trainer/Q2 Predictions Mean -70.7519 +trainer/Q2 Predictions Std 18.5414 +trainer/Q2 Predictions Max -0.314869 +trainer/Q2 Predictions Min -87.4967 +trainer/Q Targets Mean -70.9528 +trainer/Q Targets Std 18.6616 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4728 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0123749 +trainer/policy/mean Std 0.72837 +trainer/policy/mean Max 0.999637 +trainer/policy/mean Min -0.999251 +trainer/policy/std Mean 0.410201 +trainer/policy/std Std 0.0211966 +trainer/policy/std Max 0.434059 +trainer/policy/std Min 0.378609 +trainer/Advantage Weights Mean 6.00704 +trainer/Advantage Weights Std 19.2903 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.7832e-14 +trainer/Advantage Score Mean -0.316524 +trainer/Advantage Score Std 0.609852 +trainer/Advantage Score Max 1.28588 +trainer/Advantage Score Min -3.06711 +trainer/V1 Predictions Mean -70.6847 +trainer/V1 Predictions Std 18.711 +trainer/V1 Predictions Max 0.306335 +trainer/V1 Predictions Min -87.3205 +trainer/VF Loss 0.0727932 +expl/num steps total 828000 +expl/num paths total 1138 +expl/path length Mean 500 +expl/path length Std 38 +expl/path length Max 538 +expl/path length Min 462 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0704256 +expl/Actions Std 0.837601 +expl/Actions Max 2.29712 +expl/Actions Min -2.35817 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 757308 +eval/num paths total 840 +eval/path length Mean 789 +eval/path length Std 0 +eval/path length Max 789 +eval/path length Min 789 +eval/Rewards Mean 0.00126743 +eval/Rewards Std 0.0355784 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0274026 +eval/Actions Std 0.737326 +eval/Actions Max 0.999768 +eval/Actions Min -0.999387 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.9779e-06 +time/evaluation sampling (s) 3.28007 +time/exploration sampling (s) 4.61916 +time/logging (s) 0.00832373 +time/saving (s) 0.011023 +time/training (s) 15.2639 +time/epoch (s) 23.1825 +time/total (s) 21292.9 +Epoch -173 +------------------------------ --------------- +2022-05-15 23:57:46.103001 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -172 finished +------------------------------ ---------------- +epoch -172 +replay_buffer/size 999047 +trainer/num train calls 829000 +trainer/QF1 Loss 0.707977 +trainer/QF2 Loss 0.600452 +trainer/Policy Loss 6.62935 +trainer/Q1 Predictions Mean -72.7134 +trainer/Q1 Predictions Std 18.2745 +trainer/Q1 Predictions Max -0.258146 +trainer/Q1 Predictions Min -88.9229 +trainer/Q2 Predictions Mean -72.716 +trainer/Q2 Predictions Std 18.2472 +trainer/Q2 Predictions Max -0.396393 +trainer/Q2 Predictions Min -88.8793 +trainer/Q Targets Mean -72.5789 +trainer/Q Targets Std 18.1697 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.0105 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0180141 +trainer/policy/mean Std 0.721089 +trainer/policy/mean Max 0.998421 +trainer/policy/mean Min -0.998379 +trainer/policy/std Mean 0.41159 +trainer/policy/std Std 0.0194555 +trainer/policy/std Max 0.429488 +trainer/policy/std Min 0.383331 +trainer/Advantage Weights Mean 2.06222 +trainer/Advantage Weights Std 12.8542 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.40575e-33 +trainer/Advantage Score Mean -0.604125 +trainer/Advantage Score Std 0.725162 +trainer/Advantage Score Max 1.19619 +trainer/Advantage Score Min -7.47598 +trainer/V1 Predictions Mean -72.1369 +trainer/V1 Predictions Std 18.4924 +trainer/V1 Predictions Max 0.557037 +trainer/V1 Predictions Min -88.9875 +trainer/VF Loss 0.10002 +expl/num steps total 829000 +expl/num paths total 1139 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.173576 +expl/Actions Std 0.790154 +expl/Actions Max 2.30496 +expl/Actions Min -2.31917 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 758016 +eval/num paths total 841 +eval/path length Mean 708 +eval/path length Std 0 +eval/path length Max 708 +eval/path length Min 708 +eval/Rewards Mean 0.00141243 +eval/Rewards Std 0.0375558 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0223215 +eval/Actions Std 0.720592 +eval/Actions Max 0.999528 +eval/Actions Min -0.99954 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.70875e-06 +time/evaluation sampling (s) 3.60681 +time/exploration sampling (s) 3.76155 +time/logging (s) 0.010782 +time/saving (s) 0.0168447 +time/training (s) 15.0462 +time/epoch (s) 22.4422 +time/total (s) 21315.4 +Epoch -172 +------------------------------ ---------------- +2022-05-15 23:58:07.607417 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -171 finished +------------------------------ ---------------- +epoch -171 +replay_buffer/size 999047 +trainer/num train calls 830000 +trainer/QF1 Loss 0.824149 +trainer/QF2 Loss 0.696767 +trainer/Policy Loss 10.5839 +trainer/Q1 Predictions Mean -71.9691 +trainer/Q1 Predictions Std 18.154 +trainer/Q1 Predictions Max -0.129244 +trainer/Q1 Predictions Min -90.4203 +trainer/Q2 Predictions Mean -71.9811 +trainer/Q2 Predictions Std 18.2018 +trainer/Q2 Predictions Max -1.2539 +trainer/Q2 Predictions Min -90.1829 +trainer/Q Targets Mean -71.5905 +trainer/Q Targets Std 18.2093 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.9292 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000846435 +trainer/policy/mean Std 0.720427 +trainer/policy/mean Max 0.999635 +trainer/policy/mean Min -0.999 +trainer/policy/std Mean 0.412544 +trainer/policy/std Std 0.02058 +trainer/policy/std Max 0.435127 +trainer/policy/std Min 0.384631 +trainer/Advantage Weights Mean 3.46182 +trainer/Advantage Weights Std 14.2792 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.16995e-21 +trainer/Advantage Score Mean -0.491008 +trainer/Advantage Score Std 0.64944 +trainer/Advantage Score Max 0.993591 +trainer/Advantage Score Min -4.81973 +trainer/V1 Predictions Mean -71.3461 +trainer/V1 Predictions Std 18.3198 +trainer/V1 Predictions Max -0.351984 +trainer/V1 Predictions Min -89.7421 +trainer/VF Loss 0.0803901 +expl/num steps total 830000 +expl/num paths total 1141 +expl/path length Mean 500 +expl/path length Std 224 +expl/path length Max 724 +expl/path length Min 276 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0616376 +expl/Actions Std 0.831906 +expl/Actions Max 2.28055 +expl/Actions Min -2.28233 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 759016 +eval/num paths total 842 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0526673 +eval/Actions Std 0.709841 +eval/Actions Max 0.99992 +eval/Actions Min -0.999549 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.99282e-06 +time/evaluation sampling (s) 3.03496 +time/exploration sampling (s) 3.8198 +time/logging (s) 0.011761 +time/saving (s) 0.0180493 +time/training (s) 14.602 +time/epoch (s) 21.4866 +time/total (s) 21336.8 +Epoch -171 +------------------------------ ---------------- +2022-05-15 23:58:26.884355 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -170 finished +------------------------------ ---------------- +epoch -170 +replay_buffer/size 999047 +trainer/num train calls 831000 +trainer/QF1 Loss 9.19181 +trainer/QF2 Loss 9.04619 +trainer/Policy Loss 9.71611 +trainer/Q1 Predictions Mean -71.5164 +trainer/Q1 Predictions Std 17.7433 +trainer/Q1 Predictions Max -1.34377 +trainer/Q1 Predictions Min -87.0138 +trainer/Q2 Predictions Mean -71.4849 +trainer/Q2 Predictions Std 17.7263 +trainer/Q2 Predictions Max -1.63009 +trainer/Q2 Predictions Min -86.7665 +trainer/Q Targets Mean -71.2763 +trainer/Q Targets Std 17.6552 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4894 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0102363 +trainer/policy/mean Std 0.722772 +trainer/policy/mean Max 0.999642 +trainer/policy/mean Min -0.999617 +trainer/policy/std Mean 0.412441 +trainer/policy/std Std 0.0195502 +trainer/policy/std Max 0.432492 +trainer/policy/std Min 0.383906 +trainer/Advantage Weights Mean 2.58936 +trainer/Advantage Weights Std 12.4916 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.97011e-18 +trainer/Advantage Score Mean -0.507382 +trainer/Advantage Score Std 0.695227 +trainer/Advantage Score Max 1.98033 +trainer/Advantage Score Min -4.03579 +trainer/V1 Predictions Mean -71.0695 +trainer/V1 Predictions Std 17.7969 +trainer/V1 Predictions Max -0.773883 +trainer/V1 Predictions Min -86.4274 +trainer/VF Loss 0.0937755 +expl/num steps total 831000 +expl/num paths total 1143 +expl/path length Mean 500 +expl/path length Std 352 +expl/path length Max 852 +expl/path length Min 148 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0271927 +expl/Actions Std 0.833672 +expl/Actions Max 2.55728 +expl/Actions Min -2.45596 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 760016 +eval/num paths total 843 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0736787 +eval/Actions Std 0.67341 +eval/Actions Max 0.999771 +eval/Actions Min -0.999865 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.80002e-06 +time/evaluation sampling (s) 2.88074 +time/exploration sampling (s) 2.83135 +time/logging (s) 0.0113239 +time/saving (s) 0.0155258 +time/training (s) 13.5173 +time/epoch (s) 19.2562 +time/total (s) 21356.1 +Epoch -170 +------------------------------ ---------------- +2022-05-15 23:58:46.027758 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -169 finished +------------------------------ ---------------- +epoch -169 +replay_buffer/size 999047 +trainer/num train calls 832000 +trainer/QF1 Loss 0.731349 +trainer/QF2 Loss 0.767634 +trainer/Policy Loss 23.3088 +trainer/Q1 Predictions Mean -71.9565 +trainer/Q1 Predictions Std 18.1155 +trainer/Q1 Predictions Max -0.050838 +trainer/Q1 Predictions Min -87.9043 +trainer/Q2 Predictions Mean -72.0211 +trainer/Q2 Predictions Std 17.9863 +trainer/Q2 Predictions Max -0.68744 +trainer/Q2 Predictions Min -87.6223 +trainer/Q Targets Mean -71.8993 +trainer/Q Targets Std 18.2423 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7643 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0298911 +trainer/policy/mean Std 0.726011 +trainer/policy/mean Max 0.999493 +trainer/policy/mean Min -0.999454 +trainer/policy/std Mean 0.412444 +trainer/policy/std Std 0.0199002 +trainer/policy/std Max 0.433911 +trainer/policy/std Min 0.383284 +trainer/Advantage Weights Mean 4.19398 +trainer/Advantage Weights Std 16.4742 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.7054e-16 +trainer/Advantage Score Mean -0.373277 +trainer/Advantage Score Std 0.685721 +trainer/Advantage Score Max 3.21486 +trainer/Advantage Score Min -3.63076 +trainer/V1 Predictions Mean -71.6747 +trainer/V1 Predictions Std 18.3809 +trainer/V1 Predictions Max 1.19897 +trainer/V1 Predictions Min -87.7742 +trainer/VF Loss 0.104728 +expl/num steps total 832000 +expl/num paths total 1144 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.218717 +expl/Actions Std 0.81041 +expl/Actions Max 2.35937 +expl/Actions Min -2.3172 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 761016 +eval/num paths total 844 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.17065 +eval/Actions Std 0.769564 +eval/Actions Max 0.999445 +eval/Actions Min -0.999472 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.70968e-06 +time/evaluation sampling (s) 2.64278 +time/exploration sampling (s) 2.8973 +time/logging (s) 0.00774775 +time/saving (s) 0.0123774 +time/training (s) 13.5575 +time/epoch (s) 19.1177 +time/total (s) 21375.2 +Epoch -169 +------------------------------ ---------------- +2022-05-15 23:59:05.324131 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -168 finished +------------------------------ ---------------- +epoch -168 +replay_buffer/size 999047 +trainer/num train calls 833000 +trainer/QF1 Loss 0.592842 +trainer/QF2 Loss 0.691563 +trainer/Policy Loss 5.47373 +trainer/Q1 Predictions Mean -70.6755 +trainer/Q1 Predictions Std 19.1863 +trainer/Q1 Predictions Max -0.148998 +trainer/Q1 Predictions Min -86.7198 +trainer/Q2 Predictions Mean -70.6993 +trainer/Q2 Predictions Std 19.2784 +trainer/Q2 Predictions Max -0.264625 +trainer/Q2 Predictions Min -86.6304 +trainer/Q Targets Mean -70.5708 +trainer/Q Targets Std 19.3876 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4561 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0366252 +trainer/policy/mean Std 0.730625 +trainer/policy/mean Max 0.996975 +trainer/policy/mean Min -0.998846 +trainer/policy/std Mean 0.41259 +trainer/policy/std Std 0.0201087 +trainer/policy/std Max 0.434962 +trainer/policy/std Min 0.382351 +trainer/Advantage Weights Mean 1.56351 +trainer/Advantage Weights Std 11.0543 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.44966e-21 +trainer/Advantage Score Mean -0.66057 +trainer/Advantage Score Std 0.682578 +trainer/Advantage Score Max 1.08739 +trainer/Advantage Score Min -4.7983 +trainer/V1 Predictions Mean -70.3685 +trainer/V1 Predictions Std 19.4057 +trainer/V1 Predictions Max 0.488614 +trainer/V1 Predictions Min -86.3763 +trainer/VF Loss 0.0983844 +expl/num steps total 833000 +expl/num paths total 1146 +expl/path length Mean 500 +expl/path length Std 337 +expl/path length Max 837 +expl/path length Min 163 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0151404 +expl/Actions Std 0.812746 +expl/Actions Max 2.29871 +expl/Actions Min -2.54741 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 762016 +eval/num paths total 845 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0277563 +eval/Actions Std 0.700863 +eval/Actions Max 0.99993 +eval/Actions Min -0.999855 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.6599e-06 +time/evaluation sampling (s) 2.50214 +time/exploration sampling (s) 2.73652 +time/logging (s) 0.0120255 +time/saving (s) 0.016302 +time/training (s) 14.0183 +time/epoch (s) 19.2853 +time/total (s) 21394.5 +Epoch -168 +------------------------------ ---------------- +2022-05-15 23:59:24.845544 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -167 finished +------------------------------ ---------------- +epoch -167 +replay_buffer/size 999047 +trainer/num train calls 834000 +trainer/QF1 Loss 2.45065 +trainer/QF2 Loss 2.44 +trainer/Policy Loss 63.1362 +trainer/Q1 Predictions Mean -71.5955 +trainer/Q1 Predictions Std 18.1301 +trainer/Q1 Predictions Max -0.56118 +trainer/Q1 Predictions Min -86.2237 +trainer/Q2 Predictions Mean -71.6891 +trainer/Q2 Predictions Std 18.1906 +trainer/Q2 Predictions Max -0.72809 +trainer/Q2 Predictions Min -85.9899 +trainer/Q Targets Mean -72.4204 +trainer/Q Targets Std 17.6465 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9989 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000765291 +trainer/policy/mean Std 0.730629 +trainer/policy/mean Max 0.997942 +trainer/policy/mean Min -0.999532 +trainer/policy/std Mean 0.41106 +trainer/policy/std Std 0.0208068 +trainer/policy/std Max 0.432341 +trainer/policy/std Min 0.379524 +trainer/Advantage Weights Mean 15.2217 +trainer/Advantage Weights Std 33.0881 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.67209e-16 +trainer/Advantage Score Mean -0.180917 +trainer/Advantage Score Std 0.644652 +trainer/Advantage Score Max 1.67148 +trainer/Advantage Score Min -3.48038 +trainer/V1 Predictions Mean -72.0515 +trainer/V1 Predictions Std 17.8642 +trainer/V1 Predictions Max -2.24695 +trainer/V1 Predictions Min -86.9308 +trainer/VF Loss 0.119685 +expl/num steps total 834000 +expl/num paths total 1147 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0264224 +expl/Actions Std 0.827513 +expl/Actions Max 2.29308 +expl/Actions Min -2.15191 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 762841 +eval/num paths total 846 +eval/path length Mean 825 +eval/path length Std 0 +eval/path length Max 825 +eval/path length Min 825 +eval/Rewards Mean 0.00121212 +eval/Rewards Std 0.0347944 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0304553 +eval/Actions Std 0.736959 +eval/Actions Max 0.999876 +eval/Actions Min -0.999895 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.36607e-06 +time/evaluation sampling (s) 2.49253 +time/exploration sampling (s) 2.77641 +time/logging (s) 0.00720028 +time/saving (s) 0.0113573 +time/training (s) 14.2129 +time/epoch (s) 19.5004 +time/total (s) 21414 +Epoch -167 +------------------------------ ---------------- +2022-05-15 23:59:44.261296 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -166 finished +------------------------------ ---------------- +epoch -166 +replay_buffer/size 999047 +trainer/num train calls 835000 +trainer/QF1 Loss 0.654663 +trainer/QF2 Loss 0.87021 +trainer/Policy Loss 26.9919 +trainer/Q1 Predictions Mean -71.5162 +trainer/Q1 Predictions Std 19.8204 +trainer/Q1 Predictions Max -0.981336 +trainer/Q1 Predictions Min -86.5977 +trainer/Q2 Predictions Mean -71.4807 +trainer/Q2 Predictions Std 19.7677 +trainer/Q2 Predictions Max 0.107445 +trainer/Q2 Predictions Min -86.6402 +trainer/Q Targets Mean -71.4469 +trainer/Q Targets Std 19.8912 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3157 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00857244 +trainer/policy/mean Std 0.730598 +trainer/policy/mean Max 0.999029 +trainer/policy/mean Min -0.999464 +trainer/policy/std Mean 0.413379 +trainer/policy/std Std 0.0222265 +trainer/policy/std Max 0.438493 +trainer/policy/std Min 0.381797 +trainer/Advantage Weights Mean 5.49713 +trainer/Advantage Weights Std 19.0721 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.10239e-15 +trainer/Advantage Score Mean -0.313427 +trainer/Advantage Score Std 0.621542 +trainer/Advantage Score Max 1.49135 +trainer/Advantage Score Min -3.37957 +trainer/V1 Predictions Mean -71.1972 +trainer/V1 Predictions Std 20.0428 +trainer/V1 Predictions Max 1.15859 +trainer/V1 Predictions Min -86.1527 +trainer/VF Loss 0.0738371 +expl/num steps total 835000 +expl/num paths total 1148 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.109469 +expl/Actions Std 0.853185 +expl/Actions Max 2.4966 +expl/Actions Min -2.30028 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 763721 +eval/num paths total 847 +eval/path length Mean 880 +eval/path length Std 0 +eval/path length Max 880 +eval/path length Min 880 +eval/Rewards Mean 0.00113636 +eval/Rewards Std 0.0336908 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0205565 +eval/Actions Std 0.746542 +eval/Actions Max 0.999531 +eval/Actions Min -0.999506 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.29688e-06 +time/evaluation sampling (s) 2.57807 +time/exploration sampling (s) 2.96994 +time/logging (s) 0.00897635 +time/saving (s) 0.0143387 +time/training (s) 13.8333 +time/epoch (s) 19.4046 +time/total (s) 21433.4 +Epoch -166 +------------------------------ ---------------- +2022-05-16 00:00:03.008582 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -165 finished +------------------------------ ---------------- +epoch -165 +replay_buffer/size 999047 +trainer/num train calls 836000 +trainer/QF1 Loss 0.741174 +trainer/QF2 Loss 0.703817 +trainer/Policy Loss 29.3034 +trainer/Q1 Predictions Mean -72.8554 +trainer/Q1 Predictions Std 17.0264 +trainer/Q1 Predictions Max -0.862025 +trainer/Q1 Predictions Min -89.0481 +trainer/Q2 Predictions Mean -72.7118 +trainer/Q2 Predictions Std 17.0679 +trainer/Q2 Predictions Max -1.37048 +trainer/Q2 Predictions Min -88.6502 +trainer/Q Targets Mean -73.0708 +trainer/Q Targets Std 17.2508 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.3954 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0281107 +trainer/policy/mean Std 0.720066 +trainer/policy/mean Max 0.999948 +trainer/policy/mean Min -0.99958 +trainer/policy/std Mean 0.413768 +trainer/policy/std Std 0.021932 +trainer/policy/std Max 0.436899 +trainer/policy/std Min 0.383451 +trainer/Advantage Weights Mean 8.76509 +trainer/Advantage Weights Std 23.1299 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.25468e-27 +trainer/Advantage Score Mean -0.239 +trainer/Advantage Score Std 0.744888 +trainer/Advantage Score Max 2.3372 +trainer/Advantage Score Min -6.07218 +trainer/V1 Predictions Mean -72.8528 +trainer/V1 Predictions Std 17.241 +trainer/V1 Predictions Max -1.09291 +trainer/V1 Predictions Min -89.3437 +trainer/VF Loss 0.115683 +expl/num steps total 836000 +expl/num paths total 1149 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.1369 +expl/Actions Std 0.822433 +expl/Actions Max 2.27347 +expl/Actions Min -2.19764 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 764721 +eval/num paths total 848 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0366795 +eval/Actions Std 0.716557 +eval/Actions Max 0.999761 +eval/Actions Min -0.999536 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.06084e-06 +time/evaluation sampling (s) 2.34683 +time/exploration sampling (s) 2.76381 +time/logging (s) 0.0104277 +time/saving (s) 0.0155278 +time/training (s) 13.594 +time/epoch (s) 18.7306 +time/total (s) 21452.2 +Epoch -165 +------------------------------ ---------------- +2022-05-16 00:00:22.225302 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -164 finished +------------------------------ ---------------- +epoch -164 +replay_buffer/size 999047 +trainer/num train calls 837000 +trainer/QF1 Loss 4.11072 +trainer/QF2 Loss 4.23235 +trainer/Policy Loss 13.7129 +trainer/Q1 Predictions Mean -70.8932 +trainer/Q1 Predictions Std 19.065 +trainer/Q1 Predictions Max -0.910167 +trainer/Q1 Predictions Min -88.247 +trainer/Q2 Predictions Mean -70.8866 +trainer/Q2 Predictions Std 19.2041 +trainer/Q2 Predictions Max -0.301146 +trainer/Q2 Predictions Min -89.0785 +trainer/Q Targets Mean -71.1793 +trainer/Q Targets Std 19.2253 +trainer/Q Targets Max -2.06978 +trainer/Q Targets Min -88.6319 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00806551 +trainer/policy/mean Std 0.723332 +trainer/policy/mean Max 0.999198 +trainer/policy/mean Min -0.999386 +trainer/policy/std Mean 0.412797 +trainer/policy/std Std 0.0208781 +trainer/policy/std Max 0.436604 +trainer/policy/std Min 0.38172 +trainer/Advantage Weights Mean 4.68622 +trainer/Advantage Weights Std 18.0747 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.32794e-22 +trainer/Advantage Score Mean -0.459219 +trainer/Advantage Score Std 0.774362 +trainer/Advantage Score Max 1.06555 +trainer/Advantage Score Min -4.91918 +trainer/V1 Predictions Mean -70.679 +trainer/V1 Predictions Std 19.5179 +trainer/V1 Predictions Max 1.10141 +trainer/V1 Predictions Min -88.3837 +trainer/VF Loss 0.0964014 +expl/num steps total 837000 +expl/num paths total 1150 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0536465 +expl/Actions Std 0.807964 +expl/Actions Max 2.45465 +expl/Actions Min -2.2695 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 765721 +eval/num paths total 849 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0122408 +eval/Actions Std 0.716335 +eval/Actions Max 0.999732 +eval/Actions Min -0.999596 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.08501e-06 +time/evaluation sampling (s) 2.57611 +time/exploration sampling (s) 2.7241 +time/logging (s) 0.00866711 +time/saving (s) 0.0108063 +time/training (s) 13.877 +time/epoch (s) 19.1967 +time/total (s) 21471.4 +Epoch -164 +------------------------------ ---------------- +2022-05-16 00:00:41.359926 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -163 finished +------------------------------ ---------------- +epoch -163 +replay_buffer/size 999047 +trainer/num train calls 838000 +trainer/QF1 Loss 0.702789 +trainer/QF2 Loss 0.741657 +trainer/Policy Loss 22.8605 +trainer/Q1 Predictions Mean -71.0597 +trainer/Q1 Predictions Std 17.9963 +trainer/Q1 Predictions Max -3.16675 +trainer/Q1 Predictions Min -86.7796 +trainer/Q2 Predictions Mean -71.0074 +trainer/Q2 Predictions Std 18.0237 +trainer/Q2 Predictions Max -1.81975 +trainer/Q2 Predictions Min -86.9 +trainer/Q Targets Mean -70.9288 +trainer/Q Targets Std 17.8591 +trainer/Q Targets Max -2.68598 +trainer/Q Targets Min -86.6257 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0307563 +trainer/policy/mean Std 0.726495 +trainer/policy/mean Max 0.997755 +trainer/policy/mean Min -0.999792 +trainer/policy/std Mean 0.413879 +trainer/policy/std Std 0.0202758 +trainer/policy/std Max 0.437281 +trainer/policy/std Min 0.384227 +trainer/Advantage Weights Mean 5.12351 +trainer/Advantage Weights Std 18.5741 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.30324e-17 +trainer/Advantage Score Mean -0.390742 +trainer/Advantage Score Std 0.633935 +trainer/Advantage Score Max 1.8277 +trainer/Advantage Score Min -3.83096 +trainer/V1 Predictions Mean -70.6258 +trainer/V1 Predictions Std 18.0359 +trainer/V1 Predictions Max -1.54466 +trainer/V1 Predictions Min -86.5192 +trainer/VF Loss 0.0858969 +expl/num steps total 838000 +expl/num paths total 1152 +expl/path length Mean 500 +expl/path length Std 366 +expl/path length Max 866 +expl/path length Min 134 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0180117 +expl/Actions Std 0.828603 +expl/Actions Max 2.47758 +expl/Actions Min -2.45189 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 766381 +eval/num paths total 850 +eval/path length Mean 660 +eval/path length Std 0 +eval/path length Max 660 +eval/path length Min 660 +eval/Rewards Mean 0.00151515 +eval/Rewards Std 0.0388954 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0116547 +eval/Actions Std 0.755415 +eval/Actions Max 0.999963 +eval/Actions Min -0.99978 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.33185e-06 +time/evaluation sampling (s) 2.48812 +time/exploration sampling (s) 2.80169 +time/logging (s) 0.00912281 +time/saving (s) 0.0148462 +time/training (s) 13.8077 +time/epoch (s) 19.1215 +time/total (s) 21490.5 +Epoch -163 +------------------------------ ---------------- +2022-05-16 00:01:00.564339 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -162 finished +------------------------------ ---------------- +epoch -162 +replay_buffer/size 999047 +trainer/num train calls 839000 +trainer/QF1 Loss 0.972268 +trainer/QF2 Loss 0.854152 +trainer/Policy Loss 6.75954 +trainer/Q1 Predictions Mean -73.1355 +trainer/Q1 Predictions Std 16.9091 +trainer/Q1 Predictions Max -0.985009 +trainer/Q1 Predictions Min -89.6449 +trainer/Q2 Predictions Mean -73.0462 +trainer/Q2 Predictions Std 16.9328 +trainer/Q2 Predictions Max -1.19488 +trainer/Q2 Predictions Min -89.2866 +trainer/Q Targets Mean -72.5597 +trainer/Q Targets Std 17.1141 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.1968 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0101193 +trainer/policy/mean Std 0.734254 +trainer/policy/mean Max 0.999187 +trainer/policy/mean Min -0.999686 +trainer/policy/std Mean 0.413321 +trainer/policy/std Std 0.0201713 +trainer/policy/std Max 0.434914 +trainer/policy/std Min 0.383183 +trainer/Advantage Weights Mean 2.1211 +trainer/Advantage Weights Std 12.9786 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.79855e-23 +trainer/Advantage Score Mean -0.708338 +trainer/Advantage Score Std 0.73884 +trainer/Advantage Score Max 1.89231 +trainer/Advantage Score Min -5.13911 +trainer/V1 Predictions Mean -72.3119 +trainer/V1 Predictions Std 17.191 +trainer/V1 Predictions Max 0.176544 +trainer/V1 Predictions Min -87.6898 +trainer/VF Loss 0.121366 +expl/num steps total 839000 +expl/num paths total 1153 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00307173 +expl/Actions Std 0.865356 +expl/Actions Max 2.29776 +expl/Actions Min -2.2085 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 767381 +eval/num paths total 851 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.111097 +eval/Actions Std 0.719075 +eval/Actions Max 0.999691 +eval/Actions Min -0.999931 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.19304e-06 +time/evaluation sampling (s) 2.37265 +time/exploration sampling (s) 2.70616 +time/logging (s) 0.00815905 +time/saving (s) 0.0131306 +time/training (s) 14.0895 +time/epoch (s) 19.1896 +time/total (s) 21509.7 +Epoch -162 +------------------------------ ---------------- +2022-05-16 00:01:19.673201 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -161 finished +------------------------------ ---------------- +epoch -161 +replay_buffer/size 999047 +trainer/num train calls 840000 +trainer/QF1 Loss 0.93518 +trainer/QF2 Loss 0.774471 +trainer/Policy Loss 9.04697 +trainer/Q1 Predictions Mean -73.2263 +trainer/Q1 Predictions Std 17.2484 +trainer/Q1 Predictions Max 0.0508244 +trainer/Q1 Predictions Min -87.5075 +trainer/Q2 Predictions Mean -73.1999 +trainer/Q2 Predictions Std 17.2567 +trainer/Q2 Predictions Max -0.242326 +trainer/Q2 Predictions Min -87.4513 +trainer/Q Targets Mean -72.7411 +trainer/Q Targets Std 17.2277 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9471 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00199447 +trainer/policy/mean Std 0.739225 +trainer/policy/mean Max 0.999149 +trainer/policy/mean Min -0.999046 +trainer/policy/std Mean 0.411852 +trainer/policy/std Std 0.0223749 +trainer/policy/std Max 0.434067 +trainer/policy/std Min 0.377776 +trainer/Advantage Weights Mean 2.67604 +trainer/Advantage Weights Std 15.2901 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.89753e-16 +trainer/Advantage Score Mean -0.727192 +trainer/Advantage Score Std 0.687143 +trainer/Advantage Score Max 1.52407 +trainer/Advantage Score Min -3.57775 +trainer/V1 Predictions Mean -72.432 +trainer/V1 Predictions Std 17.3405 +trainer/V1 Predictions Max -0.210325 +trainer/V1 Predictions Min -86.644 +trainer/VF Loss 0.120807 +expl/num steps total 840000 +expl/num paths total 1154 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.00882277 +expl/Actions Std 0.857226 +expl/Actions Max 2.60614 +expl/Actions Min -2.31724 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 767929 +eval/num paths total 852 +eval/path length Mean 548 +eval/path length Std 0 +eval/path length Max 548 +eval/path length Min 548 +eval/Rewards Mean 0.00182482 +eval/Rewards Std 0.0426789 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.018578 +eval/Actions Std 0.739459 +eval/Actions Max 0.999314 +eval/Actions Min -0.999894 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.99187e-06 +time/evaluation sampling (s) 2.40475 +time/exploration sampling (s) 2.82712 +time/logging (s) 0.00912643 +time/saving (s) 0.015564 +time/training (s) 13.839 +time/epoch (s) 19.0956 +time/total (s) 21528.8 +Epoch -161 +------------------------------ ---------------- +2022-05-16 00:01:38.392409 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -160 finished +------------------------------ ---------------- +epoch -160 +replay_buffer/size 999047 +trainer/num train calls 841000 +trainer/QF1 Loss 0.620917 +trainer/QF2 Loss 0.583374 +trainer/Policy Loss 38.8838 +trainer/Q1 Predictions Mean -72.932 +trainer/Q1 Predictions Std 16.2068 +trainer/Q1 Predictions Max -1.63987 +trainer/Q1 Predictions Min -86.2344 +trainer/Q2 Predictions Mean -73.0301 +trainer/Q2 Predictions Std 16.1837 +trainer/Q2 Predictions Max -0.74624 +trainer/Q2 Predictions Min -86.3646 +trainer/Q Targets Mean -73.1831 +trainer/Q Targets Std 16.1448 +trainer/Q Targets Max -1.40349 +trainer/Q Targets Min -86.5067 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0195587 +trainer/policy/mean Std 0.724052 +trainer/policy/mean Max 0.998985 +trainer/policy/mean Min -0.999278 +trainer/policy/std Mean 0.411826 +trainer/policy/std Std 0.0209608 +trainer/policy/std Max 0.43437 +trainer/policy/std Min 0.38122 +trainer/Advantage Weights Mean 6.04101 +trainer/Advantage Weights Std 18.8597 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.25482e-13 +trainer/Advantage Score Mean -0.286628 +trainer/Advantage Score Std 0.542899 +trainer/Advantage Score Max 0.898888 +trainer/Advantage Score Min -2.82745 +trainer/V1 Predictions Mean -72.9464 +trainer/V1 Predictions Std 16.2308 +trainer/V1 Predictions Max -1.36615 +trainer/V1 Predictions Min -86.2434 +trainer/VF Loss 0.057291 +expl/num steps total 841000 +expl/num paths total 1155 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0557192 +expl/Actions Std 0.793386 +expl/Actions Max 2.48875 +expl/Actions Min -2.20489 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 768929 +eval/num paths total 853 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0970508 +eval/Actions Std 0.729874 +eval/Actions Max 0.999766 +eval/Actions Min -0.999232 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.0701e-06 +time/evaluation sampling (s) 2.37872 +time/exploration sampling (s) 2.86371 +time/logging (s) 0.010607 +time/saving (s) 0.015392 +time/training (s) 13.4355 +time/epoch (s) 18.7039 +time/total (s) 21547.5 +Epoch -160 +------------------------------ ---------------- +2022-05-16 00:01:56.953671 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -159 finished +------------------------------ ---------------- +epoch -159 +replay_buffer/size 999047 +trainer/num train calls 842000 +trainer/QF1 Loss 0.598928 +trainer/QF2 Loss 0.617693 +trainer/Policy Loss 15.8174 +trainer/Q1 Predictions Mean -72.6897 +trainer/Q1 Predictions Std 17.164 +trainer/Q1 Predictions Max 0.338406 +trainer/Q1 Predictions Min -86.2354 +trainer/Q2 Predictions Mean -72.654 +trainer/Q2 Predictions Std 17.1354 +trainer/Q2 Predictions Max 0.176716 +trainer/Q2 Predictions Min -86.3961 +trainer/Q Targets Mean -72.8038 +trainer/Q Targets Std 17.4213 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9587 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.034727 +trainer/policy/mean Std 0.742951 +trainer/policy/mean Max 0.999641 +trainer/policy/mean Min -0.999444 +trainer/policy/std Mean 0.412239 +trainer/policy/std Std 0.0201458 +trainer/policy/std Max 0.433985 +trainer/policy/std Min 0.382546 +trainer/Advantage Weights Mean 5.21725 +trainer/Advantage Weights Std 18.6316 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.72815e-22 +trainer/Advantage Score Mean -0.384875 +trainer/Advantage Score Std 0.710609 +trainer/Advantage Score Max 1.05235 +trainer/Advantage Score Min -4.87506 +trainer/V1 Predictions Mean -72.5753 +trainer/V1 Predictions Std 17.4932 +trainer/V1 Predictions Max 0.585277 +trainer/V1 Predictions Min -86.903 +trainer/VF Loss 0.0829111 +expl/num steps total 842000 +expl/num paths total 1157 +expl/path length Mean 500 +expl/path length Std 162 +expl/path length Max 662 +expl/path length Min 338 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00728043 +expl/Actions Std 0.849888 +expl/Actions Max 2.33264 +expl/Actions Min -2.23854 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 769922 +eval/num paths total 854 +eval/path length Mean 993 +eval/path length Std 0 +eval/path length Max 993 +eval/path length Min 993 +eval/Rewards Mean 0.00100705 +eval/Rewards Std 0.0317181 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0316662 +eval/Actions Std 0.742281 +eval/Actions Max 0.999676 +eval/Actions Min -0.999626 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.20794e-06 +time/evaluation sampling (s) 2.46965 +time/exploration sampling (s) 2.84814 +time/logging (s) 0.00838623 +time/saving (s) 0.0122046 +time/training (s) 13.2023 +time/epoch (s) 18.5407 +time/total (s) 21566.1 +Epoch -159 +------------------------------ ---------------- +2022-05-16 00:02:15.746353 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -158 finished +------------------------------ ---------------- +epoch -158 +replay_buffer/size 999047 +trainer/num train calls 843000 +trainer/QF1 Loss 0.853215 +trainer/QF2 Loss 0.858816 +trainer/Policy Loss 8.24764 +trainer/Q1 Predictions Mean -71.7549 +trainer/Q1 Predictions Std 19.0166 +trainer/Q1 Predictions Max 0.00173125 +trainer/Q1 Predictions Min -86.3935 +trainer/Q2 Predictions Mean -71.7967 +trainer/Q2 Predictions Std 19.0031 +trainer/Q2 Predictions Max -0.0569708 +trainer/Q2 Predictions Min -86.9506 +trainer/Q Targets Mean -71.6548 +trainer/Q Targets Std 18.8018 +trainer/Q Targets Max -1.2494 +trainer/Q Targets Min -87.051 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00299126 +trainer/policy/mean Std 0.735714 +trainer/policy/mean Max 0.999306 +trainer/policy/mean Min -0.998655 +trainer/policy/std Mean 0.411542 +trainer/policy/std Std 0.0195194 +trainer/policy/std Max 0.433954 +trainer/policy/std Min 0.38433 +trainer/Advantage Weights Mean 1.97377 +trainer/Advantage Weights Std 12.518 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.24324e-16 +trainer/Advantage Score Mean -0.572224 +trainer/Advantage Score Std 0.626855 +trainer/Advantage Score Max 1.99367 +trainer/Advantage Score Min -3.60334 +trainer/V1 Predictions Mean -71.4313 +trainer/V1 Predictions Std 18.9042 +trainer/V1 Predictions Max -0.26782 +trainer/V1 Predictions Min -86.5648 +trainer/VF Loss 0.0998666 +expl/num steps total 843000 +expl/num paths total 1158 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00234348 +expl/Actions Std 0.827964 +expl/Actions Max 2.19626 +expl/Actions Min -2.40976 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 770922 +eval/num paths total 855 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.223617 +eval/Actions Std 0.545949 +eval/Actions Max 0.999432 +eval/Actions Min -0.999555 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.03797e-06 +time/evaluation sampling (s) 2.46718 +time/exploration sampling (s) 2.83967 +time/logging (s) 0.00832756 +time/saving (s) 0.0100698 +time/training (s) 13.4519 +time/epoch (s) 18.7772 +time/total (s) 21584.8 +Epoch -158 +------------------------------ ---------------- +2022-05-16 00:02:35.163243 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -157 finished +------------------------------ ---------------- +epoch -157 +replay_buffer/size 999047 +trainer/num train calls 844000 +trainer/QF1 Loss 4.78439 +trainer/QF2 Loss 4.2969 +trainer/Policy Loss 23.5361 +trainer/Q1 Predictions Mean -73.0179 +trainer/Q1 Predictions Std 16.9316 +trainer/Q1 Predictions Max -1.44897 +trainer/Q1 Predictions Min -87.9519 +trainer/Q2 Predictions Mean -72.9786 +trainer/Q2 Predictions Std 16.9182 +trainer/Q2 Predictions Max -2.13131 +trainer/Q2 Predictions Min -87.5356 +trainer/Q Targets Mean -72.7051 +trainer/Q Targets Std 16.8457 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4827 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0032875 +trainer/policy/mean Std 0.725149 +trainer/policy/mean Max 0.999586 +trainer/policy/mean Min -0.999184 +trainer/policy/std Mean 0.411661 +trainer/policy/std Std 0.0205509 +trainer/policy/std Max 0.433449 +trainer/policy/std Min 0.379668 +trainer/Advantage Weights Mean 4.99972 +trainer/Advantage Weights Std 18.7708 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.73618e-14 +trainer/Advantage Score Mean -0.398649 +trainer/Advantage Score Std 0.739893 +trainer/Advantage Score Max 5.86554 +trainer/Advantage Score Min -3.12296 +trainer/V1 Predictions Mean -72.5042 +trainer/V1 Predictions Std 16.9275 +trainer/V1 Predictions Max -1.11877 +trainer/V1 Predictions Min -87.206 +trainer/VF Loss 0.196715 +expl/num steps total 844000 +expl/num paths total 1159 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0746035 +expl/Actions Std 0.818046 +expl/Actions Max 2.34151 +expl/Actions Min -2.32306 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 771231 +eval/num paths total 856 +eval/path length Mean 309 +eval/path length Std 0 +eval/path length Max 309 +eval/path length Min 309 +eval/Rewards Mean 0.00323625 +eval/Rewards Std 0.0567959 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0140667 +eval/Actions Std 0.756369 +eval/Actions Max 0.999754 +eval/Actions Min -0.999961 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.73529e-06 +time/evaluation sampling (s) 2.41362 +time/exploration sampling (s) 3.05373 +time/logging (s) 0.0078193 +time/saving (s) 0.0146656 +time/training (s) 13.9152 +time/epoch (s) 19.4051 +time/total (s) 21604.3 +Epoch -157 +------------------------------ ---------------- +2022-05-16 00:02:54.383206 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -156 finished +------------------------------ ---------------- +epoch -156 +replay_buffer/size 999047 +trainer/num train calls 845000 +trainer/QF1 Loss 0.861356 +trainer/QF2 Loss 1.00219 +trainer/Policy Loss 30.428 +trainer/Q1 Predictions Mean -72.5902 +trainer/Q1 Predictions Std 17.4257 +trainer/Q1 Predictions Max -0.803035 +trainer/Q1 Predictions Min -86.2174 +trainer/Q2 Predictions Mean -72.5933 +trainer/Q2 Predictions Std 17.4148 +trainer/Q2 Predictions Max 0.634695 +trainer/Q2 Predictions Min -86.0711 +trainer/Q Targets Mean -72.821 +trainer/Q Targets Std 17.6735 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5869 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00227461 +trainer/policy/mean Std 0.718119 +trainer/policy/mean Max 0.998585 +trainer/policy/mean Min -0.9997 +trainer/policy/std Mean 0.413303 +trainer/policy/std Std 0.0204146 +trainer/policy/std Max 0.43391 +trainer/policy/std Min 0.381944 +trainer/Advantage Weights Mean 5.26764 +trainer/Advantage Weights Std 17.8156 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.25664e-17 +trainer/Advantage Score Mean -0.299385 +trainer/Advantage Score Std 0.529788 +trainer/Advantage Score Max 1.12494 +trainer/Advantage Score Min -3.70329 +trainer/V1 Predictions Mean -72.6049 +trainer/V1 Predictions Std 17.7103 +trainer/V1 Predictions Max 0.0362074 +trainer/V1 Predictions Min -86.4509 +trainer/VF Loss 0.0584689 +expl/num steps total 845000 +expl/num paths total 1161 +expl/path length Mean 500 +expl/path length Std 442 +expl/path length Max 942 +expl/path length Min 58 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0129852 +expl/Actions Std 0.828284 +expl/Actions Max 2.23516 +expl/Actions Min -2.64965 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 772231 +eval/num paths total 857 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.108246 +eval/Actions Std 0.734165 +eval/Actions Max 0.999915 +eval/Actions Min -0.999679 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99187e-06 +time/evaluation sampling (s) 2.7031 +time/exploration sampling (s) 3.02481 +time/logging (s) 0.00948288 +time/saving (s) 0.0141071 +time/training (s) 13.4539 +time/epoch (s) 19.2054 +time/total (s) 21623.5 +Epoch -156 +------------------------------ ---------------- +2022-05-16 00:03:13.199083 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -155 finished +------------------------------ ---------------- +epoch -155 +replay_buffer/size 999047 +trainer/num train calls 846000 +trainer/QF1 Loss 1.39957 +trainer/QF2 Loss 1.6244 +trainer/Policy Loss 16.9311 +trainer/Q1 Predictions Mean -70.9091 +trainer/Q1 Predictions Std 19.5667 +trainer/Q1 Predictions Max -1.18341 +trainer/Q1 Predictions Min -86.4674 +trainer/Q2 Predictions Mean -70.9144 +trainer/Q2 Predictions Std 19.5113 +trainer/Q2 Predictions Max -0.992702 +trainer/Q2 Predictions Min -86.5925 +trainer/Q Targets Mean -70.96 +trainer/Q Targets Std 19.7036 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.525 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0328994 +trainer/policy/mean Std 0.718094 +trainer/policy/mean Max 0.998214 +trainer/policy/mean Min -0.998073 +trainer/policy/std Mean 0.412265 +trainer/policy/std Std 0.0203563 +trainer/policy/std Max 0.434491 +trainer/policy/std Min 0.382439 +trainer/Advantage Weights Mean 6.27985 +trainer/Advantage Weights Std 21.1496 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.52325e-12 +trainer/Advantage Score Mean -0.310805 +trainer/Advantage Score Std 0.57666 +trainer/Advantage Score Max 2.42175 +trainer/Advantage Score Min -2.54882 +trainer/V1 Predictions Mean -70.808 +trainer/V1 Predictions Std 19.6619 +trainer/V1 Predictions Max -1.43603 +trainer/V1 Predictions Min -86.5874 +trainer/VF Loss 0.0870113 +expl/num steps total 846000 +expl/num paths total 1163 +expl/path length Mean 500 +expl/path length Std 365 +expl/path length Max 865 +expl/path length Min 135 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0231062 +expl/Actions Std 0.838331 +expl/Actions Max 2.29358 +expl/Actions Min -2.25346 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 773231 +eval/num paths total 858 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.172426 +eval/Actions Std 0.733931 +eval/Actions Max 0.999984 +eval/Actions Min -0.999706 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.76278e-06 +time/evaluation sampling (s) 2.41802 +time/exploration sampling (s) 3.04685 +time/logging (s) 0.00742636 +time/saving (s) 0.0118983 +time/training (s) 13.3141 +time/epoch (s) 18.7983 +time/total (s) 21642.3 +Epoch -155 +------------------------------ ---------------- +2022-05-16 00:03:32.237033 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -154 finished +------------------------------ ---------------- +epoch -154 +replay_buffer/size 999047 +trainer/num train calls 847000 +trainer/QF1 Loss 0.679788 +trainer/QF2 Loss 0.668541 +trainer/Policy Loss 24.9867 +trainer/Q1 Predictions Mean -70.5739 +trainer/Q1 Predictions Std 19.2683 +trainer/Q1 Predictions Max -0.680643 +trainer/Q1 Predictions Min -87.6415 +trainer/Q2 Predictions Mean -70.5151 +trainer/Q2 Predictions Std 19.1942 +trainer/Q2 Predictions Max -0.61318 +trainer/Q2 Predictions Min -87.0249 +trainer/Q Targets Mean -70.6198 +trainer/Q Targets Std 19.3863 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3526 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00456665 +trainer/policy/mean Std 0.730116 +trainer/policy/mean Max 0.997525 +trainer/policy/mean Min -0.999107 +trainer/policy/std Mean 0.412902 +trainer/policy/std Std 0.0205593 +trainer/policy/std Max 0.435094 +trainer/policy/std Min 0.385331 +trainer/Advantage Weights Mean 6.39938 +trainer/Advantage Weights Std 21.9383 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.96125e-13 +trainer/Advantage Score Mean -0.378247 +trainer/Advantage Score Std 0.614451 +trainer/Advantage Score Max 1.04268 +trainer/Advantage Score Min -2.926 +trainer/V1 Predictions Mean -70.3567 +trainer/V1 Predictions Std 19.4458 +trainer/V1 Predictions Max -0.336022 +trainer/V1 Predictions Min -87.2382 +trainer/VF Loss 0.0764909 +expl/num steps total 847000 +expl/num paths total 1165 +expl/path length Mean 500 +expl/path length Std 98 +expl/path length Max 598 +expl/path length Min 402 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0476416 +expl/Actions Std 0.830643 +expl/Actions Max 2.30937 +expl/Actions Min -2.49149 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 774231 +eval/num paths total 859 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.218017 +eval/Actions Std 0.813019 +eval/Actions Max 0.999313 +eval/Actions Min -0.999544 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.03611e-06 +time/evaluation sampling (s) 2.41863 +time/exploration sampling (s) 2.86471 +time/logging (s) 0.00754591 +time/saving (s) 0.014034 +time/training (s) 13.7192 +time/epoch (s) 19.0241 +time/total (s) 21661.3 +Epoch -154 +------------------------------ ---------------- +2022-05-16 00:03:51.064062 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -153 finished +------------------------------ ---------------- +epoch -153 +replay_buffer/size 999047 +trainer/num train calls 848000 +trainer/QF1 Loss 0.693639 +trainer/QF2 Loss 0.792656 +trainer/Policy Loss 10.9921 +trainer/Q1 Predictions Mean -71.2105 +trainer/Q1 Predictions Std 20.0539 +trainer/Q1 Predictions Max -0.611526 +trainer/Q1 Predictions Min -88.5935 +trainer/Q2 Predictions Mean -71.1472 +trainer/Q2 Predictions Std 20.061 +trainer/Q2 Predictions Max -1.03116 +trainer/Q2 Predictions Min -88.1469 +trainer/Q Targets Mean -70.9521 +trainer/Q Targets Std 19.8548 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4683 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0075038 +trainer/policy/mean Std 0.741776 +trainer/policy/mean Max 0.998038 +trainer/policy/mean Min -0.999382 +trainer/policy/std Mean 0.413587 +trainer/policy/std Std 0.0201314 +trainer/policy/std Max 0.434524 +trainer/policy/std Min 0.384297 +trainer/Advantage Weights Mean 2.87406 +trainer/Advantage Weights Std 15.8459 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.53264e-15 +trainer/Advantage Score Mean -0.602756 +trainer/Advantage Score Std 0.492016 +trainer/Advantage Score Max 1.2121 +trainer/Advantage Score Min -3.32767 +trainer/V1 Predictions Mean -70.7315 +trainer/V1 Predictions Std 19.7776 +trainer/V1 Predictions Max -0.343006 +trainer/V1 Predictions Min -87.5249 +trainer/VF Loss 0.0765551 +expl/num steps total 848000 +expl/num paths total 1166 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0320741 +expl/Actions Std 0.821574 +expl/Actions Max 2.37721 +expl/Actions Min -2.22869 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 774903 +eval/num paths total 860 +eval/path length Mean 672 +eval/path length Std 0 +eval/path length Max 672 +eval/path length Min 672 +eval/Rewards Mean 0.0014881 +eval/Rewards Std 0.0385471 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0132229 +eval/Actions Std 0.746785 +eval/Actions Max 0.999466 +eval/Actions Min -0.999762 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.0119e-06 +time/evaluation sampling (s) 2.39056 +time/exploration sampling (s) 2.94815 +time/logging (s) 0.00706686 +time/saving (s) 0.0128895 +time/training (s) 13.452 +time/epoch (s) 18.8106 +time/total (s) 21680.1 +Epoch -153 +------------------------------ ---------------- +2022-05-16 00:04:09.642562 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -152 finished +------------------------------ ---------------- +epoch -152 +replay_buffer/size 999047 +trainer/num train calls 849000 +trainer/QF1 Loss 0.783813 +trainer/QF2 Loss 0.731272 +trainer/Policy Loss 12.4062 +trainer/Q1 Predictions Mean -70.3855 +trainer/Q1 Predictions Std 18.9404 +trainer/Q1 Predictions Max -1.35391 +trainer/Q1 Predictions Min -86.7372 +trainer/Q2 Predictions Mean -70.4526 +trainer/Q2 Predictions Std 19.0016 +trainer/Q2 Predictions Max -0.353464 +trainer/Q2 Predictions Min -86.8925 +trainer/Q Targets Mean -70.3888 +trainer/Q Targets Std 19.2931 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9878 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0116767 +trainer/policy/mean Std 0.720802 +trainer/policy/mean Max 0.999519 +trainer/policy/mean Min -0.999558 +trainer/policy/std Mean 0.413244 +trainer/policy/std Std 0.0209345 +trainer/policy/std Max 0.435001 +trainer/policy/std Min 0.383646 +trainer/Advantage Weights Mean 2.63203 +trainer/Advantage Weights Std 14.0239 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.31376e-24 +trainer/Advantage Score Mean -0.550636 +trainer/Advantage Score Std 0.820993 +trainer/Advantage Score Max 2.58564 +trainer/Advantage Score Min -5.49891 +trainer/V1 Predictions Mean -70.0985 +trainer/V1 Predictions Std 19.5283 +trainer/V1 Predictions Max 0.23383 +trainer/V1 Predictions Min -86.9233 +trainer/VF Loss 0.124907 +expl/num steps total 849000 +expl/num paths total 1168 +expl/path length Mean 500 +expl/path length Std 411 +expl/path length Max 911 +expl/path length Min 89 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0678079 +expl/Actions Std 0.817778 +expl/Actions Max 2.32459 +expl/Actions Min -2.38045 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 775903 +eval/num paths total 861 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0728129 +eval/Actions Std 0.704335 +eval/Actions Max 0.99953 +eval/Actions Min -0.999585 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.33227e-06 +time/evaluation sampling (s) 2.32817 +time/exploration sampling (s) 2.91968 +time/logging (s) 0.0081406 +time/saving (s) 0.0130955 +time/training (s) 13.2964 +time/epoch (s) 18.5655 +time/total (s) 21698.7 +Epoch -152 +------------------------------ ---------------- +2022-05-16 00:04:28.664355 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -151 finished +------------------------------ ---------------- +epoch -151 +replay_buffer/size 999047 +trainer/num train calls 850000 +trainer/QF1 Loss 0.69569 +trainer/QF2 Loss 0.959299 +trainer/Policy Loss 23.3432 +trainer/Q1 Predictions Mean -71.5501 +trainer/Q1 Predictions Std 20.0216 +trainer/Q1 Predictions Max -0.583422 +trainer/Q1 Predictions Min -87.7588 +trainer/Q2 Predictions Mean -71.6282 +trainer/Q2 Predictions Std 19.8759 +trainer/Q2 Predictions Max 0.191224 +trainer/Q2 Predictions Min -87.6059 +trainer/Q Targets Mean -71.1521 +trainer/Q Targets Std 20.0213 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1579 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0389404 +trainer/policy/mean Std 0.74018 +trainer/policy/mean Max 0.999891 +trainer/policy/mean Min -0.999415 +trainer/policy/std Mean 0.41299 +trainer/policy/std Std 0.0202217 +trainer/policy/std Max 0.433981 +trainer/policy/std Min 0.385604 +trainer/Advantage Weights Mean 4.62519 +trainer/Advantage Weights Std 18.6532 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.8134e-19 +trainer/Advantage Score Mean -0.502559 +trainer/Advantage Score Std 0.794495 +trainer/Advantage Score Max 2.07929 +trainer/Advantage Score Min -4.31539 +trainer/V1 Predictions Mean -70.8768 +trainer/V1 Predictions Std 20.1627 +trainer/V1 Predictions Max 0.179975 +trainer/V1 Predictions Min -86.8625 +trainer/VF Loss 0.125968 +expl/num steps total 850000 +expl/num paths total 1169 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.23273 +expl/Actions Std 0.827924 +expl/Actions Max 2.42494 +expl/Actions Min -2.23695 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 776903 +eval/num paths total 862 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0914338 +eval/Actions Std 0.704144 +eval/Actions Max 0.999035 +eval/Actions Min -0.998266 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66312e-06 +time/evaluation sampling (s) 2.45901 +time/exploration sampling (s) 2.93262 +time/logging (s) 0.0113872 +time/saving (s) 0.0197179 +time/training (s) 13.5869 +time/epoch (s) 19.0097 +time/total (s) 21717.7 +Epoch -151 +------------------------------ ---------------- +2022-05-16 00:04:47.695883 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -150 finished +------------------------------ ---------------- +epoch -150 +replay_buffer/size 999047 +trainer/num train calls 851000 +trainer/QF1 Loss 0.878404 +trainer/QF2 Loss 0.803702 +trainer/Policy Loss 16.2736 +trainer/Q1 Predictions Mean -73.3573 +trainer/Q1 Predictions Std 15.8419 +trainer/Q1 Predictions Max -3.68296 +trainer/Q1 Predictions Min -86.5352 +trainer/Q2 Predictions Mean -73.3576 +trainer/Q2 Predictions Std 15.8417 +trainer/Q2 Predictions Max -5.77965 +trainer/Q2 Predictions Min -86.4322 +trainer/Q Targets Mean -73.3257 +trainer/Q Targets Std 15.4036 +trainer/Q Targets Max -5.84012 +trainer/Q Targets Min -86.3602 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00436486 +trainer/policy/mean Std 0.713421 +trainer/policy/mean Max 0.999869 +trainer/policy/mean Min -0.998944 +trainer/policy/std Mean 0.411718 +trainer/policy/std Std 0.0205026 +trainer/policy/std Max 0.433312 +trainer/policy/std Min 0.384056 +trainer/Advantage Weights Mean 3.5362 +trainer/Advantage Weights Std 15.9383 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.22006e-18 +trainer/Advantage Score Mean -0.548236 +trainer/Advantage Score Std 0.647153 +trainer/Advantage Score Max 1.35828 +trainer/Advantage Score Min -4.12476 +trainer/V1 Predictions Mean -72.9798 +trainer/V1 Predictions Std 15.6895 +trainer/V1 Predictions Max -5.10463 +trainer/V1 Predictions Min -86.2839 +trainer/VF Loss 0.0913246 +expl/num steps total 851000 +expl/num paths total 1171 +expl/path length Mean 500 +expl/path length Std 382 +expl/path length Max 882 +expl/path length Min 118 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0136594 +expl/Actions Std 0.819093 +expl/Actions Max 2.48938 +expl/Actions Min -2.41898 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 777382 +eval/num paths total 863 +eval/path length Mean 479 +eval/path length Std 0 +eval/path length Max 479 +eval/path length Min 479 +eval/Rewards Mean 0.00208768 +eval/Rewards Std 0.0456434 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0276804 +eval/Actions Std 0.733882 +eval/Actions Max 0.999252 +eval/Actions Min -0.999492 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.68873e-06 +time/evaluation sampling (s) 2.32813 +time/exploration sampling (s) 3.09245 +time/logging (s) 0.00541337 +time/saving (s) 0.023654 +time/training (s) 13.5568 +time/epoch (s) 19.0064 +time/total (s) 21736.7 +Epoch -150 +------------------------------ ---------------- +2022-05-16 00:05:06.642459 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -149 finished +------------------------------ ---------------- +epoch -149 +replay_buffer/size 999047 +trainer/num train calls 852000 +trainer/QF1 Loss 1.30673 +trainer/QF2 Loss 1.3534 +trainer/Policy Loss 8.97671 +trainer/Q1 Predictions Mean -73.3317 +trainer/Q1 Predictions Std 16.5249 +trainer/Q1 Predictions Max -3.62356 +trainer/Q1 Predictions Min -88.0696 +trainer/Q2 Predictions Mean -73.3834 +trainer/Q2 Predictions Std 16.5234 +trainer/Q2 Predictions Max -3.59051 +trainer/Q2 Predictions Min -88.0286 +trainer/Q Targets Mean -73.2049 +trainer/Q Targets Std 16.7492 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4726 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0171628 +trainer/policy/mean Std 0.723748 +trainer/policy/mean Max 0.999859 +trainer/policy/mean Min -0.999724 +trainer/policy/std Mean 0.411749 +trainer/policy/std Std 0.0199307 +trainer/policy/std Max 0.432884 +trainer/policy/std Min 0.382817 +trainer/Advantage Weights Mean 2.42767 +trainer/Advantage Weights Std 12.9367 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.22076e-24 +trainer/Advantage Score Mean -0.450417 +trainer/Advantage Score Std 0.64946 +trainer/Advantage Score Max 0.839853 +trainer/Advantage Score Min -5.40924 +trainer/V1 Predictions Mean -73.0233 +trainer/V1 Predictions Std 16.7515 +trainer/V1 Predictions Max -1.89716 +trainer/V1 Predictions Min -88.0559 +trainer/VF Loss 0.0711964 +expl/num steps total 852000 +expl/num paths total 1173 +expl/path length Mean 500 +expl/path length Std 208 +expl/path length Max 708 +expl/path length Min 292 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0280817 +expl/Actions Std 0.820017 +expl/Actions Max 2.33106 +expl/Actions Min -2.32497 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 778382 +eval/num paths total 864 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0402845 +eval/Actions Std 0.691312 +eval/Actions Max 0.999858 +eval/Actions Min -0.999626 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.82075e-06 +time/evaluation sampling (s) 2.38292 +time/exploration sampling (s) 2.92131 +time/logging (s) 0.00895704 +time/saving (s) 0.0120771 +time/training (s) 13.6141 +time/epoch (s) 18.9394 +time/total (s) 21755.7 +Epoch -149 +------------------------------ ---------------- +2022-05-16 00:05:25.965280 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -148 finished +------------------------------ ---------------- +epoch -148 +replay_buffer/size 999047 +trainer/num train calls 853000 +trainer/QF1 Loss 0.720056 +trainer/QF2 Loss 0.770291 +trainer/Policy Loss 4.32708 +trainer/Q1 Predictions Mean -73.1594 +trainer/Q1 Predictions Std 17.1625 +trainer/Q1 Predictions Max -1.37632 +trainer/Q1 Predictions Min -90.353 +trainer/Q2 Predictions Mean -73.1972 +trainer/Q2 Predictions Std 17.2783 +trainer/Q2 Predictions Max -1.86325 +trainer/Q2 Predictions Min -90.6773 +trainer/Q Targets Mean -72.7756 +trainer/Q Targets Std 17.2205 +trainer/Q Targets Max -1.21255 +trainer/Q Targets Min -90.0876 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000238642 +trainer/policy/mean Std 0.729941 +trainer/policy/mean Max 0.999661 +trainer/policy/mean Min -0.998577 +trainer/policy/std Mean 0.410988 +trainer/policy/std Std 0.020222 +trainer/policy/std Max 0.435279 +trainer/policy/std Min 0.38159 +trainer/Advantage Weights Mean 1.20158 +trainer/Advantage Weights Std 8.04085 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.46243e-17 +trainer/Advantage Score Mean -0.612696 +trainer/Advantage Score Std 0.591461 +trainer/Advantage Score Max 0.644089 +trainer/Advantage Score Min -3.74461 +trainer/V1 Predictions Mean -72.4795 +trainer/V1 Predictions Std 17.392 +trainer/V1 Predictions Max -0.643605 +trainer/V1 Predictions Min -89.6737 +trainer/VF Loss 0.0760198 +expl/num steps total 853000 +expl/num paths total 1174 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0813708 +expl/Actions Std 0.862906 +expl/Actions Max 2.30286 +expl/Actions Min -2.37652 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 779382 +eval/num paths total 865 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.454699 +eval/Actions Std 0.524409 +eval/Actions Max 0.999752 +eval/Actions Min -0.99986 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99467e-06 +time/evaluation sampling (s) 2.59485 +time/exploration sampling (s) 3.02727 +time/logging (s) 0.0122649 +time/saving (s) 0.0163814 +time/training (s) 13.66 +time/epoch (s) 19.3108 +time/total (s) 21775 +Epoch -148 +------------------------------ ---------------- +2022-05-16 00:05:45.668197 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -147 finished +------------------------------ ---------------- +epoch -147 +replay_buffer/size 999047 +trainer/num train calls 854000 +trainer/QF1 Loss 1.01339 +trainer/QF2 Loss 0.817707 +trainer/Policy Loss 31.6023 +trainer/Q1 Predictions Mean -71.9158 +trainer/Q1 Predictions Std 18.8033 +trainer/Q1 Predictions Max -2.08287 +trainer/Q1 Predictions Min -87.4152 +trainer/Q2 Predictions Mean -71.9493 +trainer/Q2 Predictions Std 18.7278 +trainer/Q2 Predictions Max -2.11221 +trainer/Q2 Predictions Min -87.2657 +trainer/Q Targets Mean -72.1142 +trainer/Q Targets Std 18.4411 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2956 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.01438 +trainer/policy/mean Std 0.725268 +trainer/policy/mean Max 0.999697 +trainer/policy/mean Min -0.998944 +trainer/policy/std Mean 0.412436 +trainer/policy/std Std 0.0203573 +trainer/policy/std Max 0.435072 +trainer/policy/std Min 0.381003 +trainer/Advantage Weights Mean 6.83056 +trainer/Advantage Weights Std 23.4377 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.38945e-14 +trainer/Advantage Score Mean -0.27279 +trainer/Advantage Score Std 0.552985 +trainer/Advantage Score Max 2.33666 +trainer/Advantage Score Min -3.0757 +trainer/V1 Predictions Mean -71.9631 +trainer/V1 Predictions Std 18.3401 +trainer/V1 Predictions Max -2.82675 +trainer/V1 Predictions Min -87.1667 +trainer/VF Loss 0.0994681 +expl/num steps total 854000 +expl/num paths total 1175 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0148408 +expl/Actions Std 0.834415 +expl/Actions Max 2.20157 +expl/Actions Min -2.20389 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 780382 +eval/num paths total 866 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0241331 +eval/Actions Std 0.735804 +eval/Actions Max 0.999942 +eval/Actions Min -0.999638 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.91318e-06 +time/evaluation sampling (s) 2.46816 +time/exploration sampling (s) 3.24097 +time/logging (s) 0.00778615 +time/saving (s) 0.012609 +time/training (s) 13.9498 +time/epoch (s) 19.6793 +time/total (s) 21794.7 +Epoch -147 +------------------------------ ---------------- +2022-05-16 00:06:04.820023 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -146 finished +------------------------------ ---------------- +epoch -146 +replay_buffer/size 999047 +trainer/num train calls 855000 +trainer/QF1 Loss 0.661593 +trainer/QF2 Loss 0.954862 +trainer/Policy Loss 43.34 +trainer/Q1 Predictions Mean -73.4779 +trainer/Q1 Predictions Std 15.8262 +trainer/Q1 Predictions Max -4.60651 +trainer/Q1 Predictions Min -87.5449 +trainer/Q2 Predictions Mean -73.4448 +trainer/Q2 Predictions Std 15.8682 +trainer/Q2 Predictions Max -6.00652 +trainer/Q2 Predictions Min -87.6767 +trainer/Q Targets Mean -73.8596 +trainer/Q Targets Std 15.6662 +trainer/Q Targets Max -5.87849 +trainer/Q Targets Min -87.4757 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0109038 +trainer/policy/mean Std 0.744783 +trainer/policy/mean Max 0.999597 +trainer/policy/mean Min -0.999385 +trainer/policy/std Mean 0.411906 +trainer/policy/std Std 0.0217797 +trainer/policy/std Max 0.434576 +trainer/policy/std Min 0.379738 +trainer/Advantage Weights Mean 6.2298 +trainer/Advantage Weights Std 20.4537 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.46137e-20 +trainer/Advantage Score Mean -0.265554 +trainer/Advantage Score Std 0.604809 +trainer/Advantage Score Max 1.89517 +trainer/Advantage Score Min -4.5151 +trainer/V1 Predictions Mean -73.5404 +trainer/V1 Predictions Std 15.7859 +trainer/V1 Predictions Max -5.81297 +trainer/V1 Predictions Min -87.2727 +trainer/VF Loss 0.0874025 +expl/num steps total 855000 +expl/num paths total 1177 +expl/path length Mean 500 +expl/path length Std 114 +expl/path length Max 614 +expl/path length Min 386 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0410311 +expl/Actions Std 0.839601 +expl/Actions Max 2.73184 +expl/Actions Min -2.49818 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 781150 +eval/num paths total 867 +eval/path length Mean 768 +eval/path length Std 0 +eval/path length Max 768 +eval/path length Min 768 +eval/Rewards Mean 0.00130208 +eval/Rewards Std 0.0360609 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0458798 +eval/Actions Std 0.731614 +eval/Actions Max 0.999945 +eval/Actions Min -0.99992 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.94577e-06 +time/evaluation sampling (s) 2.57357 +time/exploration sampling (s) 2.9459 +time/logging (s) 0.00854849 +time/saving (s) 0.0127607 +time/training (s) 13.5989 +time/epoch (s) 19.1397 +time/total (s) 21813.8 +Epoch -146 +------------------------------ ---------------- +2022-05-16 00:06:23.816470 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -145 finished +------------------------------ ---------------- +epoch -145 +replay_buffer/size 999047 +trainer/num train calls 856000 +trainer/QF1 Loss 0.727074 +trainer/QF2 Loss 0.702409 +trainer/Policy Loss 24.9647 +trainer/Q1 Predictions Mean -73.6097 +trainer/Q1 Predictions Std 16.8987 +trainer/Q1 Predictions Max -0.322179 +trainer/Q1 Predictions Min -87.044 +trainer/Q2 Predictions Mean -73.5878 +trainer/Q2 Predictions Std 16.8564 +trainer/Q2 Predictions Max -0.387202 +trainer/Q2 Predictions Min -87.3088 +trainer/Q Targets Mean -73.5082 +trainer/Q Targets Std 16.9677 +trainer/Q Targets Max -1.78326 +trainer/Q Targets Min -86.9535 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0152113 +trainer/policy/mean Std 0.745834 +trainer/policy/mean Max 0.999295 +trainer/policy/mean Min -0.999945 +trainer/policy/std Mean 0.411231 +trainer/policy/std Std 0.0212059 +trainer/policy/std Max 0.430982 +trainer/policy/std Min 0.37809 +trainer/Advantage Weights Mean 3.66509 +trainer/Advantage Weights Std 15.5207 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.31509e-20 +trainer/Advantage Score Mean -0.441986 +trainer/Advantage Score Std 0.701081 +trainer/Advantage Score Max 1.21038 +trainer/Advantage Score Min -4.57778 +trainer/V1 Predictions Mean -73.2183 +trainer/V1 Predictions Std 17.1232 +trainer/V1 Predictions Max -0.298309 +trainer/V1 Predictions Min -86.9276 +trainer/VF Loss 0.0835816 +expl/num steps total 856000 +expl/num paths total 1179 +expl/path length Mean 500 +expl/path length Std 365 +expl/path length Max 865 +expl/path length Min 135 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0227831 +expl/Actions Std 0.82282 +expl/Actions Max 2.31542 +expl/Actions Min -2.80601 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 782150 +eval/num paths total 868 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0343065 +eval/Actions Std 0.724221 +eval/Actions Max 0.999818 +eval/Actions Min -0.99982 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.17302e-06 +time/evaluation sampling (s) 2.61875 +time/exploration sampling (s) 2.93384 +time/logging (s) 0.0120003 +time/saving (s) 0.0174332 +time/training (s) 13.4052 +time/epoch (s) 18.9873 +time/total (s) 21832.8 +Epoch -145 +------------------------------ ---------------- +2022-05-16 00:06:43.117783 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -144 finished +------------------------------ ---------------- +epoch -144 +replay_buffer/size 999047 +trainer/num train calls 857000 +trainer/QF1 Loss 0.832688 +trainer/QF2 Loss 0.903805 +trainer/Policy Loss 10.9657 +trainer/Q1 Predictions Mean -72.6164 +trainer/Q1 Predictions Std 17.4326 +trainer/Q1 Predictions Max -0.676921 +trainer/Q1 Predictions Min -87.431 +trainer/Q2 Predictions Mean -72.486 +trainer/Q2 Predictions Std 17.4876 +trainer/Q2 Predictions Max 0.378121 +trainer/Q2 Predictions Min -87.3434 +trainer/Q Targets Mean -72.507 +trainer/Q Targets Std 17.5406 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7899 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00642602 +trainer/policy/mean Std 0.738431 +trainer/policy/mean Max 0.999537 +trainer/policy/mean Min -0.998977 +trainer/policy/std Mean 0.411234 +trainer/policy/std Std 0.0212272 +trainer/policy/std Max 0.430741 +trainer/policy/std Min 0.380434 +trainer/Advantage Weights Mean 2.85746 +trainer/Advantage Weights Std 15.1021 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.26383e-18 +trainer/Advantage Score Mean -0.516351 +trainer/Advantage Score Std 0.63201 +trainer/Advantage Score Max 0.879906 +trainer/Advantage Score Min -3.96117 +trainer/V1 Predictions Mean -72.2542 +trainer/V1 Predictions Std 17.5394 +trainer/V1 Predictions Max -0.894136 +trainer/V1 Predictions Min -87.8576 +trainer/VF Loss 0.0786357 +expl/num steps total 857000 +expl/num paths total 1181 +expl/path length Mean 500 +expl/path length Std 410 +expl/path length Max 910 +expl/path length Min 90 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00827895 +expl/Actions Std 0.815972 +expl/Actions Max 2.24691 +expl/Actions Min -2.44066 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 783150 +eval/num paths total 869 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0550149 +eval/Actions Std 0.830691 +eval/Actions Max 0.998397 +eval/Actions Min -0.998459 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.82517e-06 +time/evaluation sampling (s) 2.68434 +time/exploration sampling (s) 2.89902 +time/logging (s) 0.0102465 +time/saving (s) 0.0149224 +time/training (s) 13.6689 +time/epoch (s) 19.2775 +time/total (s) 21852.1 +Epoch -144 +------------------------------ ---------------- +2022-05-16 00:07:02.833244 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -143 finished +------------------------------ ---------------- +epoch -143 +replay_buffer/size 999047 +trainer/num train calls 858000 +trainer/QF1 Loss 0.50738 +trainer/QF2 Loss 0.526709 +trainer/Policy Loss 18.966 +trainer/Q1 Predictions Mean -72.5341 +trainer/Q1 Predictions Std 18.455 +trainer/Q1 Predictions Max 0.105049 +trainer/Q1 Predictions Min -87.0041 +trainer/Q2 Predictions Mean -72.4504 +trainer/Q2 Predictions Std 18.3719 +trainer/Q2 Predictions Max 0.551689 +trainer/Q2 Predictions Min -87.192 +trainer/Q Targets Mean -72.5037 +trainer/Q Targets Std 18.5052 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8487 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0128056 +trainer/policy/mean Std 0.727074 +trainer/policy/mean Max 0.999691 +trainer/policy/mean Min -0.998461 +trainer/policy/std Mean 0.410032 +trainer/policy/std Std 0.0209567 +trainer/policy/std Max 0.430776 +trainer/policy/std Min 0.380642 +trainer/Advantage Weights Mean 4.94098 +trainer/Advantage Weights Std 17.5864 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.67864e-24 +trainer/Advantage Score Mean -0.312188 +trainer/Advantage Score Std 0.653446 +trainer/Advantage Score Max 1.79049 +trainer/Advantage Score Min -5.42767 +trainer/V1 Predictions Mean -72.189 +trainer/V1 Predictions Std 18.6503 +trainer/V1 Predictions Max 0.531795 +trainer/V1 Predictions Min -86.828 +trainer/VF Loss 0.0781017 +expl/num steps total 858000 +expl/num paths total 1182 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0320978 +expl/Actions Std 0.858056 +expl/Actions Max 2.27165 +expl/Actions Min -2.47736 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 784150 +eval/num paths total 870 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0449525 +eval/Actions Std 0.771405 +eval/Actions Max 0.999049 +eval/Actions Min -0.999555 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.22424e-06 +time/evaluation sampling (s) 2.65387 +time/exploration sampling (s) 2.96464 +time/logging (s) 0.0100194 +time/saving (s) 0.0107089 +time/training (s) 14.0583 +time/epoch (s) 19.6976 +time/total (s) 21871.8 +Epoch -143 +------------------------------ ---------------- +2022-05-16 00:07:22.090126 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -142 finished +------------------------------ ---------------- +epoch -142 +replay_buffer/size 999047 +trainer/num train calls 859000 +trainer/QF1 Loss 1.01057 +trainer/QF2 Loss 1.1559 +trainer/Policy Loss 41.1312 +trainer/Q1 Predictions Mean -68.4901 +trainer/Q1 Predictions Std 23.0668 +trainer/Q1 Predictions Max -0.169681 +trainer/Q1 Predictions Min -91.691 +trainer/Q2 Predictions Mean -68.5979 +trainer/Q2 Predictions Std 23.089 +trainer/Q2 Predictions Max -0.126676 +trainer/Q2 Predictions Min -91.6128 +trainer/Q Targets Mean -68.488 +trainer/Q Targets Std 23.1698 +trainer/Q Targets Max 0 +trainer/Q Targets Min -91.348 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00828082 +trainer/policy/mean Std 0.727639 +trainer/policy/mean Max 0.999601 +trainer/policy/mean Min -0.998572 +trainer/policy/std Mean 0.409956 +trainer/policy/std Std 0.0202078 +trainer/policy/std Max 0.431872 +trainer/policy/std Min 0.379928 +trainer/Advantage Weights Mean 8.51684 +trainer/Advantage Weights Std 24.8557 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.14997e-12 +trainer/Advantage Score Mean -0.250975 +trainer/Advantage Score Std 0.582817 +trainer/Advantage Score Max 1.2859 +trainer/Advantage Score Min -2.54173 +trainer/V1 Predictions Mean -68.2262 +trainer/V1 Predictions Std 23.2981 +trainer/V1 Predictions Max 0.458995 +trainer/V1 Predictions Min -91.1971 +trainer/VF Loss 0.0799451 +expl/num steps total 859000 +expl/num paths total 1184 +expl/path length Mean 500 +expl/path length Std 311 +expl/path length Max 811 +expl/path length Min 189 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00183213 +expl/Actions Std 0.831397 +expl/Actions Max 2.26519 +expl/Actions Min -2.24073 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 784784 +eval/num paths total 871 +eval/path length Mean 634 +eval/path length Std 0 +eval/path length Max 634 +eval/path length Min 634 +eval/Rewards Mean 0.00157729 +eval/Rewards Std 0.0396837 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.014511 +eval/Actions Std 0.751188 +eval/Actions Max 0.999618 +eval/Actions Min -0.999681 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 8.70507e-06 +time/evaluation sampling (s) 2.53402 +time/exploration sampling (s) 2.87995 +time/logging (s) 0.00781939 +time/saving (s) 0.0135812 +time/training (s) 13.806 +time/epoch (s) 19.2413 +time/total (s) 21891 +Epoch -142 +------------------------------ ---------------- +2022-05-16 00:07:41.051298 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -141 finished +------------------------------ ---------------- +epoch -141 +replay_buffer/size 999047 +trainer/num train calls 860000 +trainer/QF1 Loss 0.785971 +trainer/QF2 Loss 0.649399 +trainer/Policy Loss 7.91158 +trainer/Q1 Predictions Mean -70.9011 +trainer/Q1 Predictions Std 20.6907 +trainer/Q1 Predictions Max -1.13878 +trainer/Q1 Predictions Min -87.176 +trainer/Q2 Predictions Mean -70.8159 +trainer/Q2 Predictions Std 20.7783 +trainer/Q2 Predictions Max 0.680892 +trainer/Q2 Predictions Min -86.8012 +trainer/Q Targets Mean -70.6963 +trainer/Q Targets Std 20.6173 +trainer/Q Targets Max -0.825829 +trainer/Q Targets Min -87.2113 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00899923 +trainer/policy/mean Std 0.730742 +trainer/policy/mean Max 0.998856 +trainer/policy/mean Min -0.999836 +trainer/policy/std Mean 0.410104 +trainer/policy/std Std 0.0198657 +trainer/policy/std Max 0.431366 +trainer/policy/std Min 0.379537 +trainer/Advantage Weights Mean 2.11848 +trainer/Advantage Weights Std 12.6219 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.52921e-24 +trainer/Advantage Score Mean -0.57819 +trainer/Advantage Score Std 0.716155 +trainer/Advantage Score Max 1.52139 +trainer/Advantage Score Min -5.31185 +trainer/V1 Predictions Mean -70.3852 +trainer/V1 Predictions Std 20.8097 +trainer/V1 Predictions Max 1.2501 +trainer/V1 Predictions Min -86.9882 +trainer/VF Loss 0.1032 +expl/num steps total 860000 +expl/num paths total 1186 +expl/path length Mean 500 +expl/path length Std 290 +expl/path length Max 790 +expl/path length Min 210 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0127396 +expl/Actions Std 0.840177 +expl/Actions Max 2.17839 +expl/Actions Min -2.38127 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 785784 +eval/num paths total 872 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.103592 +eval/Actions Std 0.840012 +eval/Actions Max 0.999828 +eval/Actions Min -0.999926 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95369e-06 +time/evaluation sampling (s) 2.59048 +time/exploration sampling (s) 2.75414 +time/logging (s) 0.0117548 +time/saving (s) 0.0173028 +time/training (s) 13.5793 +time/epoch (s) 18.953 +time/total (s) 21910 +Epoch -141 +------------------------------ ---------------- +2022-05-16 00:08:00.141172 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -140 finished +------------------------------ ---------------- +epoch -140 +replay_buffer/size 999047 +trainer/num train calls 861000 +trainer/QF1 Loss 1.64452 +trainer/QF2 Loss 1.65963 +trainer/Policy Loss 6.31305 +trainer/Q1 Predictions Mean -71.6731 +trainer/Q1 Predictions Std 18.0414 +trainer/Q1 Predictions Max -3.17934 +trainer/Q1 Predictions Min -86.6219 +trainer/Q2 Predictions Mean -71.625 +trainer/Q2 Predictions Std 18.0565 +trainer/Q2 Predictions Max -2.28195 +trainer/Q2 Predictions Min -86.544 +trainer/Q Targets Mean -71.6314 +trainer/Q Targets Std 18.4971 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4893 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00558871 +trainer/policy/mean Std 0.733256 +trainer/policy/mean Max 0.999089 +trainer/policy/mean Min -0.999279 +trainer/policy/std Mean 0.412604 +trainer/policy/std Std 0.0196724 +trainer/policy/std Max 0.434695 +trainer/policy/std Min 0.384595 +trainer/Advantage Weights Mean 2.80874 +trainer/Advantage Weights Std 14.0098 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.52801e-18 +trainer/Advantage Score Mean -0.561214 +trainer/Advantage Score Std 0.747504 +trainer/Advantage Score Max 1.13876 +trainer/Advantage Score Min -4.10226 +trainer/V1 Predictions Mean -71.4382 +trainer/V1 Predictions Std 18.5712 +trainer/V1 Predictions Max -0.396652 +trainer/V1 Predictions Min -86.3476 +trainer/VF Loss 0.100691 +expl/num steps total 861000 +expl/num paths total 1187 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0662034 +expl/Actions Std 0.839763 +expl/Actions Max 2.24944 +expl/Actions Min -2.22771 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 786784 +eval/num paths total 873 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0537487 +eval/Actions Std 0.735004 +eval/Actions Max 0.999939 +eval/Actions Min -0.999477 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.92371e-06 +time/evaluation sampling (s) 2.67237 +time/exploration sampling (s) 2.83057 +time/logging (s) 0.0104137 +time/saving (s) 0.0131834 +time/training (s) 13.5398 +time/epoch (s) 19.0664 +time/total (s) 21929.1 +Epoch -140 +------------------------------ ---------------- +2022-05-16 00:08:19.123581 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -139 finished +------------------------------ ---------------- +epoch -139 +replay_buffer/size 999047 +trainer/num train calls 862000 +trainer/QF1 Loss 0.603252 +trainer/QF2 Loss 0.646655 +trainer/Policy Loss 22.0139 +trainer/Q1 Predictions Mean -71.0722 +trainer/Q1 Predictions Std 19.2271 +trainer/Q1 Predictions Max -0.251522 +trainer/Q1 Predictions Min -89.1466 +trainer/Q2 Predictions Mean -71.1218 +trainer/Q2 Predictions Std 19.2478 +trainer/Q2 Predictions Max -0.883732 +trainer/Q2 Predictions Min -89.4561 +trainer/Q Targets Mean -71.0372 +trainer/Q Targets Std 19.2309 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5007 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0227838 +trainer/policy/mean Std 0.732336 +trainer/policy/mean Max 0.999787 +trainer/policy/mean Min -0.998821 +trainer/policy/std Mean 0.410903 +trainer/policy/std Std 0.0197118 +trainer/policy/std Max 0.43448 +trainer/policy/std Min 0.384393 +trainer/Advantage Weights Mean 5.09978 +trainer/Advantage Weights Std 19.8409 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.5806e-38 +trainer/Advantage Score Mean -0.48095 +trainer/Advantage Score Std 0.841339 +trainer/Advantage Score Max 1.47589 +trainer/Advantage Score Min -8.54726 +trainer/V1 Predictions Mean -70.7626 +trainer/V1 Predictions Std 19.3366 +trainer/V1 Predictions Max 0.930365 +trainer/V1 Predictions Min -88.4292 +trainer/VF Loss 0.124806 +expl/num steps total 862000 +expl/num paths total 1189 +expl/path length Mean 500 +expl/path length Std 73 +expl/path length Max 573 +expl/path length Min 427 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0226198 +expl/Actions Std 0.847052 +expl/Actions Max 2.40837 +expl/Actions Min -2.23824 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 787265 +eval/num paths total 874 +eval/path length Mean 481 +eval/path length Std 0 +eval/path length Max 481 +eval/path length Min 481 +eval/Rewards Mean 0.002079 +eval/Rewards Std 0.0455487 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00830093 +eval/Actions Std 0.73613 +eval/Actions Max 0.999484 +eval/Actions Min -0.999636 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.69804e-06 +time/evaluation sampling (s) 2.50689 +time/exploration sampling (s) 2.76176 +time/logging (s) 0.00846257 +time/saving (s) 0.0138589 +time/training (s) 13.6697 +time/epoch (s) 18.9607 +time/total (s) 21948 +Epoch -139 +------------------------------ ---------------- +2022-05-16 00:08:38.220095 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -138 finished +------------------------------ ---------------- +epoch -138 +replay_buffer/size 999047 +trainer/num train calls 863000 +trainer/QF1 Loss 0.700878 +trainer/QF2 Loss 0.708601 +trainer/Policy Loss 18.7761 +trainer/Q1 Predictions Mean -72.8816 +trainer/Q1 Predictions Std 18.4153 +trainer/Q1 Predictions Max -1.78752 +trainer/Q1 Predictions Min -87.2703 +trainer/Q2 Predictions Mean -72.9003 +trainer/Q2 Predictions Std 18.4282 +trainer/Q2 Predictions Max -1.44862 +trainer/Q2 Predictions Min -87.2359 +trainer/Q Targets Mean -72.7926 +trainer/Q Targets Std 18.2722 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9848 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0278983 +trainer/policy/mean Std 0.729839 +trainer/policy/mean Max 0.999667 +trainer/policy/mean Min -0.999828 +trainer/policy/std Mean 0.411923 +trainer/policy/std Std 0.019537 +trainer/policy/std Max 0.434335 +trainer/policy/std Min 0.383267 +trainer/Advantage Weights Mean 5.82377 +trainer/Advantage Weights Std 20.5382 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.09656e-14 +trainer/Advantage Score Mean -0.318053 +trainer/Advantage Score Std 0.581051 +trainer/Advantage Score Max 1.97245 +trainer/Advantage Score Min -3.14959 +trainer/V1 Predictions Mean -72.5426 +trainer/V1 Predictions Std 18.4438 +trainer/V1 Predictions Max -1.32858 +trainer/V1 Predictions Min -86.7452 +trainer/VF Loss 0.0766778 +expl/num steps total 863000 +expl/num paths total 1191 +expl/path length Mean 500 +expl/path length Std 493 +expl/path length Max 993 +expl/path length Min 7 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0329628 +expl/Actions Std 0.823252 +expl/Actions Max 2.69261 +expl/Actions Min -2.08596 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 788265 +eval/num paths total 875 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.132642 +eval/Actions Std 0.768803 +eval/Actions Max 0.999658 +eval/Actions Min -0.999933 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.78e-06 +time/evaluation sampling (s) 2.56184 +time/exploration sampling (s) 2.87039 +time/logging (s) 0.00805453 +time/saving (s) 0.0173616 +time/training (s) 13.6257 +time/epoch (s) 19.0833 +time/total (s) 21967.1 +Epoch -138 +------------------------------ ---------------- +2022-05-16 00:08:57.238424 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -137 finished +------------------------------ ---------------- +epoch -137 +replay_buffer/size 999047 +trainer/num train calls 864000 +trainer/QF1 Loss 1.0405 +trainer/QF2 Loss 0.867166 +trainer/Policy Loss 22.563 +trainer/Q1 Predictions Mean -69.7372 +trainer/Q1 Predictions Std 20.3827 +trainer/Q1 Predictions Max -1.3177 +trainer/Q1 Predictions Min -86.0332 +trainer/Q2 Predictions Mean -69.8427 +trainer/Q2 Predictions Std 20.3905 +trainer/Q2 Predictions Max -1.73692 +trainer/Q2 Predictions Min -86.1475 +trainer/Q Targets Mean -70.1275 +trainer/Q Targets Std 20.3333 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4457 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0210035 +trainer/policy/mean Std 0.730016 +trainer/policy/mean Max 0.999848 +trainer/policy/mean Min -0.999085 +trainer/policy/std Mean 0.411751 +trainer/policy/std Std 0.02155 +trainer/policy/std Max 0.4366 +trainer/policy/std Min 0.379407 +trainer/Advantage Weights Mean 5.7584 +trainer/Advantage Weights Std 20.8094 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.51683e-16 +trainer/Advantage Score Mean -0.475126 +trainer/Advantage Score Std 0.770435 +trainer/Advantage Score Max 1.74462 +trainer/Advantage Score Min -3.55838 +trainer/V1 Predictions Mean -69.752 +trainer/V1 Predictions Std 20.6158 +trainer/V1 Predictions Max -1.45483 +trainer/V1 Predictions Min -86.3054 +trainer/VF Loss 0.118099 +expl/num steps total 864000 +expl/num paths total 1193 +expl/path length Mean 500 +expl/path length Std 398 +expl/path length Max 898 +expl/path length Min 102 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0309118 +expl/Actions Std 0.831814 +expl/Actions Max 2.39703 +expl/Actions Min -2.39847 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 789182 +eval/num paths total 876 +eval/path length Mean 917 +eval/path length Std 0 +eval/path length Max 917 +eval/path length Min 917 +eval/Rewards Mean 0.00109051 +eval/Rewards Std 0.0330049 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0152125 +eval/Actions Std 0.721658 +eval/Actions Max 0.999892 +eval/Actions Min -0.999957 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.8694e-06 +time/evaluation sampling (s) 2.54773 +time/exploration sampling (s) 2.83583 +time/logging (s) 0.00934144 +time/saving (s) 0.0152837 +time/training (s) 13.5976 +time/epoch (s) 19.0058 +time/total (s) 21986.1 +Epoch -137 +------------------------------ ---------------- +2022-05-16 00:09:16.516059 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -136 finished +------------------------------ ---------------- +epoch -136 +replay_buffer/size 999047 +trainer/num train calls 865000 +trainer/QF1 Loss 1.00781 +trainer/QF2 Loss 0.973673 +trainer/Policy Loss 19.2081 +trainer/Q1 Predictions Mean -71.2875 +trainer/Q1 Predictions Std 18.5571 +trainer/Q1 Predictions Max -1.23802 +trainer/Q1 Predictions Min -86.368 +trainer/Q2 Predictions Mean -71.2951 +trainer/Q2 Predictions Std 18.6491 +trainer/Q2 Predictions Max -0.225871 +trainer/Q2 Predictions Min -86.4345 +trainer/Q Targets Mean -71.461 +trainer/Q Targets Std 18.6447 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3564 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0180111 +trainer/policy/mean Std 0.736053 +trainer/policy/mean Max 0.999747 +trainer/policy/mean Min -0.999665 +trainer/policy/std Mean 0.409551 +trainer/policy/std Std 0.0215514 +trainer/policy/std Max 0.433839 +trainer/policy/std Min 0.375856 +trainer/Advantage Weights Mean 3.59052 +trainer/Advantage Weights Std 16.3912 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.045e-15 +trainer/Advantage Score Mean -0.385807 +trainer/Advantage Score Std 0.613657 +trainer/Advantage Score Max 1.78818 +trainer/Advantage Score Min -3.29204 +trainer/V1 Predictions Mean -71.2071 +trainer/V1 Predictions Std 18.7822 +trainer/V1 Predictions Max -0.548482 +trainer/V1 Predictions Min -86.2621 +trainer/VF Loss 0.0833714 +expl/num steps total 865000 +expl/num paths total 1195 +expl/path length Mean 500 +expl/path length Std 286 +expl/path length Max 786 +expl/path length Min 214 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0501864 +expl/Actions Std 0.826316 +expl/Actions Max 2.32648 +expl/Actions Min -2.40784 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 789663 +eval/num paths total 877 +eval/path length Mean 481 +eval/path length Std 0 +eval/path length Max 481 +eval/path length Min 481 +eval/Rewards Mean 0.002079 +eval/Rewards Std 0.0455487 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0151093 +eval/Actions Std 0.74295 +eval/Actions Max 0.999635 +eval/Actions Min -0.999514 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.26196e-06 +time/evaluation sampling (s) 2.58409 +time/exploration sampling (s) 2.90279 +time/logging (s) 0.00557374 +time/saving (s) 0.0120344 +time/training (s) 13.7514 +time/epoch (s) 19.2559 +time/total (s) 22005.4 +Epoch -136 +------------------------------ ---------------- +2022-05-16 00:09:35.678002 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -135 finished +------------------------------ ---------------- +epoch -135 +replay_buffer/size 999047 +trainer/num train calls 866000 +trainer/QF1 Loss 1.10826 +trainer/QF2 Loss 1.08612 +trainer/Policy Loss 15.6568 +trainer/Q1 Predictions Mean -71.1353 +trainer/Q1 Predictions Std 19.6152 +trainer/Q1 Predictions Max -1.37793 +trainer/Q1 Predictions Min -90.4726 +trainer/Q2 Predictions Mean -71.0968 +trainer/Q2 Predictions Std 19.7464 +trainer/Q2 Predictions Max -0.929033 +trainer/Q2 Predictions Min -91.2602 +trainer/Q Targets Mean -70.8197 +trainer/Q Targets Std 19.3401 +trainer/Q Targets Max -1.72436 +trainer/Q Targets Min -90.7916 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00249856 +trainer/policy/mean Std 0.73356 +trainer/policy/mean Max 0.999651 +trainer/policy/mean Min -0.99947 +trainer/policy/std Mean 0.410322 +trainer/policy/std Std 0.0210496 +trainer/policy/std Max 0.434228 +trainer/policy/std Min 0.378082 +trainer/Advantage Weights Mean 2.82048 +trainer/Advantage Weights Std 15.0315 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.29401e-30 +trainer/Advantage Score Mean -0.542293 +trainer/Advantage Score Std 0.702672 +trainer/Advantage Score Max 1.07921 +trainer/Advantage Score Min -6.88198 +trainer/V1 Predictions Mean -70.4513 +trainer/V1 Predictions Std 19.7421 +trainer/V1 Predictions Max -0.837141 +trainer/V1 Predictions Min -90.7884 +trainer/VF Loss 0.0892736 +expl/num steps total 866000 +expl/num paths total 1197 +expl/path length Mean 500 +expl/path length Std 93 +expl/path length Max 593 +expl/path length Min 407 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0262305 +expl/Actions Std 0.833478 +expl/Actions Max 2.70288 +expl/Actions Min -2.48575 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 790663 +eval/num paths total 878 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.168451 +eval/Actions Std 0.796549 +eval/Actions Max 0.998522 +eval/Actions Min -0.995683 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.09106e-06 +time/evaluation sampling (s) 2.65897 +time/exploration sampling (s) 2.71257 +time/logging (s) 0.00683261 +time/saving (s) 0.0102723 +time/training (s) 13.7612 +time/epoch (s) 19.1499 +time/total (s) 22024.5 +Epoch -135 +------------------------------ ---------------- +2022-05-16 00:09:54.855515 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -134 finished +------------------------------ ---------------- +epoch -134 +replay_buffer/size 999047 +trainer/num train calls 867000 +trainer/QF1 Loss 1.55475 +trainer/QF2 Loss 1.46979 +trainer/Policy Loss 38.7238 +trainer/Q1 Predictions Mean -68.9908 +trainer/Q1 Predictions Std 20.539 +trainer/Q1 Predictions Max -1.3411 +trainer/Q1 Predictions Min -86.4426 +trainer/Q2 Predictions Mean -69.0685 +trainer/Q2 Predictions Std 20.4785 +trainer/Q2 Predictions Max -1.48456 +trainer/Q2 Predictions Min -86.4665 +trainer/Q Targets Mean -69.519 +trainer/Q Targets Std 20.7029 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1274 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0116856 +trainer/policy/mean Std 0.723165 +trainer/policy/mean Max 0.99959 +trainer/policy/mean Min -0.999018 +trainer/policy/std Mean 0.41215 +trainer/policy/std Std 0.0215743 +trainer/policy/std Max 0.435881 +trainer/policy/std Min 0.380082 +trainer/Advantage Weights Mean 9.03094 +trainer/Advantage Weights Std 25.5053 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.52746e-19 +trainer/Advantage Score Mean -0.280847 +trainer/Advantage Score Std 0.674718 +trainer/Advantage Score Max 1.63042 +trainer/Advantage Score Min -4.28219 +trainer/V1 Predictions Mean -69.25 +trainer/V1 Predictions Std 20.7867 +trainer/V1 Predictions Max -1.54006 +trainer/V1 Predictions Min -87.1956 +trainer/VF Loss 0.0925943 +expl/num steps total 867000 +expl/num paths total 1198 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.195461 +expl/Actions Std 0.849648 +expl/Actions Max 2.21795 +expl/Actions Min -2.29031 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 791663 +eval/num paths total 879 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.134683 +eval/Actions Std 0.685019 +eval/Actions Max 0.999833 +eval/Actions Min -0.999973 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18978e-06 +time/evaluation sampling (s) 2.5282 +time/exploration sampling (s) 2.73332 +time/logging (s) 0.00960057 +time/saving (s) 0.0160559 +time/training (s) 13.8802 +time/epoch (s) 19.1674 +time/total (s) 22043.7 +Epoch -134 +------------------------------ ---------------- +2022-05-16 00:10:14.247904 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -133 finished +------------------------------ ---------------- +epoch -133 +replay_buffer/size 999047 +trainer/num train calls 868000 +trainer/QF1 Loss 0.76173 +trainer/QF2 Loss 0.779555 +trainer/Policy Loss 32.3973 +trainer/Q1 Predictions Mean -72.4841 +trainer/Q1 Predictions Std 17.7795 +trainer/Q1 Predictions Max -0.812226 +trainer/Q1 Predictions Min -87.4654 +trainer/Q2 Predictions Mean -72.4128 +trainer/Q2 Predictions Std 17.7253 +trainer/Q2 Predictions Max -0.781006 +trainer/Q2 Predictions Min -87.2482 +trainer/Q Targets Mean -72.4986 +trainer/Q Targets Std 17.4371 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6406 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0120852 +trainer/policy/mean Std 0.73799 +trainer/policy/mean Max 0.999777 +trainer/policy/mean Min -0.999582 +trainer/policy/std Mean 0.412533 +trainer/policy/std Std 0.0201184 +trainer/policy/std Max 0.434632 +trainer/policy/std Min 0.382559 +trainer/Advantage Weights Mean 8.03734 +trainer/Advantage Weights Std 23.6821 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.27273e-26 +trainer/Advantage Score Mean -0.275102 +trainer/Advantage Score Std 0.703391 +trainer/Advantage Score Max 1.31286 +trainer/Advantage Score Min -5.9626 +trainer/V1 Predictions Mean -72.1932 +trainer/V1 Predictions Std 17.6878 +trainer/V1 Predictions Max -1.64967 +trainer/V1 Predictions Min -86.5803 +trainer/VF Loss 0.0843961 +expl/num steps total 868000 +expl/num paths total 1199 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.098357 +expl/Actions Std 0.83606 +expl/Actions Max 2.52607 +expl/Actions Min -2.49642 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 792663 +eval/num paths total 880 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0476575 +eval/Actions Std 0.734275 +eval/Actions Max 0.999862 +eval/Actions Min -0.999242 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.18186e-06 +time/evaluation sampling (s) 2.68255 +time/exploration sampling (s) 2.70774 +time/logging (s) 0.00744283 +time/saving (s) 0.0107241 +time/training (s) 13.9646 +time/epoch (s) 19.373 +time/total (s) 22063.1 +Epoch -133 +------------------------------ ---------------- +2022-05-16 00:10:33.223405 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -132 finished +------------------------------ ---------------- +epoch -132 +replay_buffer/size 999047 +trainer/num train calls 869000 +trainer/QF1 Loss 0.809843 +trainer/QF2 Loss 0.961623 +trainer/Policy Loss 20.8957 +trainer/Q1 Predictions Mean -69.5714 +trainer/Q1 Predictions Std 19.8447 +trainer/Q1 Predictions Max -1.65901 +trainer/Q1 Predictions Min -90.891 +trainer/Q2 Predictions Mean -69.5893 +trainer/Q2 Predictions Std 19.793 +trainer/Q2 Predictions Max -1.60432 +trainer/Q2 Predictions Min -90.6932 +trainer/Q Targets Mean -69.8504 +trainer/Q Targets Std 19.8504 +trainer/Q Targets Max -3.01156 +trainer/Q Targets Min -91.5038 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00560651 +trainer/policy/mean Std 0.728249 +trainer/policy/mean Max 0.999032 +trainer/policy/mean Min -0.999393 +trainer/policy/std Mean 0.411745 +trainer/policy/std Std 0.0205738 +trainer/policy/std Max 0.434439 +trainer/policy/std Min 0.381587 +trainer/Advantage Weights Mean 5.3414 +trainer/Advantage Weights Std 19.1947 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.95971e-22 +trainer/Advantage Score Mean -0.391112 +trainer/Advantage Score Std 0.729883 +trainer/Advantage Score Max 1.59196 +trainer/Advantage Score Min -4.92807 +trainer/V1 Predictions Mean -69.5694 +trainer/V1 Predictions Std 20.1705 +trainer/V1 Predictions Max -0.905305 +trainer/V1 Predictions Min -91.6633 +trainer/VF Loss 0.0973407 +expl/num steps total 869000 +expl/num paths total 1200 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0609973 +expl/Actions Std 0.848993 +expl/Actions Max 2.30555 +expl/Actions Min -2.32648 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 793663 +eval/num paths total 881 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0918486 +eval/Actions Std 0.686045 +eval/Actions Max 0.999729 +eval/Actions Min -0.999831 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 9.88832e-06 +time/evaluation sampling (s) 2.56899 +time/exploration sampling (s) 2.82879 +time/logging (s) 0.00827896 +time/saving (s) 0.0128989 +time/training (s) 13.5436 +time/epoch (s) 18.9626 +time/total (s) 22082.1 +Epoch -132 +------------------------------ ---------------- +2022-05-16 00:10:52.249927 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -131 finished +------------------------------ ---------------- +epoch -131 +replay_buffer/size 999047 +trainer/num train calls 870000 +trainer/QF1 Loss 0.585224 +trainer/QF2 Loss 0.524339 +trainer/Policy Loss 14.7442 +trainer/Q1 Predictions Mean -72.1303 +trainer/Q1 Predictions Std 17.9013 +trainer/Q1 Predictions Max -1.21435 +trainer/Q1 Predictions Min -86.542 +trainer/Q2 Predictions Mean -72.2735 +trainer/Q2 Predictions Std 17.9032 +trainer/Q2 Predictions Max -0.90722 +trainer/Q2 Predictions Min -86.655 +trainer/Q Targets Mean -72.225 +trainer/Q Targets Std 17.9887 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7129 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00808459 +trainer/policy/mean Std 0.738434 +trainer/policy/mean Max 0.999252 +trainer/policy/mean Min -0.999856 +trainer/policy/std Mean 0.412599 +trainer/policy/std Std 0.0203243 +trainer/policy/std Max 0.433897 +trainer/policy/std Min 0.384511 +trainer/Advantage Weights Mean 2.78004 +trainer/Advantage Weights Std 13.2103 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.13313e-20 +trainer/Advantage Score Mean -0.426431 +trainer/Advantage Score Std 0.644017 +trainer/Advantage Score Max 0.812439 +trainer/Advantage Score Min -4.52941 +trainer/V1 Predictions Mean -71.8982 +trainer/V1 Predictions Std 18.1176 +trainer/V1 Predictions Max -0.555888 +trainer/V1 Predictions Min -86.8254 +trainer/VF Loss 0.0681447 +expl/num steps total 870000 +expl/num paths total 1201 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.124284 +expl/Actions Std 0.831394 +expl/Actions Max 2.25478 +expl/Actions Min -2.58073 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 794442 +eval/num paths total 882 +eval/path length Mean 779 +eval/path length Std 0 +eval/path length Max 779 +eval/path length Min 779 +eval/Rewards Mean 0.0012837 +eval/Rewards Std 0.0358057 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.0385752 +eval/Actions Std 0.742746 +eval/Actions Max 0.999938 +eval/Actions Min -0.999807 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.95695e-06 +time/evaluation sampling (s) 2.56273 +time/exploration sampling (s) 2.78914 +time/logging (s) 0.0100638 +time/saving (s) 0.0161029 +time/training (s) 13.6364 +time/epoch (s) 19.0144 +time/total (s) 22101.1 +Epoch -131 +------------------------------ ---------------- +2022-05-16 00:11:11.533311 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -130 finished +------------------------------ ---------------- +epoch -130 +replay_buffer/size 999047 +trainer/num train calls 871000 +trainer/QF1 Loss 1.03265 +trainer/QF2 Loss 0.849326 +trainer/Policy Loss 10.2259 +trainer/Q1 Predictions Mean -72.5375 +trainer/Q1 Predictions Std 16.3567 +trainer/Q1 Predictions Max -1.71165 +trainer/Q1 Predictions Min -87.9772 +trainer/Q2 Predictions Mean -72.5617 +trainer/Q2 Predictions Std 16.3441 +trainer/Q2 Predictions Max -0.668931 +trainer/Q2 Predictions Min -87.8149 +trainer/Q Targets Mean -72.3247 +trainer/Q Targets Std 16.4722 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1736 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0101489 +trainer/policy/mean Std 0.735205 +trainer/policy/mean Max 0.998913 +trainer/policy/mean Min -0.998395 +trainer/policy/std Mean 0.412082 +trainer/policy/std Std 0.0211567 +trainer/policy/std Max 0.434069 +trainer/policy/std Min 0.380784 +trainer/Advantage Weights Mean 3.85629 +trainer/Advantage Weights Std 17.4097 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.41058e-20 +trainer/Advantage Score Mean -0.564748 +trainer/Advantage Score Std 0.681301 +trainer/Advantage Score Max 1.3902 +trainer/Advantage Score Min -4.51718 +trainer/V1 Predictions Mean -72.0468 +trainer/V1 Predictions Std 16.5471 +trainer/V1 Predictions Max -0.724825 +trainer/V1 Predictions Min -86.9321 +trainer/VF Loss 0.106457 +expl/num steps total 871000 +expl/num paths total 1203 +expl/path length Mean 500 +expl/path length Std 173 +expl/path length Max 673 +expl/path length Min 327 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0328338 +expl/Actions Std 0.822421 +expl/Actions Max 2.19852 +expl/Actions Min -2.31344 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 795442 +eval/num paths total 883 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.363811 +eval/Actions Std 0.643258 +eval/Actions Max 0.999917 +eval/Actions Min -0.999751 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.2098e-06 +time/evaluation sampling (s) 2.53007 +time/exploration sampling (s) 2.78436 +time/logging (s) 0.00832934 +time/saving (s) 0.011214 +time/training (s) 13.9297 +time/epoch (s) 19.2636 +time/total (s) 22120.3 +Epoch -130 +------------------------------ ---------------- +2022-05-16 00:11:30.387498 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -129 finished +------------------------------ ---------------- +epoch -129 +replay_buffer/size 999047 +trainer/num train calls 872000 +trainer/QF1 Loss 1.13996 +trainer/QF2 Loss 1.05316 +trainer/Policy Loss 26.8091 +trainer/Q1 Predictions Mean -70.7983 +trainer/Q1 Predictions Std 18.6738 +trainer/Q1 Predictions Max -1.81821 +trainer/Q1 Predictions Min -87.6146 +trainer/Q2 Predictions Mean -70.769 +trainer/Q2 Predictions Std 18.7949 +trainer/Q2 Predictions Max -1.7092 +trainer/Q2 Predictions Min -88.0103 +trainer/Q Targets Mean -70.8665 +trainer/Q Targets Std 19.0777 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.5009 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0153787 +trainer/policy/mean Std 0.733557 +trainer/policy/mean Max 0.999441 +trainer/policy/mean Min -0.999494 +trainer/policy/std Mean 0.410595 +trainer/policy/std Std 0.0207906 +trainer/policy/std Max 0.431918 +trainer/policy/std Min 0.378309 +trainer/Advantage Weights Mean 6.07846 +trainer/Advantage Weights Std 22.1629 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.37774e-15 +trainer/Advantage Score Mean -0.379047 +trainer/Advantage Score Std 0.639664 +trainer/Advantage Score Max 2.06287 +trainer/Advantage Score Min -3.42183 +trainer/V1 Predictions Mean -70.7238 +trainer/V1 Predictions Std 18.9673 +trainer/V1 Predictions Max -1.66758 +trainer/V1 Predictions Min -88.5965 +trainer/VF Loss 0.106775 +expl/num steps total 872000 +expl/num paths total 1204 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.256169 +expl/Actions Std 0.823723 +expl/Actions Max 2.67529 +expl/Actions Min -2.53708 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 796265 +eval/num paths total 884 +eval/path length Mean 823 +eval/path length Std 0 +eval/path length Max 823 +eval/path length Min 823 +eval/Rewards Mean 0.00121507 +eval/Rewards Std 0.0348366 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0198327 +eval/Actions Std 0.750315 +eval/Actions Max 0.999803 +eval/Actions Min -0.999975 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.67895e-06 +time/evaluation sampling (s) 2.53655 +time/exploration sampling (s) 2.79284 +time/logging (s) 0.0112487 +time/saving (s) 0.0207231 +time/training (s) 13.4821 +time/epoch (s) 18.8435 +time/total (s) 22139.2 +Epoch -129 +------------------------------ ---------------- +2022-05-16 00:11:49.429979 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -128 finished +------------------------------ ---------------- +epoch -128 +replay_buffer/size 999047 +trainer/num train calls 873000 +trainer/QF1 Loss 0.857722 +trainer/QF2 Loss 0.924803 +trainer/Policy Loss 5.31163 +trainer/Q1 Predictions Mean -72.4893 +trainer/Q1 Predictions Std 16.393 +trainer/Q1 Predictions Max -0.937612 +trainer/Q1 Predictions Min -87.352 +trainer/Q2 Predictions Mean -72.5552 +trainer/Q2 Predictions Std 16.4931 +trainer/Q2 Predictions Max -1.03457 +trainer/Q2 Predictions Min -87.4678 +trainer/Q Targets Mean -72.1592 +trainer/Q Targets Std 16.6215 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9133 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000476651 +trainer/policy/mean Std 0.729884 +trainer/policy/mean Max 0.999868 +trainer/policy/mean Min -0.99995 +trainer/policy/std Mean 0.411846 +trainer/policy/std Std 0.0200219 +trainer/policy/std Max 0.435225 +trainer/policy/std Min 0.381017 +trainer/Advantage Weights Mean 1.33941 +trainer/Advantage Weights Std 9.99995 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.43768e-16 +trainer/Advantage Score Mean -0.709067 +trainer/Advantage Score Std 0.60731 +trainer/Advantage Score Max 0.938061 +trainer/Advantage Score Min -3.64783 +trainer/V1 Predictions Mean -71.9899 +trainer/V1 Predictions Std 16.5434 +trainer/V1 Predictions Max -1.78391 +trainer/V1 Predictions Min -86.8275 +trainer/VF Loss 0.0920555 +expl/num steps total 873000 +expl/num paths total 1205 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.132435 +expl/Actions Std 0.826506 +expl/Actions Max 2.30561 +expl/Actions Min -2.21493 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 797013 +eval/num paths total 885 +eval/path length Mean 748 +eval/path length Std 0 +eval/path length Max 748 +eval/path length Min 748 +eval/Rewards Mean 0.0013369 +eval/Rewards Std 0.0365392 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.027409 +eval/Actions Std 0.74215 +eval/Actions Max 0.999629 +eval/Actions Min -0.999876 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.74694e-06 +time/evaluation sampling (s) 2.59335 +time/exploration sampling (s) 2.96663 +time/logging (s) 0.00695287 +time/saving (s) 0.0104114 +time/training (s) 13.441 +time/epoch (s) 19.0184 +time/total (s) 22158.2 +Epoch -128 +------------------------------ ---------------- +2022-05-16 00:12:08.105137 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -127 finished +------------------------------ ---------------- +epoch -127 +replay_buffer/size 999047 +trainer/num train calls 874000 +trainer/QF1 Loss 1.33642 +trainer/QF2 Loss 1.40654 +trainer/Policy Loss 45.2956 +trainer/Q1 Predictions Mean -71.0364 +trainer/Q1 Predictions Std 19.2446 +trainer/Q1 Predictions Max -2.64473 +trainer/Q1 Predictions Min -89.9502 +trainer/Q2 Predictions Mean -70.9968 +trainer/Q2 Predictions Std 19.2269 +trainer/Q2 Predictions Max -3.3443 +trainer/Q2 Predictions Min -90.2455 +trainer/Q Targets Mean -71.3171 +trainer/Q Targets Std 19.1838 +trainer/Q Targets Max -3.58231 +trainer/Q Targets Min -90.1114 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00636596 +trainer/policy/mean Std 0.741911 +trainer/policy/mean Max 0.999666 +trainer/policy/mean Min -0.999538 +trainer/policy/std Mean 0.409254 +trainer/policy/std Std 0.0210142 +trainer/policy/std Max 0.432475 +trainer/policy/std Min 0.381447 +trainer/Advantage Weights Mean 9.54699 +trainer/Advantage Weights Std 25.187 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.87639e-23 +trainer/Advantage Score Mean -0.277088 +trainer/Advantage Score Std 0.740974 +trainer/Advantage Score Max 1.63346 +trainer/Advantage Score Min -5.07761 +trainer/V1 Predictions Mean -71.0145 +trainer/V1 Predictions Std 19.5383 +trainer/V1 Predictions Max -2.78121 +trainer/V1 Predictions Min -89.9645 +trainer/VF Loss 0.104471 +expl/num steps total 874000 +expl/num paths total 1206 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0539438 +expl/Actions Std 0.83758 +expl/Actions Max 2.44358 +expl/Actions Min -2.26835 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 797882 +eval/num paths total 886 +eval/path length Mean 869 +eval/path length Std 0 +eval/path length Max 869 +eval/path length Min 869 +eval/Rewards Mean 0.00115075 +eval/Rewards Std 0.0339032 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0196286 +eval/Actions Std 0.737743 +eval/Actions Max 0.999856 +eval/Actions Min -0.999871 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.09711e-06 +time/evaluation sampling (s) 2.43205 +time/exploration sampling (s) 2.75446 +time/logging (s) 0.0102515 +time/saving (s) 0.0146064 +time/training (s) 13.4532 +time/epoch (s) 18.6646 +time/total (s) 22176.9 +Epoch -127 +------------------------------ ---------------- +2022-05-16 00:12:26.718461 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -126 finished +------------------------------ ---------------- +epoch -126 +replay_buffer/size 999047 +trainer/num train calls 875000 +trainer/QF1 Loss 0.698504 +trainer/QF2 Loss 0.81863 +trainer/Policy Loss 25.9404 +trainer/Q1 Predictions Mean -69.9327 +trainer/Q1 Predictions Std 20.1068 +trainer/Q1 Predictions Max -0.495831 +trainer/Q1 Predictions Min -89.5906 +trainer/Q2 Predictions Mean -69.9383 +trainer/Q2 Predictions Std 20.1915 +trainer/Q2 Predictions Max 0.634208 +trainer/Q2 Predictions Min -89.6229 +trainer/Q Targets Mean -69.7096 +trainer/Q Targets Std 19.88 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4044 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0171387 +trainer/policy/mean Std 0.71416 +trainer/policy/mean Max 0.999496 +trainer/policy/mean Min -0.999622 +trainer/policy/std Mean 0.411158 +trainer/policy/std Std 0.0198155 +trainer/policy/std Max 0.432146 +trainer/policy/std Min 0.38536 +trainer/Advantage Weights Mean 4.93539 +trainer/Advantage Weights Std 19.9187 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.69489e-19 +trainer/Advantage Score Mean -0.538166 +trainer/Advantage Score Std 0.676102 +trainer/Advantage Score Max 1.12293 +trainer/Advantage Score Min -4.27578 +trainer/V1 Predictions Mean -69.423 +trainer/V1 Predictions Std 20.085 +trainer/V1 Predictions Max 1.15405 +trainer/V1 Predictions Min -88.584 +trainer/VF Loss 0.0945847 +expl/num steps total 875000 +expl/num paths total 1207 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.00873091 +expl/Actions Std 0.847149 +expl/Actions Max 2.44306 +expl/Actions Min -2.28606 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 798882 +eval/num paths total 887 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.116652 +eval/Actions Std 0.659495 +eval/Actions Max 0.999879 +eval/Actions Min -0.999792 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.64402e-06 +time/evaluation sampling (s) 2.49333 +time/exploration sampling (s) 2.71548 +time/logging (s) 0.0111873 +time/saving (s) 0.0115982 +time/training (s) 13.3647 +time/epoch (s) 18.5963 +time/total (s) 22195.5 +Epoch -126 +------------------------------ ---------------- +2022-05-16 00:12:45.201520 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -125 finished +------------------------------ ---------------- +epoch -125 +replay_buffer/size 999047 +trainer/num train calls 876000 +trainer/QF1 Loss 1.02235 +trainer/QF2 Loss 0.664817 +trainer/Policy Loss 15.7402 +trainer/Q1 Predictions Mean -71.7061 +trainer/Q1 Predictions Std 16.3638 +trainer/Q1 Predictions Max -6.18445 +trainer/Q1 Predictions Min -86.8527 +trainer/Q2 Predictions Mean -71.813 +trainer/Q2 Predictions Std 16.2536 +trainer/Q2 Predictions Max -7.66164 +trainer/Q2 Predictions Min -86.8643 +trainer/Q Targets Mean -71.8723 +trainer/Q Targets Std 16.1861 +trainer/Q Targets Max -7.50602 +trainer/Q Targets Min -86.9919 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00918476 +trainer/policy/mean Std 0.736537 +trainer/policy/mean Max 0.999415 +trainer/policy/mean Min -0.999808 +trainer/policy/std Mean 0.411764 +trainer/policy/std Std 0.0207272 +trainer/policy/std Max 0.433889 +trainer/policy/std Min 0.38396 +trainer/Advantage Weights Mean 3.59389 +trainer/Advantage Weights Std 16.9302 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.73822e-18 +trainer/Advantage Score Mean -0.579423 +trainer/Advantage Score Std 0.783251 +trainer/Advantage Score Max 1.09586 +trainer/Advantage Score Min -3.96994 +trainer/V1 Predictions Mean -71.4508 +trainer/V1 Predictions Std 16.5054 +trainer/V1 Predictions Max -6.26587 +trainer/V1 Predictions Min -86.8609 +trainer/VF Loss 0.109304 +expl/num steps total 876000 +expl/num paths total 1209 +expl/path length Mean 500 +expl/path length Std 142 +expl/path length Max 642 +expl/path length Min 358 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0120774 +expl/Actions Std 0.843791 +expl/Actions Max 2.39158 +expl/Actions Min -2.43938 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 799882 +eval/num paths total 888 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.15278 +eval/Actions Std 0.637602 +eval/Actions Max 0.999894 +eval/Actions Min -0.99941 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66312e-06 +time/evaluation sampling (s) 2.51889 +time/exploration sampling (s) 2.70206 +time/logging (s) 0.00684096 +time/saving (s) 0.0128235 +time/training (s) 13.2189 +time/epoch (s) 18.4595 +time/total (s) 22214 +Epoch -125 +------------------------------ ---------------- +2022-05-16 00:13:03.500574 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -124 finished +------------------------------ ---------------- +epoch -124 +replay_buffer/size 999047 +trainer/num train calls 877000 +trainer/QF1 Loss 1.01229 +trainer/QF2 Loss 1.05881 +trainer/Policy Loss 17.7309 +trainer/Q1 Predictions Mean -70.6558 +trainer/Q1 Predictions Std 20.5827 +trainer/Q1 Predictions Max 1.17982 +trainer/Q1 Predictions Min -87.4724 +trainer/Q2 Predictions Mean -70.7559 +trainer/Q2 Predictions Std 20.568 +trainer/Q2 Predictions Max -0.134239 +trainer/Q2 Predictions Min -86.9396 +trainer/Q Targets Mean -70.8763 +trainer/Q Targets Std 20.3325 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3729 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0071463 +trainer/policy/mean Std 0.732554 +trainer/policy/mean Max 0.999827 +trainer/policy/mean Min -0.998228 +trainer/policy/std Mean 0.411614 +trainer/policy/std Std 0.021557 +trainer/policy/std Max 0.435607 +trainer/policy/std Min 0.378847 +trainer/Advantage Weights Mean 5.7037 +trainer/Advantage Weights Std 21.638 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.8152e-20 +trainer/Advantage Score Mean -0.451808 +trainer/Advantage Score Std 0.709782 +trainer/Advantage Score Max 3.12665 +trainer/Advantage Score Min -4.50167 +trainer/V1 Predictions Mean -70.6075 +trainer/V1 Predictions Std 20.4436 +trainer/V1 Predictions Max 1.09639 +trainer/V1 Predictions Min -87.3929 +trainer/VF Loss 0.157166 +expl/num steps total 877000 +expl/num paths total 1210 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0224774 +expl/Actions Std 0.839473 +expl/Actions Max 2.36064 +expl/Actions Min -2.36537 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 800495 +eval/num paths total 889 +eval/path length Mean 613 +eval/path length Std 0 +eval/path length Max 613 +eval/path length Min 613 +eval/Rewards Mean 0.00163132 +eval/Rewards Std 0.0403567 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0110931 +eval/Actions Std 0.747206 +eval/Actions Max 0.999561 +eval/Actions Min -0.999572 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.02307e-06 +time/evaluation sampling (s) 2.56662 +time/exploration sampling (s) 2.68833 +time/logging (s) 0.00626381 +time/saving (s) 0.0104435 +time/training (s) 13.014 +time/epoch (s) 18.2856 +time/total (s) 22232.2 +Epoch -124 +------------------------------ ---------------- +2022-05-16 00:13:22.543143 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -123 finished +------------------------------ ---------------- +epoch -123 +replay_buffer/size 999047 +trainer/num train calls 878000 +trainer/QF1 Loss 1.91523 +trainer/QF2 Loss 1.89987 +trainer/Policy Loss 30.4103 +trainer/Q1 Predictions Mean -72.4887 +trainer/Q1 Predictions Std 16.247 +trainer/Q1 Predictions Max -2.00327 +trainer/Q1 Predictions Min -87.392 +trainer/Q2 Predictions Mean -72.5059 +trainer/Q2 Predictions Std 16.385 +trainer/Q2 Predictions Max -1.14205 +trainer/Q2 Predictions Min -87.5682 +trainer/Q Targets Mean -72.482 +trainer/Q Targets Std 16.5062 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.739 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0120207 +trainer/policy/mean Std 0.729533 +trainer/policy/mean Max 0.999644 +trainer/policy/mean Min -0.998909 +trainer/policy/std Mean 0.411581 +trainer/policy/std Std 0.0209333 +trainer/policy/std Max 0.434822 +trainer/policy/std Min 0.37988 +trainer/Advantage Weights Mean 6.409 +trainer/Advantage Weights Std 21.2705 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.18158e-18 +trainer/Advantage Score Mean -0.353103 +trainer/Advantage Score Std 0.636714 +trainer/Advantage Score Max 1.64595 +trainer/Advantage Score Min -4.12797 +trainer/V1 Predictions Mean -72.2608 +trainer/V1 Predictions Std 16.6407 +trainer/V1 Predictions Max -0.669628 +trainer/V1 Predictions Min -87.6118 +trainer/VF Loss 0.0895379 +expl/num steps total 878000 +expl/num paths total 1212 +expl/path length Mean 500 +expl/path length Std 479 +expl/path length Max 979 +expl/path length Min 21 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0103417 +expl/Actions Std 0.814012 +expl/Actions Max 2.17381 +expl/Actions Min -2.62606 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 801495 +eval/num paths total 890 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0953301 +eval/Actions Std 0.831596 +eval/Actions Max 0.999938 +eval/Actions Min -0.999911 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.51202e-06 +time/evaluation sampling (s) 2.63736 +time/exploration sampling (s) 2.72333 +time/logging (s) 0.0075642 +time/saving (s) 0.0122953 +time/training (s) 13.652 +time/epoch (s) 19.0326 +time/total (s) 22251.3 +Epoch -123 +------------------------------ ---------------- +2022-05-16 00:13:41.598145 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -122 finished +------------------------------ ---------------- +epoch -122 +replay_buffer/size 999047 +trainer/num train calls 879000 +trainer/QF1 Loss 0.887212 +trainer/QF2 Loss 0.922976 +trainer/Policy Loss 30.4413 +trainer/Q1 Predictions Mean -72.6353 +trainer/Q1 Predictions Std 17.6302 +trainer/Q1 Predictions Max -0.659694 +trainer/Q1 Predictions Min -86.5176 +trainer/Q2 Predictions Mean -72.6749 +trainer/Q2 Predictions Std 17.5266 +trainer/Q2 Predictions Max -2.54444 +trainer/Q2 Predictions Min -86.4332 +trainer/Q Targets Mean -73.0182 +trainer/Q Targets Std 17.9135 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4377 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00833852 +trainer/policy/mean Std 0.719798 +trainer/policy/mean Max 0.998288 +trainer/policy/mean Min -0.999885 +trainer/policy/std Mean 0.411867 +trainer/policy/std Std 0.0204647 +trainer/policy/std Max 0.43438 +trainer/policy/std Min 0.380816 +trainer/Advantage Weights Mean 8.5044 +trainer/Advantage Weights Std 22.8824 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.5328e-19 +trainer/Advantage Score Mean -0.215649 +trainer/Advantage Score Std 0.637028 +trainer/Advantage Score Max 3.1004 +trainer/Advantage Score Min -4.22378 +trainer/V1 Predictions Mean -72.8595 +trainer/V1 Predictions Std 17.8209 +trainer/V1 Predictions Max -0.702915 +trainer/V1 Predictions Min -87.3807 +trainer/VF Loss 0.109644 +expl/num steps total 879000 +expl/num paths total 1214 +expl/path length Mean 500 +expl/path length Std 133 +expl/path length Max 633 +expl/path length Min 367 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0045102 +expl/Actions Std 0.834365 +expl/Actions Max 2.25717 +expl/Actions Min -2.18362 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 802069 +eval/num paths total 891 +eval/path length Mean 574 +eval/path length Std 0 +eval/path length Max 574 +eval/path length Min 574 +eval/Rewards Mean 0.00174216 +eval/Rewards Std 0.0417028 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00516099 +eval/Actions Std 0.742578 +eval/Actions Max 0.999615 +eval/Actions Min -0.999912 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.63518e-06 +time/evaluation sampling (s) 2.53428 +time/exploration sampling (s) 2.67262 +time/logging (s) 0.00659334 +time/saving (s) 0.0125167 +time/training (s) 13.8148 +time/epoch (s) 19.0408 +time/total (s) 22270.3 +Epoch -122 +------------------------------ ---------------- +2022-05-16 00:14:00.915225 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -121 finished +------------------------------ ---------------- +epoch -121 +replay_buffer/size 999047 +trainer/num train calls 880000 +trainer/QF1 Loss 0.9352 +trainer/QF2 Loss 0.682146 +trainer/Policy Loss 21.3966 +trainer/Q1 Predictions Mean -72.2268 +trainer/Q1 Predictions Std 17.3805 +trainer/Q1 Predictions Max -1.14667 +trainer/Q1 Predictions Min -87.115 +trainer/Q2 Predictions Mean -72.335 +trainer/Q2 Predictions Std 17.382 +trainer/Q2 Predictions Max -1.87183 +trainer/Q2 Predictions Min -86.924 +trainer/Q Targets Mean -72.602 +trainer/Q Targets Std 17.2134 +trainer/Q Targets Max -3.27463 +trainer/Q Targets Min -87.3182 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00523939 +trainer/policy/mean Std 0.713907 +trainer/policy/mean Max 0.997873 +trainer/policy/mean Min -0.998897 +trainer/policy/std Mean 0.412015 +trainer/policy/std Std 0.0195612 +trainer/policy/std Max 0.436021 +trainer/policy/std Min 0.382912 +trainer/Advantage Weights Mean 5.43622 +trainer/Advantage Weights Std 19.0238 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.15219e-22 +trainer/Advantage Score Mean -0.351996 +trainer/Advantage Score Std 0.67108 +trainer/Advantage Score Max 1.83556 +trainer/Advantage Score Min -4.95088 +trainer/V1 Predictions Mean -72.3193 +trainer/V1 Predictions Std 17.5197 +trainer/V1 Predictions Max -0.765706 +trainer/V1 Predictions Min -87.2816 +trainer/VF Loss 0.092639 +expl/num steps total 880000 +expl/num paths total 1216 +expl/path length Mean 500 +expl/path length Std 179 +expl/path length Max 679 +expl/path length Min 321 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0737127 +expl/Actions Std 0.845825 +expl/Actions Max 2.39109 +expl/Actions Min -2.47728 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 803069 +eval/num paths total 892 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0357662 +eval/Actions Std 0.743602 +eval/Actions Max 0.999257 +eval/Actions Min -0.999887 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.78982e-06 +time/evaluation sampling (s) 2.58073 +time/exploration sampling (s) 2.7439 +time/logging (s) 0.0114532 +time/saving (s) 0.0162149 +time/training (s) 13.95 +time/epoch (s) 19.3023 +time/total (s) 22289.6 +Epoch -121 +------------------------------ ---------------- +2022-05-16 00:14:19.606056 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -120 finished +------------------------------ ---------------- +epoch -120 +replay_buffer/size 999047 +trainer/num train calls 881000 +trainer/QF1 Loss 1.25931 +trainer/QF2 Loss 0.99928 +trainer/Policy Loss 9.92671 +trainer/Q1 Predictions Mean -72.481 +trainer/Q1 Predictions Std 16.9099 +trainer/Q1 Predictions Max -0.85898 +trainer/Q1 Predictions Min -90.528 +trainer/Q2 Predictions Mean -72.4568 +trainer/Q2 Predictions Std 16.9649 +trainer/Q2 Predictions Max -0.467778 +trainer/Q2 Predictions Min -90.8147 +trainer/Q Targets Mean -72.296 +trainer/Q Targets Std 17.2191 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.5206 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00469491 +trainer/policy/mean Std 0.732615 +trainer/policy/mean Max 0.999917 +trainer/policy/mean Min -0.999469 +trainer/policy/std Mean 0.411142 +trainer/policy/std Std 0.0190945 +trainer/policy/std Max 0.432714 +trainer/policy/std Min 0.38339 +trainer/Advantage Weights Mean 3.51215 +trainer/Advantage Weights Std 14.5721 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.77588e-19 +trainer/Advantage Score Mean -0.381118 +trainer/Advantage Score Std 0.640081 +trainer/Advantage Score Max 1.22783 +trainer/Advantage Score Min -4.31748 +trainer/V1 Predictions Mean -72.126 +trainer/V1 Predictions Std 17.0489 +trainer/V1 Predictions Max 0.207028 +trainer/V1 Predictions Min -90.4671 +trainer/VF Loss 0.0708055 +expl/num steps total 881000 +expl/num paths total 1218 +expl/path length Mean 500 +expl/path length Std 232 +expl/path length Max 732 +expl/path length Min 268 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0520079 +expl/Actions Std 0.832663 +expl/Actions Max 2.3655 +expl/Actions Min -2.4219 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 804069 +eval/num paths total 893 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0918039 +eval/Actions Std 0.711144 +eval/Actions Max 0.999285 +eval/Actions Min -0.999819 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.98209e-06 +time/evaluation sampling (s) 2.54891 +time/exploration sampling (s) 2.68704 +time/logging (s) 0.00704689 +time/saving (s) 0.010354 +time/training (s) 13.4148 +time/epoch (s) 18.6682 +time/total (s) 22308.3 +Epoch -120 +------------------------------ ---------------- +2022-05-16 00:14:38.620004 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -119 finished +------------------------------ ---------------- +epoch -119 +replay_buffer/size 999047 +trainer/num train calls 882000 +trainer/QF1 Loss 0.642189 +trainer/QF2 Loss 0.556233 +trainer/Policy Loss 13.6984 +trainer/Q1 Predictions Mean -73.7573 +trainer/Q1 Predictions Std 15.7661 +trainer/Q1 Predictions Max -1.1808 +trainer/Q1 Predictions Min -91.327 +trainer/Q2 Predictions Mean -73.6991 +trainer/Q2 Predictions Std 15.7293 +trainer/Q2 Predictions Max -1.46603 +trainer/Q2 Predictions Min -90.6661 +trainer/Q Targets Mean -73.4755 +trainer/Q Targets Std 15.8847 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.4682 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0119908 +trainer/policy/mean Std 0.732003 +trainer/policy/mean Max 0.999688 +trainer/policy/mean Min -0.999429 +trainer/policy/std Mean 0.412951 +trainer/policy/std Std 0.0199387 +trainer/policy/std Max 0.433759 +trainer/policy/std Min 0.38425 +trainer/Advantage Weights Mean 4.85307 +trainer/Advantage Weights Std 17.9422 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.28594e-11 +trainer/Advantage Score Mean -0.434478 +trainer/Advantage Score Std 0.565387 +trainer/Advantage Score Max 0.838238 +trainer/Advantage Score Min -2.50769 +trainer/V1 Predictions Mean -73.2761 +trainer/V1 Predictions Std 15.8459 +trainer/V1 Predictions Max 0.433572 +trainer/V1 Predictions Min -90.7747 +trainer/VF Loss 0.0660609 +expl/num steps total 882000 +expl/num paths total 1220 +expl/path length Mean 500 +expl/path length Std 317 +expl/path length Max 817 +expl/path length Min 183 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0273498 +expl/Actions Std 0.839177 +expl/Actions Max 2.14584 +expl/Actions Min -2.50646 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 804684 +eval/num paths total 894 +eval/path length Mean 615 +eval/path length Std 0 +eval/path length Max 615 +eval/path length Min 615 +eval/Rewards Mean 0.00162602 +eval/Rewards Std 0.0402911 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0213732 +eval/Actions Std 0.753914 +eval/Actions Max 0.9999 +eval/Actions Min -0.999964 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.51015e-06 +time/evaluation sampling (s) 2.55886 +time/exploration sampling (s) 2.65928 +time/logging (s) 0.00629911 +time/saving (s) 0.0104293 +time/training (s) 13.7653 +time/epoch (s) 19.0002 +time/total (s) 22327.3 +Epoch -119 +------------------------------ ---------------- +2022-05-16 00:14:57.408254 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -118 finished +------------------------------ ---------------- +epoch -118 +replay_buffer/size 999047 +trainer/num train calls 883000 +trainer/QF1 Loss 0.76215 +trainer/QF2 Loss 0.890584 +trainer/Policy Loss 30.7912 +trainer/Q1 Predictions Mean -72.1943 +trainer/Q1 Predictions Std 17.1877 +trainer/Q1 Predictions Max 0.0559551 +trainer/Q1 Predictions Min -90.2992 +trainer/Q2 Predictions Mean -72.0318 +trainer/Q2 Predictions Std 17.1459 +trainer/Q2 Predictions Max 0.114712 +trainer/Q2 Predictions Min -89.8773 +trainer/Q Targets Mean -72.3252 +trainer/Q Targets Std 17.33 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.576 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0194538 +trainer/policy/mean Std 0.733952 +trainer/policy/mean Max 0.998608 +trainer/policy/mean Min -0.998769 +trainer/policy/std Mean 0.411449 +trainer/policy/std Std 0.0203685 +trainer/policy/std Max 0.431756 +trainer/policy/std Min 0.379615 +trainer/Advantage Weights Mean 5.73489 +trainer/Advantage Weights Std 20.9785 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.30905e-25 +trainer/Advantage Score Mean -0.413513 +trainer/Advantage Score Std 0.754732 +trainer/Advantage Score Max 1.01609 +trainer/Advantage Score Min -5.72953 +trainer/V1 Predictions Mean -72.0362 +trainer/V1 Predictions Std 17.4556 +trainer/V1 Predictions Max 1.31815 +trainer/V1 Predictions Min -90.6781 +trainer/VF Loss 0.0969213 +expl/num steps total 883000 +expl/num paths total 1221 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.28872 +expl/Actions Std 0.865993 +expl/Actions Max 2.4351 +expl/Actions Min -2.52427 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 805358 +eval/num paths total 895 +eval/path length Mean 674 +eval/path length Std 0 +eval/path length Max 674 +eval/path length Min 674 +eval/Rewards Mean 0.00148368 +eval/Rewards Std 0.03849 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0367862 +eval/Actions Std 0.728626 +eval/Actions Max 0.999594 +eval/Actions Min -0.999881 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.69804e-06 +time/evaluation sampling (s) 2.48735 +time/exploration sampling (s) 2.79094 +time/logging (s) 0.00671147 +time/saving (s) 0.0101583 +time/training (s) 13.4824 +time/epoch (s) 18.7775 +time/total (s) 22346.1 +Epoch -118 +------------------------------ ---------------- +2022-05-16 00:15:16.153136 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -117 finished +------------------------------ ---------------- +epoch -117 +replay_buffer/size 999047 +trainer/num train calls 884000 +trainer/QF1 Loss 0.727294 +trainer/QF2 Loss 0.728069 +trainer/Policy Loss 11.7247 +trainer/Q1 Predictions Mean -72.0665 +trainer/Q1 Predictions Std 18.9784 +trainer/Q1 Predictions Max -1.7297 +trainer/Q1 Predictions Min -87.8895 +trainer/Q2 Predictions Mean -72.0919 +trainer/Q2 Predictions Std 18.9623 +trainer/Q2 Predictions Max -2.06508 +trainer/Q2 Predictions Min -87.8454 +trainer/Q Targets Mean -71.9962 +trainer/Q Targets Std 19.0327 +trainer/Q Targets Max -2.81207 +trainer/Q Targets Min -87.3669 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00625121 +trainer/policy/mean Std 0.749144 +trainer/policy/mean Max 0.998068 +trainer/policy/mean Min -0.99932 +trainer/policy/std Mean 0.412403 +trainer/policy/std Std 0.0200777 +trainer/policy/std Max 0.434565 +trainer/policy/std Min 0.381187 +trainer/Advantage Weights Mean 3.8122 +trainer/Advantage Weights Std 16.0026 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.94719e-16 +trainer/Advantage Score Mean -0.383492 +trainer/Advantage Score Std 0.616781 +trainer/Advantage Score Max 2.87584 +trainer/Advantage Score Min -3.47685 +trainer/V1 Predictions Mean -71.715 +trainer/V1 Predictions Std 19.2944 +trainer/V1 Predictions Max 0.348706 +trainer/V1 Predictions Min -87.2409 +trainer/VF Loss 0.0896388 +expl/num steps total 884000 +expl/num paths total 1223 +expl/path length Mean 500 +expl/path length Std 233 +expl/path length Max 733 +expl/path length Min 267 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0464013 +expl/Actions Std 0.832049 +expl/Actions Max 2.20105 +expl/Actions Min -2.29054 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 806358 +eval/num paths total 896 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0462323 +eval/Actions Std 0.694705 +eval/Actions Max 0.99982 +eval/Actions Min -0.999775 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.12086e-06 +time/evaluation sampling (s) 2.52119 +time/exploration sampling (s) 2.7149 +time/logging (s) 0.0106359 +time/saving (s) 0.0151718 +time/training (s) 13.4734 +time/epoch (s) 18.7353 +time/total (s) 22364.8 +Epoch -117 +------------------------------ ---------------- +2022-05-16 00:15:35.510457 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -116 finished +------------------------------ ---------------- +epoch -116 +replay_buffer/size 999047 +trainer/num train calls 885000 +trainer/QF1 Loss 0.730323 +trainer/QF2 Loss 0.634497 +trainer/Policy Loss 28.508 +trainer/Q1 Predictions Mean -73.0793 +trainer/Q1 Predictions Std 16.6428 +trainer/Q1 Predictions Max -0.681632 +trainer/Q1 Predictions Min -86.4573 +trainer/Q2 Predictions Mean -73.0845 +trainer/Q2 Predictions Std 16.7291 +trainer/Q2 Predictions Max -0.379489 +trainer/Q2 Predictions Min -86.5961 +trainer/Q Targets Mean -73.3798 +trainer/Q Targets Std 16.7806 +trainer/Q Targets Max -1.08076 +trainer/Q Targets Min -87.0176 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0189426 +trainer/policy/mean Std 0.737669 +trainer/policy/mean Max 0.999715 +trainer/policy/mean Min -0.999305 +trainer/policy/std Mean 0.41249 +trainer/policy/std Std 0.0200979 +trainer/policy/std Max 0.435084 +trainer/policy/std Min 0.381924 +trainer/Advantage Weights Mean 5.09101 +trainer/Advantage Weights Std 18.1643 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.18387e-20 +trainer/Advantage Score Mean -0.38083 +trainer/Advantage Score Std 0.771308 +trainer/Advantage Score Max 1.99566 +trainer/Advantage Score Min -4.58829 +trainer/V1 Predictions Mean -73.0325 +trainer/V1 Predictions Std 17.1158 +trainer/V1 Predictions Max -1.15372 +trainer/V1 Predictions Min -86.5414 +trainer/VF Loss 0.103411 +expl/num steps total 885000 +expl/num paths total 1225 +expl/path length Mean 500 +expl/path length Std 255 +expl/path length Max 755 +expl/path length Min 245 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0369249 +expl/Actions Std 0.845517 +expl/Actions Max 2.57614 +expl/Actions Min -2.27531 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 807291 +eval/num paths total 897 +eval/path length Mean 933 +eval/path length Std 0 +eval/path length Max 933 +eval/path length Min 933 +eval/Rewards Mean 0.00107181 +eval/Rewards Std 0.032721 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0428904 +eval/Actions Std 0.741791 +eval/Actions Max 0.999452 +eval/Actions Min -0.999572 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.65009e-06 +time/evaluation sampling (s) 2.59624 +time/exploration sampling (s) 2.80097 +time/logging (s) 0.00735935 +time/saving (s) 0.0120403 +time/training (s) 13.9177 +time/epoch (s) 19.3343 +time/total (s) 22384.2 +Epoch -116 +------------------------------ ---------------- +2022-05-16 00:15:54.551845 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -115 finished +------------------------------ ---------------- +epoch -115 +replay_buffer/size 999047 +trainer/num train calls 886000 +trainer/QF1 Loss 0.754523 +trainer/QF2 Loss 0.74226 +trainer/Policy Loss 19.7049 +trainer/Q1 Predictions Mean -72.5906 +trainer/Q1 Predictions Std 17.0958 +trainer/Q1 Predictions Max 0.111186 +trainer/Q1 Predictions Min -86.6356 +trainer/Q2 Predictions Mean -72.6205 +trainer/Q2 Predictions Std 17.1012 +trainer/Q2 Predictions Max -0.98872 +trainer/Q2 Predictions Min -86.6055 +trainer/Q Targets Mean -72.372 +trainer/Q Targets Std 16.9423 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.3833 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00836978 +trainer/policy/mean Std 0.729381 +trainer/policy/mean Max 0.999829 +trainer/policy/mean Min -0.998444 +trainer/policy/std Mean 0.41111 +trainer/policy/std Std 0.0204899 +trainer/policy/std Max 0.433248 +trainer/policy/std Min 0.380569 +trainer/Advantage Weights Mean 3.69373 +trainer/Advantage Weights Std 15.9255 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.48784e-16 +trainer/Advantage Score Mean -0.424339 +trainer/Advantage Score Std 0.635958 +trainer/Advantage Score Max 1.36857 +trainer/Advantage Score Min -3.59299 +trainer/V1 Predictions Mean -72.0639 +trainer/V1 Predictions Std 17.1781 +trainer/V1 Predictions Max -1.25678 +trainer/V1 Predictions Min -86.0359 +trainer/VF Loss 0.0795907 +expl/num steps total 886000 +expl/num paths total 1226 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.177325 +expl/Actions Std 0.806883 +expl/Actions Max 2.2835 +expl/Actions Min -2.387 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 807820 +eval/num paths total 898 +eval/path length Mean 529 +eval/path length Std 0 +eval/path length Max 529 +eval/path length Min 529 +eval/Rewards Mean 0.00189036 +eval/Rewards Std 0.0434371 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0274589 +eval/Actions Std 0.732136 +eval/Actions Max 0.999767 +eval/Actions Min -0.999307 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.05891e-05 +time/evaluation sampling (s) 2.54612 +time/exploration sampling (s) 2.67442 +time/logging (s) 0.00891777 +time/saving (s) 0.0154221 +time/training (s) 13.7847 +time/epoch (s) 19.0296 +time/total (s) 22403.2 +Epoch -115 +------------------------------ ---------------- +2022-05-16 00:16:13.302552 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -114 finished +------------------------------ ---------------- +epoch -114 +replay_buffer/size 999047 +trainer/num train calls 887000 +trainer/QF1 Loss 0.852754 +trainer/QF2 Loss 1.08564 +trainer/Policy Loss 18.0507 +trainer/Q1 Predictions Mean -69.9792 +trainer/Q1 Predictions Std 21.4999 +trainer/Q1 Predictions Max -0.409607 +trainer/Q1 Predictions Min -86.8689 +trainer/Q2 Predictions Mean -69.8933 +trainer/Q2 Predictions Std 21.4877 +trainer/Q2 Predictions Max -0.482055 +trainer/Q2 Predictions Min -86.5867 +trainer/Q Targets Mean -69.6954 +trainer/Q Targets Std 21.5193 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5034 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0225878 +trainer/policy/mean Std 0.738279 +trainer/policy/mean Max 0.999437 +trainer/policy/mean Min -0.999472 +trainer/policy/std Mean 0.411199 +trainer/policy/std Std 0.0204113 +trainer/policy/std Max 0.435097 +trainer/policy/std Min 0.382915 +trainer/Advantage Weights Mean 4.81502 +trainer/Advantage Weights Std 18.574 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.0911e-13 +trainer/Advantage Score Mean -0.487304 +trainer/Advantage Score Std 0.661729 +trainer/Advantage Score Max 1.15899 +trainer/Advantage Score Min -2.98464 +trainer/V1 Predictions Mean -69.406 +trainer/V1 Predictions Std 21.6583 +trainer/V1 Predictions Max 0.583306 +trainer/V1 Predictions Min -86.2846 +trainer/VF Loss 0.0889483 +expl/num steps total 887000 +expl/num paths total 1228 +expl/path length Mean 500 +expl/path length Std 381 +expl/path length Max 881 +expl/path length Min 119 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.067084 +expl/Actions Std 0.833134 +expl/Actions Max 2.36268 +expl/Actions Min -2.09252 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 808820 +eval/num paths total 899 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0359753 +eval/Actions Std 0.732984 +eval/Actions Max 0.999945 +eval/Actions Min -0.999747 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.57395e-06 +time/evaluation sampling (s) 2.64891 +time/exploration sampling (s) 2.66225 +time/logging (s) 0.0107679 +time/saving (s) 0.0157307 +time/training (s) 13.3982 +time/epoch (s) 18.7359 +time/total (s) 22422 +Epoch -114 +------------------------------ ---------------- +2022-05-16 00:16:32.354295 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -113 finished +------------------------------ ---------------- +epoch -113 +replay_buffer/size 999047 +trainer/num train calls 888000 +trainer/QF1 Loss 1.22008 +trainer/QF2 Loss 1.21773 +trainer/Policy Loss 40.8893 +trainer/Q1 Predictions Mean -72.5459 +trainer/Q1 Predictions Std 16.3476 +trainer/Q1 Predictions Max -8.86809 +trainer/Q1 Predictions Min -90.4687 +trainer/Q2 Predictions Mean -72.5882 +trainer/Q2 Predictions Std 16.3662 +trainer/Q2 Predictions Max -10.1198 +trainer/Q2 Predictions Min -91.3431 +trainer/Q Targets Mean -72.4156 +trainer/Q Targets Std 16.3957 +trainer/Q Targets Max -7.7493 +trainer/Q Targets Min -90.2048 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0268156 +trainer/policy/mean Std 0.729084 +trainer/policy/mean Max 0.999687 +trainer/policy/mean Min -0.999725 +trainer/policy/std Mean 0.412791 +trainer/policy/std Std 0.0207086 +trainer/policy/std Max 0.436564 +trainer/policy/std Min 0.379735 +trainer/Advantage Weights Mean 7.22278 +trainer/Advantage Weights Std 23.2404 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.90293e-23 +trainer/Advantage Score Mean -0.392212 +trainer/Advantage Score Std 0.75924 +trainer/Advantage Score Max 1.98737 +trainer/Advantage Score Min -5.15977 +trainer/V1 Predictions Mean -72.1951 +trainer/V1 Predictions Std 16.4515 +trainer/V1 Predictions Max -8.14322 +trainer/V1 Predictions Min -90.6937 +trainer/VF Loss 0.124983 +expl/num steps total 888000 +expl/num paths total 1229 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0286761 +expl/Actions Std 0.815326 +expl/Actions Max 2.33415 +expl/Actions Min -2.24975 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 809820 +eval/num paths total 900 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.151569 +eval/Actions Std 0.756826 +eval/Actions Max 0.99989 +eval/Actions Min -0.99974 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.92575e-06 +time/evaluation sampling (s) 2.61673 +time/exploration sampling (s) 2.63493 +time/logging (s) 0.00730637 +time/saving (s) 0.0104902 +time/training (s) 13.76 +time/epoch (s) 19.0294 +time/total (s) 22441 +Epoch -113 +------------------------------ ---------------- +2022-05-16 00:16:51.313962 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -112 finished +------------------------------ ---------------- +epoch -112 +replay_buffer/size 999047 +trainer/num train calls 889000 +trainer/QF1 Loss 0.532782 +trainer/QF2 Loss 0.66693 +trainer/Policy Loss 18.7177 +trainer/Q1 Predictions Mean -72.9184 +trainer/Q1 Predictions Std 16.6781 +trainer/Q1 Predictions Max -2.83272 +trainer/Q1 Predictions Min -87.8925 +trainer/Q2 Predictions Mean -72.8498 +trainer/Q2 Predictions Std 16.7883 +trainer/Q2 Predictions Max -2.22109 +trainer/Q2 Predictions Min -87.8414 +trainer/Q Targets Mean -72.9619 +trainer/Q Targets Std 16.6 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5037 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0145001 +trainer/policy/mean Std 0.735853 +trainer/policy/mean Max 0.999301 +trainer/policy/mean Min -0.998811 +trainer/policy/std Mean 0.41296 +trainer/policy/std Std 0.0200896 +trainer/policy/std Max 0.437178 +trainer/policy/std Min 0.383097 +trainer/Advantage Weights Mean 4.38467 +trainer/Advantage Weights Std 17.7408 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.17008e-16 +trainer/Advantage Score Mean -0.375131 +trainer/Advantage Score Std 0.679776 +trainer/Advantage Score Max 2.1461 +trainer/Advantage Score Min -3.48714 +trainer/V1 Predictions Mean -72.6202 +trainer/V1 Predictions Std 16.8236 +trainer/V1 Predictions Max -2.47181 +trainer/V1 Predictions Min -87.4448 +trainer/VF Loss 0.103184 +expl/num steps total 889000 +expl/num paths total 1231 +expl/path length Mean 500 +expl/path length Std 396 +expl/path length Max 896 +expl/path length Min 104 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.016658 +expl/Actions Std 0.834767 +expl/Actions Max 2.2118 +expl/Actions Min -2.3218 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 810769 +eval/num paths total 902 +eval/path length Mean 474.5 +eval/path length Std 68.5 +eval/path length Max 543 +eval/path length Min 406 +eval/Rewards Mean 0.00210748 +eval/Rewards Std 0.0458589 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0321083 +eval/Actions Std 0.747179 +eval/Actions Max 0.9996 +eval/Actions Min -0.999865 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 2.99886e-06 +time/evaluation sampling (s) 2.55342 +time/exploration sampling (s) 2.65395 +time/logging (s) 0.00722843 +time/saving (s) 0.0103979 +time/training (s) 13.7213 +time/epoch (s) 18.9463 +time/total (s) 22459.9 +Epoch -112 +------------------------------ ---------------- +2022-05-16 00:17:10.492038 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -111 finished +------------------------------ ---------------- +epoch -111 +replay_buffer/size 999047 +trainer/num train calls 890000 +trainer/QF1 Loss 0.986833 +trainer/QF2 Loss 1.05451 +trainer/Policy Loss 12.4869 +trainer/Q1 Predictions Mean -71.9749 +trainer/Q1 Predictions Std 17.4244 +trainer/Q1 Predictions Max -2.28179 +trainer/Q1 Predictions Min -91.9586 +trainer/Q2 Predictions Mean -71.8907 +trainer/Q2 Predictions Std 17.3591 +trainer/Q2 Predictions Max -2.28434 +trainer/Q2 Predictions Min -92.2129 +trainer/Q Targets Mean -71.7462 +trainer/Q Targets Std 17.3886 +trainer/Q Targets Max -2.07213 +trainer/Q Targets Min -91.7792 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00237704 +trainer/policy/mean Std 0.732367 +trainer/policy/mean Max 0.999415 +trainer/policy/mean Min -0.998695 +trainer/policy/std Mean 0.413363 +trainer/policy/std Std 0.020105 +trainer/policy/std Max 0.436645 +trainer/policy/std Min 0.38318 +trainer/Advantage Weights Mean 3.15908 +trainer/Advantage Weights Std 15.8311 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.49968e-21 +trainer/Advantage Score Mean -0.595241 +trainer/Advantage Score Std 0.666225 +trainer/Advantage Score Max 1.58733 +trainer/Advantage Score Min -4.68503 +trainer/V1 Predictions Mean -71.462 +trainer/V1 Predictions Std 17.4065 +trainer/V1 Predictions Max -0.439787 +trainer/V1 Predictions Min -91.9076 +trainer/VF Loss 0.0958454 +expl/num steps total 890000 +expl/num paths total 1232 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0490765 +expl/Actions Std 0.834902 +expl/Actions Max 2.38217 +expl/Actions Min -2.27357 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 811769 +eval/num paths total 903 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00959173 +eval/Actions Std 0.778729 +eval/Actions Max 0.999915 +eval/Actions Min -0.999553 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 7.76397e-06 +time/evaluation sampling (s) 2.51852 +time/exploration sampling (s) 2.5724 +time/logging (s) 0.00747035 +time/saving (s) 0.0144133 +time/training (s) 14.0523 +time/epoch (s) 19.1651 +time/total (s) 22479.1 +Epoch -111 +------------------------------ ---------------- +2022-05-16 00:17:29.476292 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -110 finished +------------------------------ ---------------- +epoch -110 +replay_buffer/size 999047 +trainer/num train calls 891000 +trainer/QF1 Loss 0.696543 +trainer/QF2 Loss 0.574926 +trainer/Policy Loss 23.1964 +trainer/Q1 Predictions Mean -74.2317 +trainer/Q1 Predictions Std 15.614 +trainer/Q1 Predictions Max -2.94768 +trainer/Q1 Predictions Min -90.3624 +trainer/Q2 Predictions Mean -74.3173 +trainer/Q2 Predictions Std 15.5076 +trainer/Q2 Predictions Max -1.97407 +trainer/Q2 Predictions Min -90.9826 +trainer/Q Targets Mean -74.5373 +trainer/Q Targets Std 15.554 +trainer/Q Targets Max 0 +trainer/Q Targets Min -91.231 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00732966 +trainer/policy/mean Std 0.730574 +trainer/policy/mean Max 0.999735 +trainer/policy/mean Min -0.999909 +trainer/policy/std Mean 0.411121 +trainer/policy/std Std 0.0195983 +trainer/policy/std Max 0.431597 +trainer/policy/std Min 0.381507 +trainer/Advantage Weights Mean 5.84656 +trainer/Advantage Weights Std 19.8187 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.83453e-20 +trainer/Advantage Score Mean -0.279309 +trainer/Advantage Score Std 0.617712 +trainer/Advantage Score Max 1.24442 +trainer/Advantage Score Min -4.44759 +trainer/V1 Predictions Mean -74.2618 +trainer/V1 Predictions Std 15.7143 +trainer/V1 Predictions Max -2.21047 +trainer/V1 Predictions Min -91.2029 +trainer/VF Loss 0.0645762 +expl/num steps total 891000 +expl/num paths total 1233 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.152365 +expl/Actions Std 0.858228 +expl/Actions Max 2.45167 +expl/Actions Min -2.36454 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 812719 +eval/num paths total 904 +eval/path length Mean 950 +eval/path length Std 0 +eval/path length Max 950 +eval/path length Min 950 +eval/Rewards Mean 0.00105263 +eval/Rewards Std 0.0324272 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.017498 +eval/Actions Std 0.726496 +eval/Actions Max 0.999413 +eval/Actions Min -0.999926 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.03197e-06 +time/evaluation sampling (s) 2.47778 +time/exploration sampling (s) 2.56964 +time/logging (s) 0.00711499 +time/saving (s) 0.0139848 +time/training (s) 13.9021 +time/epoch (s) 18.9706 +time/total (s) 22498.1 +Epoch -110 +------------------------------ ---------------- +2022-05-16 00:17:48.404791 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -109 finished +------------------------------ ---------------- +epoch -109 +replay_buffer/size 999047 +trainer/num train calls 892000 +trainer/QF1 Loss 0.926951 +trainer/QF2 Loss 0.998584 +trainer/Policy Loss 41.2582 +trainer/Q1 Predictions Mean -70.8332 +trainer/Q1 Predictions Std 20.3731 +trainer/Q1 Predictions Max -0.306664 +trainer/Q1 Predictions Min -86.389 +trainer/Q2 Predictions Mean -70.827 +trainer/Q2 Predictions Std 20.2513 +trainer/Q2 Predictions Max -0.667617 +trainer/Q2 Predictions Min -86.5954 +trainer/Q Targets Mean -71.1845 +trainer/Q Targets Std 20.3299 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7014 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00199288 +trainer/policy/mean Std 0.7346 +trainer/policy/mean Max 0.999778 +trainer/policy/mean Min -0.999846 +trainer/policy/std Mean 0.411466 +trainer/policy/std Std 0.0195456 +trainer/policy/std Max 0.432403 +trainer/policy/std Min 0.381366 +trainer/Advantage Weights Mean 10.7671 +trainer/Advantage Weights Std 26.0301 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.43534e-13 +trainer/Advantage Score Mean -0.16976 +trainer/Advantage Score Std 0.643467 +trainer/Advantage Score Max 2.18532 +trainer/Advantage Score Min -2.90435 +trainer/V1 Predictions Mean -70.9209 +trainer/V1 Predictions Std 20.5267 +trainer/V1 Predictions Max -0.0404105 +trainer/V1 Predictions Min -86.5389 +trainer/VF Loss 0.101979 +expl/num steps total 892000 +expl/num paths total 1234 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0179015 +expl/Actions Std 0.828047 +expl/Actions Max 2.47998 +expl/Actions Min -2.12272 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 813336 +eval/num paths total 905 +eval/path length Mean 617 +eval/path length Std 0 +eval/path length Max 617 +eval/path length Min 617 +eval/Rewards Mean 0.00162075 +eval/Rewards Std 0.0402258 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0256796 +eval/Actions Std 0.718802 +eval/Actions Max 0.99993 +eval/Actions Min -0.999822 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.39793e-06 +time/evaluation sampling (s) 2.46748 +time/exploration sampling (s) 2.50971 +time/logging (s) 0.00984679 +time/saving (s) 0.0160796 +time/training (s) 13.9151 +time/epoch (s) 18.9182 +time/total (s) 22517 +Epoch -109 +------------------------------ ---------------- +2022-05-16 00:18:07.212495 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -108 finished +------------------------------ ---------------- +epoch -108 +replay_buffer/size 999047 +trainer/num train calls 893000 +trainer/QF1 Loss 1.11891 +trainer/QF2 Loss 1.25692 +trainer/Policy Loss 39.1231 +trainer/Q1 Predictions Mean -70.5761 +trainer/Q1 Predictions Std 19.5387 +trainer/Q1 Predictions Max 0.125963 +trainer/Q1 Predictions Min -86.9983 +trainer/Q2 Predictions Mean -70.5313 +trainer/Q2 Predictions Std 19.5887 +trainer/Q2 Predictions Max -0.41447 +trainer/Q2 Predictions Min -86.6709 +trainer/Q Targets Mean -70.8972 +trainer/Q Targets Std 19.7379 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1576 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0181963 +trainer/policy/mean Std 0.732925 +trainer/policy/mean Max 0.999781 +trainer/policy/mean Min -0.999236 +trainer/policy/std Mean 0.410737 +trainer/policy/std Std 0.0201431 +trainer/policy/std Max 0.431361 +trainer/policy/std Min 0.379064 +trainer/Advantage Weights Mean 9.71079 +trainer/Advantage Weights Std 24.444 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.30507e-33 +trainer/Advantage Score Mean -0.331188 +trainer/Advantage Score Std 0.847322 +trainer/Advantage Score Max 1.02706 +trainer/Advantage Score Min -7.57191 +trainer/V1 Predictions Mean -70.594 +trainer/V1 Predictions Std 19.8059 +trainer/V1 Predictions Max -1.28339 +trainer/V1 Predictions Min -87.3435 +trainer/VF Loss 0.116095 +expl/num steps total 893000 +expl/num paths total 1236 +expl/path length Mean 500 +expl/path length Std 185 +expl/path length Max 685 +expl/path length Min 315 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0473765 +expl/Actions Std 0.842932 +expl/Actions Max 2.28402 +expl/Actions Min -2.40295 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 813943 +eval/num paths total 906 +eval/path length Mean 607 +eval/path length Std 0 +eval/path length Max 607 +eval/path length Min 607 +eval/Rewards Mean 0.00164745 +eval/Rewards Std 0.0405553 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0387425 +eval/Actions Std 0.753946 +eval/Actions Max 0.999731 +eval/Actions Min -0.999686 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.38024e-06 +time/evaluation sampling (s) 2.51665 +time/exploration sampling (s) 2.61181 +time/logging (s) 0.0105372 +time/saving (s) 0.0124189 +time/training (s) 13.6383 +time/epoch (s) 18.7897 +time/total (s) 22535.8 +Epoch -108 +------------------------------ ---------------- +2022-05-16 00:18:25.911162 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -107 finished +------------------------------ ---------------- +epoch -107 +replay_buffer/size 999047 +trainer/num train calls 894000 +trainer/QF1 Loss 0.666859 +trainer/QF2 Loss 0.687184 +trainer/Policy Loss 20.5023 +trainer/Q1 Predictions Mean -72.9251 +trainer/Q1 Predictions Std 17.0212 +trainer/Q1 Predictions Max 0.570935 +trainer/Q1 Predictions Min -88.2313 +trainer/Q2 Predictions Mean -72.9555 +trainer/Q2 Predictions Std 17.0669 +trainer/Q2 Predictions Max 0.590262 +trainer/Q2 Predictions Min -88.971 +trainer/Q Targets Mean -73.066 +trainer/Q Targets Std 16.9843 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.8256 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.019691 +trainer/policy/mean Std 0.727457 +trainer/policy/mean Max 0.999826 +trainer/policy/mean Min -0.99971 +trainer/policy/std Mean 0.411789 +trainer/policy/std Std 0.0201931 +trainer/policy/std Max 0.431572 +trainer/policy/std Min 0.382728 +trainer/Advantage Weights Mean 4.5544 +trainer/Advantage Weights Std 17.8119 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.1904e-13 +trainer/Advantage Score Mean -0.405509 +trainer/Advantage Score Std 0.570551 +trainer/Advantage Score Max 1.49238 +trainer/Advantage Score Min -2.87735 +trainer/V1 Predictions Mean -72.7733 +trainer/V1 Predictions Std 17.0104 +trainer/V1 Predictions Max 0.335369 +trainer/V1 Predictions Min -89.2155 +trainer/VF Loss 0.0746722 +expl/num steps total 894000 +expl/num paths total 1238 +expl/path length Mean 500 +expl/path length Std 304 +expl/path length Max 804 +expl/path length Min 196 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.042034 +expl/Actions Std 0.844159 +expl/Actions Max 2.28455 +expl/Actions Min -2.46257 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 814943 +eval/num paths total 907 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0381801 +eval/Actions Std 0.751263 +eval/Actions Max 0.999879 +eval/Actions Min -0.999945 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.92808e-06 +time/evaluation sampling (s) 2.52186 +time/exploration sampling (s) 2.5996 +time/logging (s) 0.00914125 +time/saving (s) 0.0186225 +time/training (s) 13.5316 +time/epoch (s) 18.6808 +time/total (s) 22554.5 +Epoch -107 +------------------------------ ---------------- +2022-05-16 00:18:45.017773 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -106 finished +------------------------------ ---------------- +epoch -106 +replay_buffer/size 999047 +trainer/num train calls 895000 +trainer/QF1 Loss 12.8779 +trainer/QF2 Loss 11.4731 +trainer/Policy Loss 16.2849 +trainer/Q1 Predictions Mean -73.7109 +trainer/Q1 Predictions Std 15.8373 +trainer/Q1 Predictions Max -3.98782 +trainer/Q1 Predictions Min -87.8696 +trainer/Q2 Predictions Mean -73.7324 +trainer/Q2 Predictions Std 15.817 +trainer/Q2 Predictions Max -4.68286 +trainer/Q2 Predictions Min -87.7214 +trainer/Q Targets Mean -73.2851 +trainer/Q Targets Std 16.1877 +trainer/Q Targets Max -3.97045 +trainer/Q Targets Min -87.0702 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00036168 +trainer/policy/mean Std 0.732433 +trainer/policy/mean Max 0.998579 +trainer/policy/mean Min -0.999709 +trainer/policy/std Mean 0.410995 +trainer/policy/std Std 0.0190415 +trainer/policy/std Max 0.431754 +trainer/policy/std Min 0.381894 +trainer/Advantage Weights Mean 4.57168 +trainer/Advantage Weights Std 17.8528 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.14658e-37 +trainer/Advantage Score Mean -0.414405 +trainer/Advantage Score Std 0.921478 +trainer/Advantage Score Max 1.6078 +trainer/Advantage Score Min -8.50589 +trainer/V1 Predictions Mean -73.283 +trainer/V1 Predictions Std 15.8496 +trainer/V1 Predictions Max -3.69555 +trainer/V1 Predictions Min -86.9909 +trainer/VF Loss 0.135345 +expl/num steps total 895000 +expl/num paths total 1240 +expl/path length Mean 500 +expl/path length Std 96 +expl/path length Max 596 +expl/path length Min 404 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0194926 +expl/Actions Std 0.84124 +expl/Actions Max 2.14006 +expl/Actions Min -2.44093 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 815605 +eval/num paths total 908 +eval/path length Mean 662 +eval/path length Std 0 +eval/path length Max 662 +eval/path length Min 662 +eval/Rewards Mean 0.00151057 +eval/Rewards Std 0.0388367 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00823776 +eval/Actions Std 0.739403 +eval/Actions Max 0.999613 +eval/Actions Min -0.999685 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.11738e-06 +time/evaluation sampling (s) 2.48415 +time/exploration sampling (s) 2.68569 +time/logging (s) 0.00673894 +time/saving (s) 0.0115633 +time/training (s) 13.9012 +time/epoch (s) 19.0894 +time/total (s) 22573.6 +Epoch -106 +------------------------------ ---------------- +2022-05-16 00:19:04.021304 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -105 finished +------------------------------ ---------------- +epoch -105 +replay_buffer/size 999047 +trainer/num train calls 896000 +trainer/QF1 Loss 0.890966 +trainer/QF2 Loss 1.11976 +trainer/Policy Loss 16.3966 +trainer/Q1 Predictions Mean -72.7221 +trainer/Q1 Predictions Std 17.2489 +trainer/Q1 Predictions Max -2.1907 +trainer/Q1 Predictions Min -87.3524 +trainer/Q2 Predictions Mean -72.7273 +trainer/Q2 Predictions Std 17.1732 +trainer/Q2 Predictions Max -2.56548 +trainer/Q2 Predictions Min -87.4542 +trainer/Q Targets Mean -72.7885 +trainer/Q Targets Std 17.67 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5644 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00644299 +trainer/policy/mean Std 0.734143 +trainer/policy/mean Max 0.999183 +trainer/policy/mean Min -0.998422 +trainer/policy/std Mean 0.411116 +trainer/policy/std Std 0.0204451 +trainer/policy/std Max 0.431072 +trainer/policy/std Min 0.380831 +trainer/Advantage Weights Mean 4.26982 +trainer/Advantage Weights Std 16.2942 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.97148e-19 +trainer/Advantage Score Mean -0.385155 +trainer/Advantage Score Std 0.703197 +trainer/Advantage Score Max 3.50399 +trainer/Advantage Score Min -4.18073 +trainer/V1 Predictions Mean -72.5994 +trainer/V1 Predictions Std 17.4735 +trainer/V1 Predictions Max -2.79165 +trainer/V1 Predictions Min -87.5277 +trainer/VF Loss 0.117108 +expl/num steps total 896000 +expl/num paths total 1242 +expl/path length Mean 500 +expl/path length Std 438 +expl/path length Max 938 +expl/path length Min 62 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0459842 +expl/Actions Std 0.81994 +expl/Actions Max 2.28375 +expl/Actions Min -2.3573 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 816605 +eval/num paths total 909 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.23753 +eval/Actions Std 0.80124 +eval/Actions Max 0.999782 +eval/Actions Min -0.999811 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.78909e-06 +time/evaluation sampling (s) 2.46912 +time/exploration sampling (s) 2.54355 +time/logging (s) 0.00953878 +time/saving (s) 0.0148664 +time/training (s) 13.9452 +time/epoch (s) 18.9823 +time/total (s) 22592.6 +Epoch -105 +------------------------------ ---------------- +2022-05-16 00:19:22.828565 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -104 finished +------------------------------ ---------------- +epoch -104 +replay_buffer/size 999047 +trainer/num train calls 897000 +trainer/QF1 Loss 0.801703 +trainer/QF2 Loss 0.937755 +trainer/Policy Loss 15.6619 +trainer/Q1 Predictions Mean -70.9708 +trainer/Q1 Predictions Std 18.8749 +trainer/Q1 Predictions Max -0.677365 +trainer/Q1 Predictions Min -88.2924 +trainer/Q2 Predictions Mean -71.0516 +trainer/Q2 Predictions Std 18.7799 +trainer/Q2 Predictions Max -0.485827 +trainer/Q2 Predictions Min -88.1706 +trainer/Q Targets Mean -70.9082 +trainer/Q Targets Std 19.0861 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.6469 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00476216 +trainer/policy/mean Std 0.733168 +trainer/policy/mean Max 0.998923 +trainer/policy/mean Min -0.999902 +trainer/policy/std Mean 0.412503 +trainer/policy/std Std 0.0215393 +trainer/policy/std Max 0.434396 +trainer/policy/std Min 0.382103 +trainer/Advantage Weights Mean 3.62098 +trainer/Advantage Weights Std 14.9692 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.45986e-27 +trainer/Advantage Score Mean -0.468879 +trainer/Advantage Score Std 0.7265 +trainer/Advantage Score Max 0.671982 +trainer/Advantage Score Min -6.17915 +trainer/V1 Predictions Mean -70.7321 +trainer/V1 Predictions Std 18.9847 +trainer/V1 Predictions Max -0.662695 +trainer/V1 Predictions Min -89.0252 +trainer/VF Loss 0.084582 +expl/num steps total 897000 +expl/num paths total 1244 +expl/path length Mean 500 +expl/path length Std 490 +expl/path length Max 990 +expl/path length Min 10 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0133953 +expl/Actions Std 0.797933 +expl/Actions Max 2.24309 +expl/Actions Min -2.40694 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 817605 +eval/num paths total 910 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00137137 +eval/Actions Std 0.745427 +eval/Actions Max 0.999886 +eval/Actions Min -0.999704 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.08012e-06 +time/evaluation sampling (s) 2.44621 +time/exploration sampling (s) 2.49628 +time/logging (s) 0.00895136 +time/saving (s) 0.0119865 +time/training (s) 13.8271 +time/epoch (s) 18.7905 +time/total (s) 22611.4 +Epoch -104 +------------------------------ ---------------- +2022-05-16 00:19:41.327048 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -103 finished +------------------------------ ---------------- +epoch -103 +replay_buffer/size 999047 +trainer/num train calls 898000 +trainer/QF1 Loss 0.757381 +trainer/QF2 Loss 0.856994 +trainer/Policy Loss 6.18567 +trainer/Q1 Predictions Mean -71.3875 +trainer/Q1 Predictions Std 19.8426 +trainer/Q1 Predictions Max -0.677305 +trainer/Q1 Predictions Min -88.2127 +trainer/Q2 Predictions Mean -71.4766 +trainer/Q2 Predictions Std 19.7884 +trainer/Q2 Predictions Max -0.578608 +trainer/Q2 Predictions Min -88.438 +trainer/Q Targets Mean -71.1383 +trainer/Q Targets Std 19.8266 +trainer/Q Targets Max 0.332582 +trainer/Q Targets Min -87.3086 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00283802 +trainer/policy/mean Std 0.735292 +trainer/policy/mean Max 0.999607 +trainer/policy/mean Min -0.999778 +trainer/policy/std Mean 0.40994 +trainer/policy/std Std 0.0218469 +trainer/policy/std Max 0.435987 +trainer/policy/std Min 0.379358 +trainer/Advantage Weights Mean 1.38305 +trainer/Advantage Weights Std 10.8059 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.90327e-24 +trainer/Advantage Score Mean -0.733538 +trainer/Advantage Score Std 0.63992 +trainer/Advantage Score Max 1.96852 +trainer/Advantage Score Min -5.29692 +trainer/V1 Predictions Mean -70.9139 +trainer/V1 Predictions Std 19.8323 +trainer/V1 Predictions Max 0.366507 +trainer/V1 Predictions Min -87.3818 +trainer/VF Loss 0.109073 +expl/num steps total 898000 +expl/num paths total 1246 +expl/path length Mean 500 +expl/path length Std 328 +expl/path length Max 828 +expl/path length Min 172 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0282227 +expl/Actions Std 0.827342 +expl/Actions Max 2.09981 +expl/Actions Min -2.40361 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 818605 +eval/num paths total 911 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.11832 +eval/Actions Std 0.70737 +eval/Actions Max 0.999375 +eval/Actions Min -0.999538 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.92901e-06 +time/evaluation sampling (s) 2.48356 +time/exploration sampling (s) 2.4709 +time/logging (s) 0.0137193 +time/saving (s) 0.0187961 +time/training (s) 13.5015 +time/epoch (s) 18.4884 +time/total (s) 22629.9 +Epoch -103 +------------------------------ ---------------- +2022-05-16 00:20:00.160318 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -102 finished +------------------------------ ---------------- +epoch -102 +replay_buffer/size 999047 +trainer/num train calls 899000 +trainer/QF1 Loss 0.950723 +trainer/QF2 Loss 0.747405 +trainer/Policy Loss 20.6523 +trainer/Q1 Predictions Mean -72.4412 +trainer/Q1 Predictions Std 17.0121 +trainer/Q1 Predictions Max -1.02888 +trainer/Q1 Predictions Min -89.9564 +trainer/Q2 Predictions Mean -72.4523 +trainer/Q2 Predictions Std 16.9999 +trainer/Q2 Predictions Max -1.53773 +trainer/Q2 Predictions Min -89.9158 +trainer/Q Targets Mean -72.1288 +trainer/Q Targets Std 17.2736 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.4343 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0080767 +trainer/policy/mean Std 0.724435 +trainer/policy/mean Max 0.999501 +trainer/policy/mean Min -0.999949 +trainer/policy/std Mean 0.411159 +trainer/policy/std Std 0.0204131 +trainer/policy/std Max 0.435167 +trainer/policy/std Min 0.383273 +trainer/Advantage Weights Mean 5.47006 +trainer/Advantage Weights Std 20.1234 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.87636e-21 +trainer/Advantage Score Mean -0.446651 +trainer/Advantage Score Std 0.684109 +trainer/Advantage Score Max 1.49788 +trainer/Advantage Score Min -4.69994 +trainer/V1 Predictions Mean -71.9065 +trainer/V1 Predictions Std 17.3308 +trainer/V1 Predictions Max -1.2358 +trainer/V1 Predictions Min -90.0079 +trainer/VF Loss 0.0964956 +expl/num steps total 899000 +expl/num paths total 1248 +expl/path length Mean 500 +expl/path length Std 365 +expl/path length Max 865 +expl/path length Min 135 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00314936 +expl/Actions Std 0.835376 +expl/Actions Max 2.48137 +expl/Actions Min -2.3204 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 819605 +eval/num paths total 912 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.186414 +eval/Actions Std 0.770457 +eval/Actions Max 0.999963 +eval/Actions Min -0.999872 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.24007e-06 +time/evaluation sampling (s) 2.47668 +time/exploration sampling (s) 2.50444 +time/logging (s) 0.00670565 +time/saving (s) 0.00962419 +time/training (s) 13.8021 +time/epoch (s) 18.7995 +time/total (s) 22648.7 +Epoch -102 +------------------------------ ---------------- +2022-05-16 00:20:18.867789 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -101 finished +------------------------------ ---------------- +epoch -101 +replay_buffer/size 999047 +trainer/num train calls 900000 +trainer/QF1 Loss 2.07551 +trainer/QF2 Loss 2.10551 +trainer/Policy Loss 17.9845 +trainer/Q1 Predictions Mean -72.005 +trainer/Q1 Predictions Std 18.8129 +trainer/Q1 Predictions Max -1.23657 +trainer/Q1 Predictions Min -90.9951 +trainer/Q2 Predictions Mean -72.0802 +trainer/Q2 Predictions Std 18.7533 +trainer/Q2 Predictions Max -1.01926 +trainer/Q2 Predictions Min -91.3396 +trainer/Q Targets Mean -71.8175 +trainer/Q Targets Std 18.8104 +trainer/Q Targets Max 0.00235152 +trainer/Q Targets Min -90.3117 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00797806 +trainer/policy/mean Std 0.74184 +trainer/policy/mean Max 0.998732 +trainer/policy/mean Min -0.999737 +trainer/policy/std Mean 0.411015 +trainer/policy/std Std 0.0205823 +trainer/policy/std Max 0.432723 +trainer/policy/std Min 0.382814 +trainer/Advantage Weights Mean 3.76978 +trainer/Advantage Weights Std 16.6695 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.59934e-11 +trainer/Advantage Score Mean -0.407288 +trainer/Advantage Score Std 0.517171 +trainer/Advantage Score Max 1.57053 +trainer/Advantage Score Min -2.43732 +trainer/V1 Predictions Mean -71.6351 +trainer/V1 Predictions Std 18.7249 +trainer/V1 Predictions Max -0.465488 +trainer/V1 Predictions Min -90.3511 +trainer/VF Loss 0.0639703 +expl/num steps total 900000 +expl/num paths total 1250 +expl/path length Mean 500 +expl/path length Std 197 +expl/path length Max 697 +expl/path length Min 303 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.022232 +expl/Actions Std 0.838263 +expl/Actions Max 2.31106 +expl/Actions Min -2.39323 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 820190 +eval/num paths total 913 +eval/path length Mean 585 +eval/path length Std 0 +eval/path length Max 585 +eval/path length Min 585 +eval/Rewards Mean 0.0017094 +eval/Rewards Std 0.0413096 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0287581 +eval/Actions Std 0.743431 +eval/Actions Max 0.999924 +eval/Actions Min -0.999837 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.32668e-06 +time/evaluation sampling (s) 2.42734 +time/exploration sampling (s) 2.53154 +time/logging (s) 0.00700497 +time/saving (s) 0.0130729 +time/training (s) 13.7156 +time/epoch (s) 18.6946 +time/total (s) 22667.4 +Epoch -101 +------------------------------ ---------------- +2022-05-16 00:20:37.451611 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -100 finished +------------------------------ ---------------- +epoch -100 +replay_buffer/size 999047 +trainer/num train calls 901000 +trainer/QF1 Loss 0.822128 +trainer/QF2 Loss 0.61431 +trainer/Policy Loss 56.3242 +trainer/Q1 Predictions Mean -74.4037 +trainer/Q1 Predictions Std 13.9366 +trainer/Q1 Predictions Max -0.157139 +trainer/Q1 Predictions Min -88.7555 +trainer/Q2 Predictions Mean -74.4235 +trainer/Q2 Predictions Std 13.9344 +trainer/Q2 Predictions Max -0.282607 +trainer/Q2 Predictions Min -88.7312 +trainer/Q Targets Mean -74.5057 +trainer/Q Targets Std 14.1028 +trainer/Q Targets Max -0.904804 +trainer/Q Targets Min -88.1303 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0245818 +trainer/policy/mean Std 0.731609 +trainer/policy/mean Max 0.999637 +trainer/policy/mean Min -0.999569 +trainer/policy/std Mean 0.409396 +trainer/policy/std Std 0.0217431 +trainer/policy/std Max 0.43523 +trainer/policy/std Min 0.380657 +trainer/Advantage Weights Mean 11.5976 +trainer/Advantage Weights Std 26.7953 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.58138e-18 +trainer/Advantage Score Mean -0.221442 +trainer/Advantage Score Std 0.726216 +trainer/Advantage Score Max 1.71045 +trainer/Advantage Score Min -4.09882 +trainer/V1 Predictions Mean -74.2169 +trainer/V1 Predictions Std 14.272 +trainer/V1 Predictions Max -0.625242 +trainer/V1 Predictions Min -88.2895 +trainer/VF Loss 0.103684 +expl/num steps total 901000 +expl/num paths total 1252 +expl/path length Mean 500 +expl/path length Std 137 +expl/path length Max 637 +expl/path length Min 363 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00957672 +expl/Actions Std 0.836998 +expl/Actions Max 2.4615 +expl/Actions Min -2.43922 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 821190 +eval/num paths total 914 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0324426 +eval/Actions Std 0.729478 +eval/Actions Max 0.999916 +eval/Actions Min -0.999883 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.87825e-06 +time/evaluation sampling (s) 2.53212 +time/exploration sampling (s) 2.44491 +time/logging (s) 0.0110104 +time/saving (s) 0.0343198 +time/training (s) 13.5501 +time/epoch (s) 18.5725 +time/total (s) 22686 +Epoch -100 +------------------------------ ---------------- +2022-05-16 00:20:56.137547 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -99 finished +------------------------------ ---------------- +epoch -99 +replay_buffer/size 999047 +trainer/num train calls 902000 +trainer/QF1 Loss 0.825053 +trainer/QF2 Loss 0.914216 +trainer/Policy Loss 32.941 +trainer/Q1 Predictions Mean -70.0726 +trainer/Q1 Predictions Std 20.2485 +trainer/Q1 Predictions Max -0.606766 +trainer/Q1 Predictions Min -87.264 +trainer/Q2 Predictions Mean -70.067 +trainer/Q2 Predictions Std 20.3507 +trainer/Q2 Predictions Max -0.896585 +trainer/Q2 Predictions Min -87.1293 +trainer/Q Targets Mean -70.4795 +trainer/Q Targets Std 20.0702 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.246 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0144114 +trainer/policy/mean Std 0.737115 +trainer/policy/mean Max 0.999307 +trainer/policy/mean Min -0.999006 +trainer/policy/std Mean 0.410554 +trainer/policy/std Std 0.0213506 +trainer/policy/std Max 0.433634 +trainer/policy/std Min 0.382583 +trainer/Advantage Weights Mean 5.73752 +trainer/Advantage Weights Std 19.3755 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.11945e-17 +trainer/Advantage Score Mean -0.356614 +trainer/Advantage Score Std 0.699123 +trainer/Advantage Score Max 2.00873 +trainer/Advantage Score Min -3.83928 +trainer/V1 Predictions Mean -70.1163 +trainer/V1 Predictions Std 20.3235 +trainer/V1 Predictions Max -1.01837 +trainer/V1 Predictions Min -87.1199 +trainer/VF Loss 0.0957815 +expl/num steps total 902000 +expl/num paths total 1253 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0555172 +expl/Actions Std 0.825541 +expl/Actions Max 2.29345 +expl/Actions Min -2.35394 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 821524 +eval/num paths total 915 +eval/path length Mean 334 +eval/path length Std 0 +eval/path length Max 334 +eval/path length Min 334 +eval/Rewards Mean 0.00299401 +eval/Rewards Std 0.0546356 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0211749 +eval/Actions Std 0.766876 +eval/Actions Max 0.999152 +eval/Actions Min -0.999504 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.7013e-06 +time/evaluation sampling (s) 2.49455 +time/exploration sampling (s) 2.61516 +time/logging (s) 0.00609831 +time/saving (s) 0.0142592 +time/training (s) 13.5294 +time/epoch (s) 18.6595 +time/total (s) 22704.6 +Epoch -99 +------------------------------ ---------------- +2022-05-16 00:21:14.698363 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -98 finished +------------------------------ ---------------- +epoch -98 +replay_buffer/size 999047 +trainer/num train calls 903000 +trainer/QF1 Loss 0.808123 +trainer/QF2 Loss 0.823823 +trainer/Policy Loss 3.18618 +trainer/Q1 Predictions Mean -72.5844 +trainer/Q1 Predictions Std 17.9977 +trainer/Q1 Predictions Max -0.212131 +trainer/Q1 Predictions Min -86.8601 +trainer/Q2 Predictions Mean -72.4363 +trainer/Q2 Predictions Std 17.9865 +trainer/Q2 Predictions Max -0.439327 +trainer/Q2 Predictions Min -86.82 +trainer/Q Targets Mean -72.2464 +trainer/Q Targets Std 18.1818 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0292 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0150754 +trainer/policy/mean Std 0.72873 +trainer/policy/mean Max 0.99975 +trainer/policy/mean Min -0.999428 +trainer/policy/std Mean 0.41055 +trainer/policy/std Std 0.0219627 +trainer/policy/std Max 0.436976 +trainer/policy/std Min 0.382217 +trainer/Advantage Weights Mean 1.49658 +trainer/Advantage Weights Std 10.2637 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.23502e-21 +trainer/Advantage Score Mean -0.604569 +trainer/Advantage Score Std 0.60185 +trainer/Advantage Score Max 1.42662 +trainer/Advantage Score Min -4.81432 +trainer/V1 Predictions Mean -71.994 +trainer/V1 Predictions Std 18.2127 +trainer/V1 Predictions Max 1.04149 +trainer/V1 Predictions Min -86.8068 +trainer/VF Loss 0.0817361 +expl/num steps total 903000 +expl/num paths total 1255 +expl/path length Mean 500 +expl/path length Std 324 +expl/path length Max 824 +expl/path length Min 176 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0522569 +expl/Actions Std 0.824905 +expl/Actions Max 2.35632 +expl/Actions Min -2.36218 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 822055 +eval/num paths total 916 +eval/path length Mean 531 +eval/path length Std 0 +eval/path length Max 531 +eval/path length Min 531 +eval/Rewards Mean 0.00188324 +eval/Rewards Std 0.0433554 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0407578 +eval/Actions Std 0.75847 +eval/Actions Max 0.999952 +eval/Actions Min -0.99979 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.69713e-06 +time/evaluation sampling (s) 2.56428 +time/exploration sampling (s) 2.64971 +time/logging (s) 0.00619578 +time/saving (s) 0.0157155 +time/training (s) 13.3075 +time/epoch (s) 18.5434 +time/total (s) 22723.2 +Epoch -98 +------------------------------ ---------------- +2022-05-16 00:21:33.427853 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -97 finished +------------------------------ ---------------- +epoch -97 +replay_buffer/size 999047 +trainer/num train calls 904000 +trainer/QF1 Loss 1.02597 +trainer/QF2 Loss 0.93903 +trainer/Policy Loss 17.408 +trainer/Q1 Predictions Mean -70.4852 +trainer/Q1 Predictions Std 19.6251 +trainer/Q1 Predictions Max -1.89379 +trainer/Q1 Predictions Min -87.6038 +trainer/Q2 Predictions Mean -70.4867 +trainer/Q2 Predictions Std 19.7533 +trainer/Q2 Predictions Max -2.22577 +trainer/Q2 Predictions Min -87.6523 +trainer/Q Targets Mean -70.2614 +trainer/Q Targets Std 20.0034 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7197 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0291601 +trainer/policy/mean Std 0.722513 +trainer/policy/mean Max 0.999707 +trainer/policy/mean Min -0.999543 +trainer/policy/std Mean 0.410833 +trainer/policy/std Std 0.0217713 +trainer/policy/std Max 0.435208 +trainer/policy/std Min 0.378453 +trainer/Advantage Weights Mean 5.16115 +trainer/Advantage Weights Std 19.9458 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.38918e-18 +trainer/Advantage Score Mean -0.46103 +trainer/Advantage Score Std 0.737706 +trainer/Advantage Score Max 1.61306 +trainer/Advantage Score Min -4.11178 +trainer/V1 Predictions Mean -69.902 +trainer/V1 Predictions Std 20.2 +trainer/V1 Predictions Max 0.837179 +trainer/V1 Predictions Min -87.5183 +trainer/VF Loss 0.101305 +expl/num steps total 904000 +expl/num paths total 1256 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0244584 +expl/Actions Std 0.797796 +expl/Actions Max 2.35508 +expl/Actions Min -2.44657 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 822815 +eval/num paths total 917 +eval/path length Mean 760 +eval/path length Std 0 +eval/path length Max 760 +eval/path length Min 760 +eval/Rewards Mean 0.00131579 +eval/Rewards Std 0.0362499 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.027124 +eval/Actions Std 0.721242 +eval/Actions Max 0.999873 +eval/Actions Min -0.999861 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.59792e-06 +time/evaluation sampling (s) 2.52039 +time/exploration sampling (s) 2.68831 +time/logging (s) 0.00614574 +time/saving (s) 0.00978924 +time/training (s) 13.493 +time/epoch (s) 18.7176 +time/total (s) 22741.9 +Epoch -97 +------------------------------ ---------------- +2022-05-16 00:21:51.923737 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -96 finished +------------------------------ ---------------- +epoch -96 +replay_buffer/size 999047 +trainer/num train calls 905000 +trainer/QF1 Loss 0.605173 +trainer/QF2 Loss 0.472022 +trainer/Policy Loss 24.7317 +trainer/Q1 Predictions Mean -71.3374 +trainer/Q1 Predictions Std 18.1705 +trainer/Q1 Predictions Max -0.718067 +trainer/Q1 Predictions Min -86.4518 +trainer/Q2 Predictions Mean -71.3382 +trainer/Q2 Predictions Std 18.1821 +trainer/Q2 Predictions Max 0.167712 +trainer/Q2 Predictions Min -86.5261 +trainer/Q Targets Mean -71.2415 +trainer/Q Targets Std 18.2168 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4083 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00364163 +trainer/policy/mean Std 0.728781 +trainer/policy/mean Max 0.999136 +trainer/policy/mean Min -0.998361 +trainer/policy/std Mean 0.412734 +trainer/policy/std Std 0.0200484 +trainer/policy/std Max 0.433909 +trainer/policy/std Min 0.384524 +trainer/Advantage Weights Mean 5.45204 +trainer/Advantage Weights Std 19.0579 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.31899e-18 +trainer/Advantage Score Mean -0.389612 +trainer/Advantage Score Std 0.706938 +trainer/Advantage Score Max 2.22269 +trainer/Advantage Score Min -3.92145 +trainer/V1 Predictions Mean -70.9818 +trainer/V1 Predictions Std 18.4013 +trainer/V1 Predictions Max -0.0465319 +trainer/V1 Predictions Min -86.2692 +trainer/VF Loss 0.0978204 +expl/num steps total 905000 +expl/num paths total 1258 +expl/path length Mean 500 +expl/path length Std 377 +expl/path length Max 877 +expl/path length Min 123 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0406359 +expl/Actions Std 0.84323 +expl/Actions Max 2.24952 +expl/Actions Min -2.19038 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 823815 +eval/num paths total 918 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0507443 +eval/Actions Std 0.774036 +eval/Actions Max 0.999771 +eval/Actions Min -0.999546 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.02192e-06 +time/evaluation sampling (s) 2.49238 +time/exploration sampling (s) 2.74619 +time/logging (s) 0.00864411 +time/saving (s) 0.0122866 +time/training (s) 13.2263 +time/epoch (s) 18.4858 +time/total (s) 22760.4 +Epoch -96 +------------------------------ ---------------- +2022-05-16 00:22:11.128614 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -95 finished +------------------------------ ---------------- +epoch -95 +replay_buffer/size 999047 +trainer/num train calls 906000 +trainer/QF1 Loss 0.822731 +trainer/QF2 Loss 0.876588 +trainer/Policy Loss 40.1895 +trainer/Q1 Predictions Mean -69.8651 +trainer/Q1 Predictions Std 19.9821 +trainer/Q1 Predictions Max -0.611361 +trainer/Q1 Predictions Min -86.8546 +trainer/Q2 Predictions Mean -69.7751 +trainer/Q2 Predictions Std 20.0039 +trainer/Q2 Predictions Max -0.0292163 +trainer/Q2 Predictions Min -86.547 +trainer/Q Targets Mean -70.1452 +trainer/Q Targets Std 19.9074 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3327 +trainer/rewards Mean -0.980469 +trainer/rewards Std 0.138383 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0195312 +trainer/terminals Std 0.138383 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0155535 +trainer/policy/mean Std 0.723648 +trainer/policy/mean Max 0.999707 +trainer/policy/mean Min -0.999622 +trainer/policy/std Mean 0.411926 +trainer/policy/std Std 0.0209949 +trainer/policy/std Max 0.434264 +trainer/policy/std Min 0.380965 +trainer/Advantage Weights Mean 6.86396 +trainer/Advantage Weights Std 24.0133 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.42785e-14 +trainer/Advantage Score Mean -0.34377 +trainer/Advantage Score Std 0.664208 +trainer/Advantage Score Max 3.68864 +trainer/Advantage Score Min -2.99925 +trainer/V1 Predictions Mean -69.8943 +trainer/V1 Predictions Std 19.9053 +trainer/V1 Predictions Max 0.127577 +trainer/V1 Predictions Min -87.176 +trainer/VF Loss 0.171178 +expl/num steps total 906000 +expl/num paths total 1260 +expl/path length Mean 500 +expl/path length Std 236 +expl/path length Max 736 +expl/path length Min 264 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.042477 +expl/Actions Std 0.842569 +expl/Actions Max 2.603 +expl/Actions Min -2.42653 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 824815 +eval/num paths total 919 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.122229 +eval/Actions Std 0.739866 +eval/Actions Max 0.999655 +eval/Actions Min -0.99992 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.1637e-06 +time/evaluation sampling (s) 2.62081 +time/exploration sampling (s) 2.92245 +time/logging (s) 0.010249 +time/saving (s) 0.0148849 +time/training (s) 13.6236 +time/epoch (s) 19.192 +time/total (s) 22779.6 +Epoch -95 +------------------------------ ---------------- +2022-05-16 00:22:29.879392 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -94 finished +------------------------------ ---------------- +epoch -94 +replay_buffer/size 999047 +trainer/num train calls 907000 +trainer/QF1 Loss 8.52091 +trainer/QF2 Loss 8.59123 +trainer/Policy Loss 23.5437 +trainer/Q1 Predictions Mean -71.7451 +trainer/Q1 Predictions Std 17.579 +trainer/Q1 Predictions Max -0.393322 +trainer/Q1 Predictions Min -90.7483 +trainer/Q2 Predictions Mean -71.8144 +trainer/Q2 Predictions Std 17.4865 +trainer/Q2 Predictions Max -0.668657 +trainer/Q2 Predictions Min -91.5336 +trainer/Q Targets Mean -71.7407 +trainer/Q Targets Std 17.86 +trainer/Q Targets Max 0 +trainer/Q Targets Min -91.6924 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0013141 +trainer/policy/mean Std 0.742115 +trainer/policy/mean Max 0.999941 +trainer/policy/mean Min -0.999736 +trainer/policy/std Mean 0.410922 +trainer/policy/std Std 0.0205731 +trainer/policy/std Max 0.43445 +trainer/policy/std Min 0.382841 +trainer/Advantage Weights Mean 6.37105 +trainer/Advantage Weights Std 20.8183 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.8189e-15 +trainer/Advantage Score Mean -0.301333 +trainer/Advantage Score Std 0.631877 +trainer/Advantage Score Max 2.48865 +trainer/Advantage Score Min -3.39405 +trainer/V1 Predictions Mean -71.6323 +trainer/V1 Predictions Std 17.8043 +trainer/V1 Predictions Max -0.78905 +trainer/V1 Predictions Min -91.6979 +trainer/VF Loss 0.0934057 +expl/num steps total 907000 +expl/num paths total 1261 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0693497 +expl/Actions Std 0.84054 +expl/Actions Max 2.3697 +expl/Actions Min -2.52226 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 825815 +eval/num paths total 920 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.124682 +eval/Actions Std 0.709187 +eval/Actions Max 0.999661 +eval/Actions Min -0.999718 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.91178e-06 +time/evaluation sampling (s) 2.41151 +time/exploration sampling (s) 2.70545 +time/logging (s) 0.00742861 +time/saving (s) 0.0102614 +time/training (s) 13.5933 +time/epoch (s) 18.7279 +time/total (s) 22798.3 +Epoch -94 +------------------------------ ---------------- +2022-05-16 00:22:48.064901 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -93 finished +------------------------------ ---------------- +epoch -93 +replay_buffer/size 999047 +trainer/num train calls 908000 +trainer/QF1 Loss 0.445509 +trainer/QF2 Loss 0.51292 +trainer/Policy Loss 25.9151 +trainer/Q1 Predictions Mean -72.0731 +trainer/Q1 Predictions Std 18.3329 +trainer/Q1 Predictions Max 0.0898143 +trainer/Q1 Predictions Min -86.3496 +trainer/Q2 Predictions Mean -72.0742 +trainer/Q2 Predictions Std 18.4025 +trainer/Q2 Predictions Max -0.118479 +trainer/Q2 Predictions Min -86.4121 +trainer/Q Targets Mean -72.2598 +trainer/Q Targets Std 18.5018 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6909 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0115375 +trainer/policy/mean Std 0.732814 +trainer/policy/mean Max 0.999707 +trainer/policy/mean Min -0.998682 +trainer/policy/std Mean 0.411225 +trainer/policy/std Std 0.0212084 +trainer/policy/std Max 0.433174 +trainer/policy/std Min 0.381043 +trainer/Advantage Weights Mean 6.39538 +trainer/Advantage Weights Std 19.7698 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.22879e-20 +trainer/Advantage Score Mean -0.315188 +trainer/Advantage Score Std 0.728833 +trainer/Advantage Score Max 1.84742 +trainer/Advantage Score Min -4.52502 +trainer/V1 Predictions Mean -71.9863 +trainer/V1 Predictions Std 18.6327 +trainer/V1 Predictions Max 0.447731 +trainer/V1 Predictions Min -86.5553 +trainer/VF Loss 0.108429 +expl/num steps total 908000 +expl/num paths total 1263 +expl/path length Mean 500 +expl/path length Std 401 +expl/path length Max 901 +expl/path length Min 99 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0237507 +expl/Actions Std 0.8268 +expl/Actions Max 2.40024 +expl/Actions Min -2.27433 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 826398 +eval/num paths total 921 +eval/path length Mean 583 +eval/path length Std 0 +eval/path length Max 583 +eval/path length Min 583 +eval/Rewards Mean 0.00171527 +eval/Rewards Std 0.0413802 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00658718 +eval/Actions Std 0.746112 +eval/Actions Max 0.999959 +eval/Actions Min -0.99983 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.77627e-06 +time/evaluation sampling (s) 2.41448 +time/exploration sampling (s) 2.67645 +time/logging (s) 0.00886819 +time/saving (s) 0.015025 +time/training (s) 13.0581 +time/epoch (s) 18.1729 +time/total (s) 22816.5 +Epoch -93 +------------------------------ ---------------- +2022-05-16 00:23:07.102844 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -92 finished +------------------------------ ---------------- +epoch -92 +replay_buffer/size 999047 +trainer/num train calls 909000 +trainer/QF1 Loss 0.952668 +trainer/QF2 Loss 1.02502 +trainer/Policy Loss 30.075 +trainer/Q1 Predictions Mean -71.5353 +trainer/Q1 Predictions Std 18.4447 +trainer/Q1 Predictions Max -1.18906 +trainer/Q1 Predictions Min -86.8521 +trainer/Q2 Predictions Mean -71.4811 +trainer/Q2 Predictions Std 18.4074 +trainer/Q2 Predictions Max -1.1664 +trainer/Q2 Predictions Min -87.0129 +trainer/Q Targets Mean -71.4196 +trainer/Q Targets Std 18.6308 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.718 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00536651 +trainer/policy/mean Std 0.72065 +trainer/policy/mean Max 0.998418 +trainer/policy/mean Min -0.998927 +trainer/policy/std Mean 0.409435 +trainer/policy/std Std 0.0197892 +trainer/policy/std Max 0.42938 +trainer/policy/std Min 0.38234 +trainer/Advantage Weights Mean 5.67025 +trainer/Advantage Weights Std 20.1454 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.22383e-14 +trainer/Advantage Score Mean -0.386921 +trainer/Advantage Score Std 0.673807 +trainer/Advantage Score Max 1.80387 +trainer/Advantage Score Min -3.1437 +trainer/V1 Predictions Mean -71.2211 +trainer/V1 Predictions Std 18.7226 +trainer/V1 Predictions Max 1.44846 +trainer/V1 Predictions Min -86.6177 +trainer/VF Loss 0.0993038 +expl/num steps total 909000 +expl/num paths total 1264 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.215602 +expl/Actions Std 0.856883 +expl/Actions Max 2.61681 +expl/Actions Min -2.26314 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 826870 +eval/num paths total 922 +eval/path length Mean 472 +eval/path length Std 0 +eval/path length Max 472 +eval/path length Min 472 +eval/Rewards Mean 0.00211864 +eval/Rewards Std 0.0459799 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.053048 +eval/Actions Std 0.746011 +eval/Actions Max 0.999516 +eval/Actions Min -0.999221 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.8871e-06 +time/evaluation sampling (s) 2.43005 +time/exploration sampling (s) 2.69072 +time/logging (s) 0.0101989 +time/saving (s) 0.0191951 +time/training (s) 13.8716 +time/epoch (s) 19.0217 +time/total (s) 22835.5 +Epoch -92 +------------------------------ ---------------- +2022-05-16 00:23:26.275797 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -91 finished +------------------------------ ---------------- +epoch -91 +replay_buffer/size 999047 +trainer/num train calls 910000 +trainer/QF1 Loss 0.646406 +trainer/QF2 Loss 0.584217 +trainer/Policy Loss 15.7755 +trainer/Q1 Predictions Mean -72.2701 +trainer/Q1 Predictions Std 18.8989 +trainer/Q1 Predictions Max -0.835723 +trainer/Q1 Predictions Min -86.9085 +trainer/Q2 Predictions Mean -72.3252 +trainer/Q2 Predictions Std 18.8835 +trainer/Q2 Predictions Max -1.70893 +trainer/Q2 Predictions Min -87.0405 +trainer/Q Targets Mean -71.9671 +trainer/Q Targets Std 18.7271 +trainer/Q Targets Max -1.08102 +trainer/Q Targets Min -86.6311 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00802261 +trainer/policy/mean Std 0.733014 +trainer/policy/mean Max 0.999795 +trainer/policy/mean Min -0.999728 +trainer/policy/std Mean 0.410185 +trainer/policy/std Std 0.0205347 +trainer/policy/std Max 0.432715 +trainer/policy/std Min 0.381948 +trainer/Advantage Weights Mean 3.47317 +trainer/Advantage Weights Std 17.3388 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.52846e-11 +trainer/Advantage Score Mean -0.572546 +trainer/Advantage Score Std 0.489746 +trainer/Advantage Score Max 0.973194 +trainer/Advantage Score Min -2.38181 +trainer/V1 Predictions Mean -71.7543 +trainer/V1 Predictions Std 18.8443 +trainer/V1 Predictions Max -0.86218 +trainer/V1 Predictions Min -86.3876 +trainer/VF Loss 0.0694919 +expl/num steps total 910000 +expl/num paths total 1266 +expl/path length Mean 500 +expl/path length Std 304 +expl/path length Max 804 +expl/path length Min 196 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0163345 +expl/Actions Std 0.82639 +expl/Actions Max 2.27013 +expl/Actions Min -2.30919 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 827870 +eval/num paths total 923 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0182356 +eval/Actions Std 0.75209 +eval/Actions Max 0.999699 +eval/Actions Min -0.999738 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.48202e-06 +time/evaluation sampling (s) 2.51675 +time/exploration sampling (s) 2.75051 +time/logging (s) 0.00837034 +time/saving (s) 0.0133659 +time/training (s) 13.8597 +time/epoch (s) 19.1487 +time/total (s) 22854.7 +Epoch -91 +------------------------------ ---------------- +2022-05-16 00:23:45.210261 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -90 finished +------------------------------ ---------------- +epoch -90 +replay_buffer/size 999047 +trainer/num train calls 911000 +trainer/QF1 Loss 0.800831 +trainer/QF2 Loss 0.803854 +trainer/Policy Loss 26.8103 +trainer/Q1 Predictions Mean -71.4442 +trainer/Q1 Predictions Std 18.2573 +trainer/Q1 Predictions Max -0.612806 +trainer/Q1 Predictions Min -87.3034 +trainer/Q2 Predictions Mean -71.5465 +trainer/Q2 Predictions Std 18.2265 +trainer/Q2 Predictions Max -0.944963 +trainer/Q2 Predictions Min -87.3231 +trainer/Q Targets Mean -71.7313 +trainer/Q Targets Std 18.3477 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5871 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0175851 +trainer/policy/mean Std 0.735126 +trainer/policy/mean Max 0.999159 +trainer/policy/mean Min -0.999564 +trainer/policy/std Mean 0.411685 +trainer/policy/std Std 0.0197857 +trainer/policy/std Max 0.431953 +trainer/policy/std Min 0.382375 +trainer/Advantage Weights Mean 5.76592 +trainer/Advantage Weights Std 19.9592 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.80364e-16 +trainer/Advantage Score Mean -0.316218 +trainer/Advantage Score Std 0.583428 +trainer/Advantage Score Max 1.67905 +trainer/Advantage Score Min -3.5272 +trainer/V1 Predictions Mean -71.54 +trainer/V1 Predictions Std 18.3024 +trainer/V1 Predictions Max -1.27882 +trainer/V1 Predictions Min -87.5662 +trainer/VF Loss 0.0673585 +expl/num steps total 911000 +expl/num paths total 1267 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0136779 +expl/Actions Std 0.823231 +expl/Actions Max 2.53493 +expl/Actions Min -2.33092 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 828870 +eval/num paths total 924 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.201453 +eval/Actions Std 0.72277 +eval/Actions Max 0.999413 +eval/Actions Min -0.999451 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94577e-06 +time/evaluation sampling (s) 2.55499 +time/exploration sampling (s) 2.67904 +time/logging (s) 0.00795848 +time/saving (s) 0.0118949 +time/training (s) 13.6653 +time/epoch (s) 18.9192 +time/total (s) 22873.6 +Epoch -90 +------------------------------ ---------------- +2022-05-16 00:24:04.123336 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -89 finished +------------------------------ ---------------- +epoch -89 +replay_buffer/size 999047 +trainer/num train calls 912000 +trainer/QF1 Loss 0.601365 +trainer/QF2 Loss 0.553557 +trainer/Policy Loss 23.9587 +trainer/Q1 Predictions Mean -71.9396 +trainer/Q1 Predictions Std 18.1459 +trainer/Q1 Predictions Max 0.388279 +trainer/Q1 Predictions Min -86.8386 +trainer/Q2 Predictions Mean -71.8491 +trainer/Q2 Predictions Std 18.2073 +trainer/Q2 Predictions Max 0.118208 +trainer/Q2 Predictions Min -86.3879 +trainer/Q Targets Mean -71.9612 +trainer/Q Targets Std 18.1125 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7348 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00386171 +trainer/policy/mean Std 0.727883 +trainer/policy/mean Max 0.998602 +trainer/policy/mean Min -0.99936 +trainer/policy/std Mean 0.412998 +trainer/policy/std Std 0.0212639 +trainer/policy/std Max 0.435079 +trainer/policy/std Min 0.382026 +trainer/Advantage Weights Mean 6.297 +trainer/Advantage Weights Std 21.0353 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.50168e-15 +trainer/Advantage Score Mean -0.360172 +trainer/Advantage Score Std 0.673642 +trainer/Advantage Score Max 1.80522 +trainer/Advantage Score Min -3.41322 +trainer/V1 Predictions Mean -71.6747 +trainer/V1 Predictions Std 18.2808 +trainer/V1 Predictions Max -0.0520294 +trainer/V1 Predictions Min -86.7607 +trainer/VF Loss 0.0888867 +expl/num steps total 912000 +expl/num paths total 1268 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.436554 +expl/Actions Std 0.800064 +expl/Actions Max 2.20251 +expl/Actions Min -2.29283 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 829205 +eval/num paths total 925 +eval/path length Mean 335 +eval/path length Std 0 +eval/path length Max 335 +eval/path length Min 335 +eval/Rewards Mean 0.00298507 +eval/Rewards Std 0.0545542 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.000921732 +eval/Actions Std 0.776986 +eval/Actions Max 0.999669 +eval/Actions Min -0.999895 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.14089e-06 +time/evaluation sampling (s) 2.3845 +time/exploration sampling (s) 2.77462 +time/logging (s) 0.00582199 +time/saving (s) 0.0139714 +time/training (s) 13.7174 +time/epoch (s) 18.8963 +time/total (s) 22892.5 +Epoch -89 +------------------------------ ---------------- +2022-05-16 00:24:22.526327 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -88 finished +------------------------------ ---------------- +epoch -88 +replay_buffer/size 999047 +trainer/num train calls 913000 +trainer/QF1 Loss 0.568688 +trainer/QF2 Loss 0.45577 +trainer/Policy Loss 34.1794 +trainer/Q1 Predictions Mean -73.7483 +trainer/Q1 Predictions Std 15.0906 +trainer/Q1 Predictions Max -0.220059 +trainer/Q1 Predictions Min -86.6805 +trainer/Q2 Predictions Mean -73.7595 +trainer/Q2 Predictions Std 15.0545 +trainer/Q2 Predictions Max -0.281688 +trainer/Q2 Predictions Min -86.7494 +trainer/Q Targets Mean -73.8954 +trainer/Q Targets Std 15.2046 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0674 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0087338 +trainer/policy/mean Std 0.738246 +trainer/policy/mean Max 0.998893 +trainer/policy/mean Min -0.998297 +trainer/policy/std Mean 0.412469 +trainer/policy/std Std 0.0210687 +trainer/policy/std Max 0.435633 +trainer/policy/std Min 0.384414 +trainer/Advantage Weights Mean 7.55321 +trainer/Advantage Weights Std 22.7895 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.05659e-19 +trainer/Advantage Score Mean -0.276455 +trainer/Advantage Score Std 0.637143 +trainer/Advantage Score Max 1.79076 +trainer/Advantage Score Min -4.36941 +trainer/V1 Predictions Mean -73.6769 +trainer/V1 Predictions Std 15.1693 +trainer/V1 Predictions Max 0.292125 +trainer/V1 Predictions Min -86.8279 +trainer/VF Loss 0.0819014 +expl/num steps total 913000 +expl/num paths total 1270 +expl/path length Mean 500 +expl/path length Std 46 +expl/path length Max 546 +expl/path length Min 454 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0321028 +expl/Actions Std 0.840161 +expl/Actions Max 2.44238 +expl/Actions Min -2.25986 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 830205 +eval/num paths total 926 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.186208 +eval/Actions Std 0.754871 +eval/Actions Max 0.999686 +eval/Actions Min -0.999761 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.82517e-06 +time/evaluation sampling (s) 2.45371 +time/exploration sampling (s) 2.69457 +time/logging (s) 0.00802849 +time/saving (s) 0.0191246 +time/training (s) 13.2141 +time/epoch (s) 18.3896 +time/total (s) 22910.9 +Epoch -88 +------------------------------ ---------------- +2022-05-16 00:24:41.713243 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -87 finished +------------------------------ ---------------- +epoch -87 +replay_buffer/size 999047 +trainer/num train calls 914000 +trainer/QF1 Loss 3.83462 +trainer/QF2 Loss 3.86449 +trainer/Policy Loss 18.4889 +trainer/Q1 Predictions Mean -72.3625 +trainer/Q1 Predictions Std 17.6787 +trainer/Q1 Predictions Max -1.18744 +trainer/Q1 Predictions Min -87.3918 +trainer/Q2 Predictions Mean -72.19 +trainer/Q2 Predictions Std 17.6666 +trainer/Q2 Predictions Max -1.44287 +trainer/Q2 Predictions Min -87.1037 +trainer/Q Targets Mean -72.1331 +trainer/Q Targets Std 17.7417 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6084 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00129649 +trainer/policy/mean Std 0.728038 +trainer/policy/mean Max 0.998033 +trainer/policy/mean Min -0.999954 +trainer/policy/std Mean 0.411802 +trainer/policy/std Std 0.02021 +trainer/policy/std Max 0.432947 +trainer/policy/std Min 0.384616 +trainer/Advantage Weights Mean 5.78668 +trainer/Advantage Weights Std 21.2437 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.5239e-21 +trainer/Advantage Score Mean -0.392435 +trainer/Advantage Score Std 0.745686 +trainer/Advantage Score Max 2.04252 +trainer/Advantage Score Min -4.66452 +trainer/V1 Predictions Mean -72.01 +trainer/V1 Predictions Std 17.7889 +trainer/V1 Predictions Max -0.328587 +trainer/V1 Predictions Min -87.4937 +trainer/VF Loss 0.121073 +expl/num steps total 914000 +expl/num paths total 1271 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.011499 +expl/Actions Std 0.850953 +expl/Actions Max 2.24974 +expl/Actions Min -2.48571 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 830962 +eval/num paths total 927 +eval/path length Mean 757 +eval/path length Std 0 +eval/path length Max 757 +eval/path length Min 757 +eval/Rewards Mean 0.001321 +eval/Rewards Std 0.0363216 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0385507 +eval/Actions Std 0.745314 +eval/Actions Max 0.99991 +eval/Actions Min -0.999818 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 9.02219e-06 +time/evaluation sampling (s) 2.44551 +time/exploration sampling (s) 2.83463 +time/logging (s) 0.00993733 +time/saving (s) 0.0162308 +time/training (s) 13.867 +time/epoch (s) 19.1734 +time/total (s) 22930.1 +Epoch -87 +------------------------------ ---------------- +2022-05-16 00:25:00.737710 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -86 finished +------------------------------ ---------------- +epoch -86 +replay_buffer/size 999047 +trainer/num train calls 915000 +trainer/QF1 Loss 0.759895 +trainer/QF2 Loss 0.716486 +trainer/Policy Loss 10.3243 +trainer/Q1 Predictions Mean -71.3739 +trainer/Q1 Predictions Std 18.7261 +trainer/Q1 Predictions Max -1.4561 +trainer/Q1 Predictions Min -87.414 +trainer/Q2 Predictions Mean -71.2936 +trainer/Q2 Predictions Std 18.7759 +trainer/Q2 Predictions Max -1.74001 +trainer/Q2 Predictions Min -87.3945 +trainer/Q Targets Mean -71.374 +trainer/Q Targets Std 18.8388 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7431 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00919467 +trainer/policy/mean Std 0.729296 +trainer/policy/mean Max 0.999645 +trainer/policy/mean Min -0.999268 +trainer/policy/std Mean 0.410436 +trainer/policy/std Std 0.0202841 +trainer/policy/std Max 0.433733 +trainer/policy/std Min 0.382528 +trainer/Advantage Weights Mean 2.59785 +trainer/Advantage Weights Std 13.9894 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.38672e-21 +trainer/Advantage Score Mean -0.499083 +trainer/Advantage Score Std 0.743158 +trainer/Advantage Score Max 1.12101 +trainer/Advantage Score Min -4.80273 +trainer/V1 Predictions Mean -71.0024 +trainer/V1 Predictions Std 19.0105 +trainer/V1 Predictions Max -1.92305 +trainer/V1 Predictions Min -87.4812 +trainer/VF Loss 0.0919531 +expl/num steps total 915000 +expl/num paths total 1273 +expl/path length Mean 500 +expl/path length Std 100 +expl/path length Max 600 +expl/path length Min 400 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0354096 +expl/Actions Std 0.849548 +expl/Actions Max 2.33142 +expl/Actions Min -2.27226 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 831462 +eval/num paths total 928 +eval/path length Mean 500 +eval/path length Std 0 +eval/path length Max 500 +eval/path length Min 500 +eval/Rewards Mean 0.002 +eval/Rewards Std 0.0446766 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0421801 +eval/Actions Std 0.746279 +eval/Actions Max 0.999829 +eval/Actions Min -0.999919 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.997e-06 +time/evaluation sampling (s) 2.44396 +time/exploration sampling (s) 2.73167 +time/logging (s) 0.00610417 +time/saving (s) 0.0131543 +time/training (s) 13.8074 +time/epoch (s) 19.0023 +time/total (s) 22949.1 +Epoch -86 +------------------------------ ---------------- +2022-05-16 00:25:19.629494 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -85 finished +------------------------------ ---------------- +epoch -85 +replay_buffer/size 999047 +trainer/num train calls 916000 +trainer/QF1 Loss 0.84975 +trainer/QF2 Loss 0.619365 +trainer/Policy Loss 13.8965 +trainer/Q1 Predictions Mean -70.9509 +trainer/Q1 Predictions Std 18.5839 +trainer/Q1 Predictions Max -2.19519 +trainer/Q1 Predictions Min -87.2233 +trainer/Q2 Predictions Mean -70.8697 +trainer/Q2 Predictions Std 18.5821 +trainer/Q2 Predictions Max -1.67256 +trainer/Q2 Predictions Min -87.0152 +trainer/Q Targets Mean -70.5916 +trainer/Q Targets Std 18.7223 +trainer/Q Targets Max -1.48114 +trainer/Q Targets Min -86.9806 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0162253 +trainer/policy/mean Std 0.721702 +trainer/policy/mean Max 0.99881 +trainer/policy/mean Min -0.998162 +trainer/policy/std Mean 0.410285 +trainer/policy/std Std 0.0203179 +trainer/policy/std Max 0.430681 +trainer/policy/std Min 0.382534 +trainer/Advantage Weights Mean 1.66734 +trainer/Advantage Weights Std 12.392 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.97701e-21 +trainer/Advantage Score Mean -0.655189 +trainer/Advantage Score Std 0.72196 +trainer/Advantage Score Max 0.643074 +trainer/Advantage Score Min -4.64117 +trainer/V1 Predictions Mean -70.3327 +trainer/V1 Predictions Std 18.8413 +trainer/V1 Predictions Max -1.98104 +trainer/V1 Predictions Min -86.9043 +trainer/VF Loss 0.0995627 +expl/num steps total 916000 +expl/num paths total 1275 +expl/path length Mean 500 +expl/path length Std 311 +expl/path length Max 811 +expl/path length Min 189 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0567735 +expl/Actions Std 0.811216 +expl/Actions Max 2.19956 +expl/Actions Min -2.56793 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 832462 +eval/num paths total 929 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.271771 +eval/Actions Std 0.673266 +eval/Actions Max 0.999836 +eval/Actions Min -0.999433 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93693e-06 +time/evaluation sampling (s) 2.42077 +time/exploration sampling (s) 2.72967 +time/logging (s) 0.00898847 +time/saving (s) 0.0150366 +time/training (s) 13.7086 +time/epoch (s) 18.8831 +time/total (s) 22968 +Epoch -85 +------------------------------ ---------------- +2022-05-16 00:25:38.444703 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -84 finished +------------------------------ ---------------- +epoch -84 +replay_buffer/size 999047 +trainer/num train calls 917000 +trainer/QF1 Loss 0.768231 +trainer/QF2 Loss 0.742644 +trainer/Policy Loss 33.7157 +trainer/Q1 Predictions Mean -71.568 +trainer/Q1 Predictions Std 18.6798 +trainer/Q1 Predictions Max -1.62342 +trainer/Q1 Predictions Min -89.9014 +trainer/Q2 Predictions Mean -71.5811 +trainer/Q2 Predictions Std 18.6634 +trainer/Q2 Predictions Max -0.980291 +trainer/Q2 Predictions Min -89.6179 +trainer/Q Targets Mean -71.5741 +trainer/Q Targets Std 18.7455 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.7883 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0135784 +trainer/policy/mean Std 0.732289 +trainer/policy/mean Max 0.99963 +trainer/policy/mean Min -0.998989 +trainer/policy/std Mean 0.409936 +trainer/policy/std Std 0.0218092 +trainer/policy/std Max 0.43399 +trainer/policy/std Min 0.378882 +trainer/Advantage Weights Mean 6.1978 +trainer/Advantage Weights Std 21.2112 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.27343e-15 +trainer/Advantage Score Mean -0.31805 +trainer/Advantage Score Std 0.593882 +trainer/Advantage Score Max 2.86857 +trainer/Advantage Score Min -3.24257 +trainer/V1 Predictions Mean -71.418 +trainer/V1 Predictions Std 18.6858 +trainer/V1 Predictions Max -0.698277 +trainer/V1 Predictions Min -89.4084 +trainer/VF Loss 0.10354 +expl/num steps total 917000 +expl/num paths total 1277 +expl/path length Mean 500 +expl/path length Std 426 +expl/path length Max 926 +expl/path length Min 74 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0568766 +expl/Actions Std 0.826902 +expl/Actions Max 2.4636 +expl/Actions Min -2.26738 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 833007 +eval/num paths total 930 +eval/path length Mean 545 +eval/path length Std 0 +eval/path length Max 545 +eval/path length Min 545 +eval/Rewards Mean 0.00183486 +eval/Rewards Std 0.042796 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0354092 +eval/Actions Std 0.746462 +eval/Actions Max 0.999589 +eval/Actions Min -0.999495 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.33227e-06 +time/evaluation sampling (s) 2.41103 +time/exploration sampling (s) 2.79304 +time/logging (s) 0.0063115 +time/saving (s) 0.0110697 +time/training (s) 13.5759 +time/epoch (s) 18.7974 +time/total (s) 22986.8 +Epoch -84 +------------------------------ ---------------- +2022-05-16 00:25:56.952179 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -83 finished +------------------------------ ---------------- +epoch -83 +replay_buffer/size 999047 +trainer/num train calls 918000 +trainer/QF1 Loss 1.34499 +trainer/QF2 Loss 1.28573 +trainer/Policy Loss 9.67008 +trainer/Q1 Predictions Mean -72.5609 +trainer/Q1 Predictions Std 18.4029 +trainer/Q1 Predictions Max -0.914227 +trainer/Q1 Predictions Min -88.486 +trainer/Q2 Predictions Mean -72.5609 +trainer/Q2 Predictions Std 18.3885 +trainer/Q2 Predictions Max -1.06691 +trainer/Q2 Predictions Min -88.8643 +trainer/Q Targets Mean -72.3554 +trainer/Q Targets Std 18.2313 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9548 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0283365 +trainer/policy/mean Std 0.733606 +trainer/policy/mean Max 0.999718 +trainer/policy/mean Min -0.99934 +trainer/policy/std Mean 0.410934 +trainer/policy/std Std 0.0212769 +trainer/policy/std Max 0.433157 +trainer/policy/std Min 0.379029 +trainer/Advantage Weights Mean 3.19579 +trainer/Advantage Weights Std 14.7398 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 7.06016e-16 +trainer/Advantage Score Mean -0.431466 +trainer/Advantage Score Std 0.622297 +trainer/Advantage Score Max 0.997082 +trainer/Advantage Score Min -3.48869 +trainer/V1 Predictions Mean -72.0538 +trainer/V1 Predictions Std 18.4473 +trainer/V1 Predictions Max 0.480579 +trainer/V1 Predictions Min -88.0161 +trainer/VF Loss 0.0704022 +expl/num steps total 918000 +expl/num paths total 1278 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0362641 +expl/Actions Std 0.84204 +expl/Actions Max 2.24975 +expl/Actions Min -2.33612 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 834007 +eval/num paths total 931 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00748772 +eval/Actions Std 0.730576 +eval/Actions Max 0.999912 +eval/Actions Min -0.999829 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.99606e-06 +time/evaluation sampling (s) 2.4303 +time/exploration sampling (s) 2.78329 +time/logging (s) 0.0110607 +time/saving (s) 0.0158376 +time/training (s) 13.2593 +time/epoch (s) 18.4998 +time/total (s) 23005.3 +Epoch -83 +------------------------------ ---------------- +2022-05-16 00:26:16.086487 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -82 finished +------------------------------ ---------------- +epoch -82 +replay_buffer/size 999047 +trainer/num train calls 919000 +trainer/QF1 Loss 0.97541 +trainer/QF2 Loss 0.818314 +trainer/Policy Loss 31.1006 +trainer/Q1 Predictions Mean -70.6659 +trainer/Q1 Predictions Std 18.5848 +trainer/Q1 Predictions Max -0.505013 +trainer/Q1 Predictions Min -86.7455 +trainer/Q2 Predictions Mean -70.7192 +trainer/Q2 Predictions Std 18.6551 +trainer/Q2 Predictions Max -0.160027 +trainer/Q2 Predictions Min -86.7554 +trainer/Q Targets Mean -70.7539 +trainer/Q Targets Std 18.7124 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.778 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0275963 +trainer/policy/mean Std 0.737834 +trainer/policy/mean Max 0.998938 +trainer/policy/mean Min -0.999806 +trainer/policy/std Mean 0.409618 +trainer/policy/std Std 0.0204201 +trainer/policy/std Max 0.432883 +trainer/policy/std Min 0.380046 +trainer/Advantage Weights Mean 6.71103 +trainer/Advantage Weights Std 22.584 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.04868e-14 +trainer/Advantage Score Mean -0.301405 +trainer/Advantage Score Std 0.582293 +trainer/Advantage Score Max 2.55783 +trainer/Advantage Score Min -3.11215 +trainer/V1 Predictions Mean -70.5158 +trainer/V1 Predictions Std 18.7949 +trainer/V1 Predictions Max 0.759049 +trainer/V1 Predictions Min -86.5796 +trainer/VF Loss 0.0957139 +expl/num steps total 919000 +expl/num paths total 1280 +expl/path length Mean 500 +expl/path length Std 357 +expl/path length Max 857 +expl/path length Min 143 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.040271 +expl/Actions Std 0.83574 +expl/Actions Max 2.55121 +expl/Actions Min -2.42783 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 835007 +eval/num paths total 932 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0206189 +eval/Actions Std 0.702391 +eval/Actions Max 0.999073 +eval/Actions Min -0.999818 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.24519e-06 +time/evaluation sampling (s) 2.37266 +time/exploration sampling (s) 2.76855 +time/logging (s) 0.0106738 +time/saving (s) 0.0156711 +time/training (s) 13.947 +time/epoch (s) 19.1146 +time/total (s) 23024.4 +Epoch -82 +------------------------------ ---------------- +2022-05-16 00:26:35.151785 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -81 finished +------------------------------ ---------------- +epoch -81 +replay_buffer/size 999047 +trainer/num train calls 920000 +trainer/QF1 Loss 0.854609 +trainer/QF2 Loss 0.908649 +trainer/Policy Loss 22.6735 +trainer/Q1 Predictions Mean -69.6206 +trainer/Q1 Predictions Std 19.0351 +trainer/Q1 Predictions Max -1.09254 +trainer/Q1 Predictions Min -89.8117 +trainer/Q2 Predictions Mean -69.6254 +trainer/Q2 Predictions Std 19.0238 +trainer/Q2 Predictions Max -0.957716 +trainer/Q2 Predictions Min -88.641 +trainer/Q Targets Mean -69.8402 +trainer/Q Targets Std 19.3888 +trainer/Q Targets Max 0.243073 +trainer/Q Targets Min -89.8259 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00373168 +trainer/policy/mean Std 0.725079 +trainer/policy/mean Max 0.999824 +trainer/policy/mean Min -0.999587 +trainer/policy/std Mean 0.410621 +trainer/policy/std Std 0.0202497 +trainer/policy/std Max 0.433722 +trainer/policy/std Min 0.382093 +trainer/Advantage Weights Mean 6.05408 +trainer/Advantage Weights Std 20.1034 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.56966e-30 +trainer/Advantage Score Mean -0.523661 +trainer/Advantage Score Std 0.908 +trainer/Advantage Score Max 1.31658 +trainer/Advantage Score Min -6.71951 +trainer/V1 Predictions Mean -69.5605 +trainer/V1 Predictions Std 19.4958 +trainer/V1 Predictions Max 0.954179 +trainer/V1 Predictions Min -90.73 +trainer/VF Loss 0.138423 +expl/num steps total 920000 +expl/num paths total 1282 +expl/path length Mean 500 +expl/path length Std 226 +expl/path length Max 726 +expl/path length Min 274 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00803742 +expl/Actions Std 0.846883 +expl/Actions Max 2.38349 +expl/Actions Min -2.40213 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 835431 +eval/num paths total 933 +eval/path length Mean 424 +eval/path length Std 0 +eval/path length Max 424 +eval/path length Min 424 +eval/Rewards Mean 0.00235849 +eval/Rewards Std 0.048507 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0157804 +eval/Actions Std 0.753518 +eval/Actions Max 0.999684 +eval/Actions Min -0.999504 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.89967e-06 +time/evaluation sampling (s) 2.67575 +time/exploration sampling (s) 2.93252 +time/logging (s) 0.00530728 +time/saving (s) 0.00972572 +time/training (s) 13.4165 +time/epoch (s) 19.0398 +time/total (s) 23043.4 +Epoch -81 +------------------------------ ---------------- +2022-05-16 00:26:54.005784 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -80 finished +------------------------------ ---------------- +epoch -80 +replay_buffer/size 999047 +trainer/num train calls 921000 +trainer/QF1 Loss 0.681905 +trainer/QF2 Loss 0.644764 +trainer/Policy Loss 27.1558 +trainer/Q1 Predictions Mean -72.0844 +trainer/Q1 Predictions Std 17.973 +trainer/Q1 Predictions Max -0.699426 +trainer/Q1 Predictions Min -91.0808 +trainer/Q2 Predictions Mean -72.0131 +trainer/Q2 Predictions Std 18.0386 +trainer/Q2 Predictions Max -0.353821 +trainer/Q2 Predictions Min -90.9327 +trainer/Q Targets Mean -72.0555 +trainer/Q Targets Std 18.0269 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.7168 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00138067 +trainer/policy/mean Std 0.730953 +trainer/policy/mean Max 0.999717 +trainer/policy/mean Min -0.999812 +trainer/policy/std Mean 0.41126 +trainer/policy/std Std 0.0192501 +trainer/policy/std Max 0.43222 +trainer/policy/std Min 0.383873 +trainer/Advantage Weights Mean 7.05793 +trainer/Advantage Weights Std 21.0095 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.2984e-25 +trainer/Advantage Score Mean -0.345843 +trainer/Advantage Score Std 0.772763 +trainer/Advantage Score Max 1.46054 +trainer/Advantage Score Min -5.73035 +trainer/V1 Predictions Mean -71.8067 +trainer/V1 Predictions Std 18.2163 +trainer/V1 Predictions Max -0.728813 +trainer/V1 Predictions Min -91.1731 +trainer/VF Loss 0.0996404 +expl/num steps total 921000 +expl/num paths total 1284 +expl/path length Mean 500 +expl/path length Std 164 +expl/path length Max 664 +expl/path length Min 336 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0300328 +expl/Actions Std 0.835206 +expl/Actions Max 2.4969 +expl/Actions Min -2.1463 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 836431 +eval/num paths total 934 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0886868 +eval/Actions Std 0.648534 +eval/Actions Max 0.999259 +eval/Actions Min -0.999864 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93693e-06 +time/evaluation sampling (s) 2.4047 +time/exploration sampling (s) 2.79849 +time/logging (s) 0.00823707 +time/saving (s) 0.0130434 +time/training (s) 13.6212 +time/epoch (s) 18.8457 +time/total (s) 23062.3 +Epoch -80 +------------------------------ ---------------- +2022-05-16 00:27:12.991060 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -79 finished +------------------------------ ---------------- +epoch -79 +replay_buffer/size 999047 +trainer/num train calls 922000 +trainer/QF1 Loss 0.875222 +trainer/QF2 Loss 0.793032 +trainer/Policy Loss 17.0059 +trainer/Q1 Predictions Mean -71.2427 +trainer/Q1 Predictions Std 20.3986 +trainer/Q1 Predictions Max -1.27948 +trainer/Q1 Predictions Min -89.2226 +trainer/Q2 Predictions Mean -71.3635 +trainer/Q2 Predictions Std 20.4007 +trainer/Q2 Predictions Max -1.34979 +trainer/Q2 Predictions Min -89.4585 +trainer/Q Targets Mean -71.4213 +trainer/Q Targets Std 20.4792 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.5539 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00167198 +trainer/policy/mean Std 0.747792 +trainer/policy/mean Max 0.999823 +trainer/policy/mean Min -0.999222 +trainer/policy/std Mean 0.40994 +trainer/policy/std Std 0.0198715 +trainer/policy/std Max 0.434435 +trainer/policy/std Min 0.381242 +trainer/Advantage Weights Mean 4.89582 +trainer/Advantage Weights Std 17.5134 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.35353e-18 +trainer/Advantage Score Mean -0.317912 +trainer/Advantage Score Std 0.638398 +trainer/Advantage Score Max 2.27917 +trainer/Advantage Score Min -4.11438 +trainer/V1 Predictions Mean -71.1803 +trainer/V1 Predictions Std 20.6157 +trainer/V1 Predictions Max -0.599844 +trainer/V1 Predictions Min -89.1659 +trainer/VF Loss 0.0884592 +expl/num steps total 922000 +expl/num paths total 1286 +expl/path length Mean 500 +expl/path length Std 285 +expl/path length Max 785 +expl/path length Min 215 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0510593 +expl/Actions Std 0.829774 +expl/Actions Max 2.27309 +expl/Actions Min -2.41501 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 837431 +eval/num paths total 935 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0878447 +eval/Actions Std 0.751559 +eval/Actions Max 0.999805 +eval/Actions Min -0.999834 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.88105e-06 +time/evaluation sampling (s) 2.39867 +time/exploration sampling (s) 3.00571 +time/logging (s) 0.0111284 +time/saving (s) 0.0172777 +time/training (s) 13.5403 +time/epoch (s) 18.9731 +time/total (s) 23081.3 +Epoch -79 +------------------------------ ---------------- +2022-05-16 00:27:31.715830 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -78 finished +------------------------------ ---------------- +epoch -78 +replay_buffer/size 999047 +trainer/num train calls 923000 +trainer/QF1 Loss 10.5705 +trainer/QF2 Loss 10.5112 +trainer/Policy Loss 37.6647 +trainer/Q1 Predictions Mean -72.3998 +trainer/Q1 Predictions Std 15.411 +trainer/Q1 Predictions Max 1.14034 +trainer/Q1 Predictions Min -85.9403 +trainer/Q2 Predictions Mean -72.3925 +trainer/Q2 Predictions Std 15.3134 +trainer/Q2 Predictions Max 0.349541 +trainer/Q2 Predictions Min -85.9869 +trainer/Q Targets Mean -72.9514 +trainer/Q Targets Std 15.4741 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.68 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0202821 +trainer/policy/mean Std 0.728408 +trainer/policy/mean Max 0.998498 +trainer/policy/mean Min -0.999307 +trainer/policy/std Mean 0.411463 +trainer/policy/std Std 0.0205538 +trainer/policy/std Max 0.434673 +trainer/policy/std Min 0.382561 +trainer/Advantage Weights Mean 9.85211 +trainer/Advantage Weights Std 25.1427 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.85074e-18 +trainer/Advantage Score Mean -0.289446 +trainer/Advantage Score Std 0.657535 +trainer/Advantage Score Max 1.50011 +trainer/Advantage Score Min -4.00983 +trainer/V1 Predictions Mean -72.8073 +trainer/V1 Predictions Std 15.471 +trainer/V1 Predictions Max -0.399854 +trainer/V1 Predictions Min -86.5049 +trainer/VF Loss 0.0979391 +expl/num steps total 923000 +expl/num paths total 1288 +expl/path length Mean 500 +expl/path length Std 354 +expl/path length Max 854 +expl/path length Min 146 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0186182 +expl/Actions Std 0.834199 +expl/Actions Max 2.36168 +expl/Actions Min -2.36265 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 838120 +eval/num paths total 936 +eval/path length Mean 689 +eval/path length Std 0 +eval/path length Max 689 +eval/path length Min 689 +eval/Rewards Mean 0.00145138 +eval/Rewards Std 0.0380693 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0230737 +eval/Actions Std 0.746959 +eval/Actions Max 0.999578 +eval/Actions Min -0.999694 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.10875e-06 +time/evaluation sampling (s) 2.35757 +time/exploration sampling (s) 2.92564 +time/logging (s) 0.00964114 +time/saving (s) 0.014913 +time/training (s) 13.3973 +time/epoch (s) 18.7051 +time/total (s) 23100 +Epoch -78 +------------------------------ ---------------- +2022-05-16 00:27:50.745881 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -77 finished +------------------------------ ---------------- +epoch -77 +replay_buffer/size 999047 +trainer/num train calls 924000 +trainer/QF1 Loss 0.741963 +trainer/QF2 Loss 0.673752 +trainer/Policy Loss 2.6757 +trainer/Q1 Predictions Mean -72.1574 +trainer/Q1 Predictions Std 17.3846 +trainer/Q1 Predictions Max -1.36627 +trainer/Q1 Predictions Min -86.795 +trainer/Q2 Predictions Mean -72.1985 +trainer/Q2 Predictions Std 17.2306 +trainer/Q2 Predictions Max -0.851724 +trainer/Q2 Predictions Min -86.5261 +trainer/Q Targets Mean -71.8284 +trainer/Q Targets Std 17.2559 +trainer/Q Targets Max 0 +trainer/Q Targets Min -85.9201 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00413477 +trainer/policy/mean Std 0.743241 +trainer/policy/mean Max 0.998872 +trainer/policy/mean Min -0.999673 +trainer/policy/std Mean 0.411768 +trainer/policy/std Std 0.0208601 +trainer/policy/std Max 0.434848 +trainer/policy/std Min 0.382908 +trainer/Advantage Weights Mean 0.968079 +trainer/Advantage Weights Std 7.83947 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.31124e-18 +trainer/Advantage Score Mean -0.61227 +trainer/Advantage Score Std 0.589154 +trainer/Advantage Score Max 0.643269 +trainer/Advantage Score Min -4.06088 +trainer/V1 Predictions Mean -71.5314 +trainer/V1 Predictions Std 17.3946 +trainer/V1 Predictions Max -0.994449 +trainer/V1 Predictions Min -85.9498 +trainer/VF Loss 0.074929 +expl/num steps total 924000 +expl/num paths total 1289 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0321417 +expl/Actions Std 0.828517 +expl/Actions Max 2.45255 +expl/Actions Min -2.18208 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 839120 +eval/num paths total 937 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.537528 +eval/Actions Std 0.567886 +eval/Actions Max 0.997674 +eval/Actions Min -0.999187 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.58908e-06 +time/evaluation sampling (s) 2.5501 +time/exploration sampling (s) 2.93681 +time/logging (s) 0.00747423 +time/saving (s) 0.0126293 +time/training (s) 13.5045 +time/epoch (s) 19.0115 +time/total (s) 23119 +Epoch -77 +------------------------------ ---------------- +2022-05-16 00:28:09.745162 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -76 finished +------------------------------ ---------------- +epoch -76 +replay_buffer/size 999047 +trainer/num train calls 925000 +trainer/QF1 Loss 0.837306 +trainer/QF2 Loss 0.692498 +trainer/Policy Loss 19.9207 +trainer/Q1 Predictions Mean -69.1761 +trainer/Q1 Predictions Std 19.8851 +trainer/Q1 Predictions Max -1.19692 +trainer/Q1 Predictions Min -91.2077 +trainer/Q2 Predictions Mean -69.174 +trainer/Q2 Predictions Std 19.8331 +trainer/Q2 Predictions Max -0.8658 +trainer/Q2 Predictions Min -91.5157 +trainer/Q Targets Mean -69.3912 +trainer/Q Targets Std 19.7422 +trainer/Q Targets Max -1.97463 +trainer/Q Targets Min -91.5506 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0242979 +trainer/policy/mean Std 0.734255 +trainer/policy/mean Max 0.998694 +trainer/policy/mean Min -0.999165 +trainer/policy/std Mean 0.412672 +trainer/policy/std Std 0.0195031 +trainer/policy/std Max 0.431282 +trainer/policy/std Min 0.384485 +trainer/Advantage Weights Mean 4.83007 +trainer/Advantage Weights Std 19.8671 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.48198e-23 +trainer/Advantage Score Mean -0.448544 +trainer/Advantage Score Std 0.681622 +trainer/Advantage Score Max 2.03354 +trainer/Advantage Score Min -5.10904 +trainer/V1 Predictions Mean -69.084 +trainer/V1 Predictions Std 19.9633 +trainer/V1 Predictions Max -0.954428 +trainer/V1 Predictions Min -91.694 +trainer/VF Loss 0.0997022 +expl/num steps total 925000 +expl/num paths total 1290 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0218321 +expl/Actions Std 0.81051 +expl/Actions Max 2.31731 +expl/Actions Min -2.24542 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 839523 +eval/num paths total 938 +eval/path length Mean 403 +eval/path length Std 0 +eval/path length Max 403 +eval/path length Min 403 +eval/Rewards Mean 0.00248139 +eval/Rewards Std 0.0497517 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0413272 +eval/Actions Std 0.746403 +eval/Actions Max 0.999706 +eval/Actions Min -0.99962 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.22005e-06 +time/evaluation sampling (s) 2.53162 +time/exploration sampling (s) 2.9122 +time/logging (s) 0.00533842 +time/saving (s) 0.0101939 +time/training (s) 13.5228 +time/epoch (s) 18.9822 +time/total (s) 23138 +Epoch -76 +------------------------------ ---------------- +2022-05-16 00:28:28.896634 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -75 finished +------------------------------ ---------------- +epoch -75 +replay_buffer/size 999047 +trainer/num train calls 926000 +trainer/QF1 Loss 1.11878 +trainer/QF2 Loss 1.12849 +trainer/Policy Loss 3.5681 +trainer/Q1 Predictions Mean -71.73 +trainer/Q1 Predictions Std 18.484 +trainer/Q1 Predictions Max -1.03307 +trainer/Q1 Predictions Min -86.7894 +trainer/Q2 Predictions Mean -71.8205 +trainer/Q2 Predictions Std 18.3689 +trainer/Q2 Predictions Max -1.22189 +trainer/Q2 Predictions Min -86.8746 +trainer/Q Targets Mean -71.3799 +trainer/Q Targets Std 18.4442 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4946 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00931321 +trainer/policy/mean Std 0.72272 +trainer/policy/mean Max 0.99887 +trainer/policy/mean Min -0.997706 +trainer/policy/std Mean 0.412169 +trainer/policy/std Std 0.0200123 +trainer/policy/std Max 0.433325 +trainer/policy/std Min 0.384162 +trainer/Advantage Weights Mean 1.22933 +trainer/Advantage Weights Std 8.54405 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.71308e-18 +trainer/Advantage Score Mean -0.582496 +trainer/Advantage Score Std 0.675154 +trainer/Advantage Score Max 1.92737 +trainer/Advantage Score Min -4.09082 +trainer/V1 Predictions Mean -71.2043 +trainer/V1 Predictions Std 18.524 +trainer/V1 Predictions Max -0.584146 +trainer/V1 Predictions Min -86.2087 +trainer/VF Loss 0.09312 +expl/num steps total 926000 +expl/num paths total 1291 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0501517 +expl/Actions Std 0.823245 +expl/Actions Max 2.39107 +expl/Actions Min -2.45576 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 840513 +eval/num paths total 940 +eval/path length Mean 495 +eval/path length Std 49 +eval/path length Max 544 +eval/path length Min 446 +eval/Rewards Mean 0.0020202 +eval/Rewards Std 0.0449012 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00588079 +eval/Actions Std 0.744818 +eval/Actions Max 0.999608 +eval/Actions Min -0.999593 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 2.95136e-06 +time/evaluation sampling (s) 2.36555 +time/exploration sampling (s) 2.98946 +time/logging (s) 0.0119424 +time/saving (s) 0.0188099 +time/training (s) 13.761 +time/epoch (s) 19.1468 +time/total (s) 23157.1 +Epoch -75 +------------------------------ ---------------- +2022-05-16 00:28:47.822982 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -74 finished +------------------------------ ---------------- +epoch -74 +replay_buffer/size 999047 +trainer/num train calls 927000 +trainer/QF1 Loss 2.40846 +trainer/QF2 Loss 2.40533 +trainer/Policy Loss 36.6883 +trainer/Q1 Predictions Mean -70.8204 +trainer/Q1 Predictions Std 18.7242 +trainer/Q1 Predictions Max -0.585683 +trainer/Q1 Predictions Min -87.9829 +trainer/Q2 Predictions Mean -70.8297 +trainer/Q2 Predictions Std 18.6764 +trainer/Q2 Predictions Max -0.427517 +trainer/Q2 Predictions Min -87.9153 +trainer/Q Targets Mean -71.2835 +trainer/Q Targets Std 18.7271 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.9319 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00913185 +trainer/policy/mean Std 0.739651 +trainer/policy/mean Max 0.999708 +trainer/policy/mean Min -0.998773 +trainer/policy/std Mean 0.409849 +trainer/policy/std Std 0.0205246 +trainer/policy/std Max 0.430718 +trainer/policy/std Min 0.382414 +trainer/Advantage Weights Mean 8.42314 +trainer/Advantage Weights Std 23.6063 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.48148e-15 +trainer/Advantage Score Mean -0.232697 +trainer/Advantage Score Std 0.625244 +trainer/Advantage Score Max 2.35043 +trainer/Advantage Score Min -3.41457 +trainer/V1 Predictions Mean -70.9432 +trainer/V1 Predictions Std 18.8708 +trainer/V1 Predictions Max -0.119572 +trainer/V1 Predictions Min -87.7929 +trainer/VF Loss 0.10077 +expl/num steps total 927000 +expl/num paths total 1292 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0542941 +expl/Actions Std 0.834411 +expl/Actions Max 2.34297 +expl/Actions Min -2.68381 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 841513 +eval/num paths total 941 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00927378 +eval/Actions Std 0.751679 +eval/Actions Max 0.999507 +eval/Actions Min -0.999844 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.68826e-06 +time/evaluation sampling (s) 2.50452 +time/exploration sampling (s) 2.93426 +time/logging (s) 0.00764617 +time/saving (s) 0.0190725 +time/training (s) 13.4335 +time/epoch (s) 18.899 +time/total (s) 23176 +Epoch -74 +------------------------------ ---------------- +2022-05-16 00:29:07.072266 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -73 finished +------------------------------ ---------------- +epoch -73 +replay_buffer/size 999047 +trainer/num train calls 928000 +trainer/QF1 Loss 0.644567 +trainer/QF2 Loss 0.661108 +trainer/Policy Loss 24.553 +trainer/Q1 Predictions Mean -72.5265 +trainer/Q1 Predictions Std 16.994 +trainer/Q1 Predictions Max -1.31951 +trainer/Q1 Predictions Min -87.5535 +trainer/Q2 Predictions Mean -72.5339 +trainer/Q2 Predictions Std 17.0237 +trainer/Q2 Predictions Max -2.1702 +trainer/Q2 Predictions Min -87.6578 +trainer/Q Targets Mean -72.2571 +trainer/Q Targets Std 16.9624 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.865 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00880407 +trainer/policy/mean Std 0.727812 +trainer/policy/mean Max 0.999514 +trainer/policy/mean Min -0.999474 +trainer/policy/std Mean 0.409749 +trainer/policy/std Std 0.0201497 +trainer/policy/std Max 0.431502 +trainer/policy/std Min 0.381403 +trainer/Advantage Weights Mean 4.43934 +trainer/Advantage Weights Std 18.648 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.19265e-16 +trainer/Advantage Score Mean -0.482575 +trainer/Advantage Score Std 0.616609 +trainer/Advantage Score Max 1.31592 +trainer/Advantage Score Min -3.60563 +trainer/V1 Predictions Mean -72.0008 +trainer/V1 Predictions Std 17.0459 +trainer/V1 Predictions Max -2.18495 +trainer/V1 Predictions Min -87.2612 +trainer/VF Loss 0.0829897 +expl/num steps total 928000 +expl/num paths total 1294 +expl/path length Mean 500 +expl/path length Std 470 +expl/path length Max 970 +expl/path length Min 30 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0176914 +expl/Actions Std 0.819411 +expl/Actions Max 2.69585 +expl/Actions Min -2.65745 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 842101 +eval/num paths total 942 +eval/path length Mean 588 +eval/path length Std 0 +eval/path length Max 588 +eval/path length Min 588 +eval/Rewards Mean 0.00170068 +eval/Rewards Std 0.0412042 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0147437 +eval/Actions Std 0.757177 +eval/Actions Max 0.999838 +eval/Actions Min -0.999709 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.87872e-06 +time/evaluation sampling (s) 2.46118 +time/exploration sampling (s) 3.004 +time/logging (s) 0.00887386 +time/saving (s) 0.0152897 +time/training (s) 13.746 +time/epoch (s) 19.2353 +time/total (s) 23195.3 +Epoch -73 +------------------------------ ---------------- +2022-05-16 00:29:26.195162 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -72 finished +------------------------------ ---------------- +epoch -72 +replay_buffer/size 999047 +trainer/num train calls 929000 +trainer/QF1 Loss 1.57971 +trainer/QF2 Loss 1.59268 +trainer/Policy Loss 38.2944 +trainer/Q1 Predictions Mean -70.2118 +trainer/Q1 Predictions Std 19.1377 +trainer/Q1 Predictions Max -0.0901826 +trainer/Q1 Predictions Min -88.7083 +trainer/Q2 Predictions Mean -70.0982 +trainer/Q2 Predictions Std 19.1271 +trainer/Q2 Predictions Max 0.526144 +trainer/Q2 Predictions Min -88.339 +trainer/Q Targets Mean -70.0533 +trainer/Q Targets Std 19.3108 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.4653 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00408513 +trainer/policy/mean Std 0.739878 +trainer/policy/mean Max 0.997895 +trainer/policy/mean Min -0.99856 +trainer/policy/std Mean 0.410444 +trainer/policy/std Std 0.0214525 +trainer/policy/std Max 0.435726 +trainer/policy/std Min 0.381803 +trainer/Advantage Weights Mean 8.05513 +trainer/Advantage Weights Std 24.6739 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.83851e-16 +trainer/Advantage Score Mean -0.36568 +trainer/Advantage Score Std 0.726348 +trainer/Advantage Score Max 1.81206 +trainer/Advantage Score Min -3.50769 +trainer/V1 Predictions Mean -69.8307 +trainer/V1 Predictions Std 19.4643 +trainer/V1 Predictions Max 0.355681 +trainer/V1 Predictions Min -88.6914 +trainer/VF Loss 0.118146 +expl/num steps total 929000 +expl/num paths total 1295 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.17134 +expl/Actions Std 0.866904 +expl/Actions Max 2.37686 +expl/Actions Min -2.44391 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 842596 +eval/num paths total 943 +eval/path length Mean 495 +eval/path length Std 0 +eval/path length Max 495 +eval/path length Min 495 +eval/Rewards Mean 0.0020202 +eval/Rewards Std 0.0449012 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0229721 +eval/Actions Std 0.757307 +eval/Actions Max 0.999693 +eval/Actions Min -0.999645 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.79909e-06 +time/evaluation sampling (s) 2.57393 +time/exploration sampling (s) 2.97343 +time/logging (s) 0.00797877 +time/saving (s) 0.0110127 +time/training (s) 13.5396 +time/epoch (s) 19.1059 +time/total (s) 23214.4 +Epoch -72 +------------------------------ ---------------- +2022-05-16 00:29:45.020167 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -71 finished +------------------------------ ---------------- +epoch -71 +replay_buffer/size 999047 +trainer/num train calls 930000 +trainer/QF1 Loss 0.798472 +trainer/QF2 Loss 0.90858 +trainer/Policy Loss 50.3461 +trainer/Q1 Predictions Mean -71.302 +trainer/Q1 Predictions Std 18.4233 +trainer/Q1 Predictions Max -2.21859 +trainer/Q1 Predictions Min -87.1944 +trainer/Q2 Predictions Mean -71.2636 +trainer/Q2 Predictions Std 18.4637 +trainer/Q2 Predictions Max -1.87038 +trainer/Q2 Predictions Min -87.1098 +trainer/Q Targets Mean -71.6489 +trainer/Q Targets Std 18.3034 +trainer/Q Targets Max -1.44429 +trainer/Q Targets Min -87.2192 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0133444 +trainer/policy/mean Std 0.734907 +trainer/policy/mean Max 0.999704 +trainer/policy/mean Min -0.999702 +trainer/policy/std Mean 0.40988 +trainer/policy/std Std 0.0218228 +trainer/policy/std Max 0.434153 +trainer/policy/std Min 0.380322 +trainer/Advantage Weights Mean 10.2773 +trainer/Advantage Weights Std 27.394 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.91562e-27 +trainer/Advantage Score Mean -0.262276 +trainer/Advantage Score Std 0.755616 +trainer/Advantage Score Max 2.43898 +trainer/Advantage Score Min -6.0236 +trainer/V1 Predictions Mean -71.349 +trainer/V1 Predictions Std 18.3057 +trainer/V1 Predictions Max -2.5971 +trainer/V1 Predictions Min -86.9975 +trainer/VF Loss 0.127666 +expl/num steps total 930000 +expl/num paths total 1297 +expl/path length Mean 500 +expl/path length Std 183 +expl/path length Max 683 +expl/path length Min 317 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.00214636 +expl/Actions Std 0.844967 +expl/Actions Max 2.32172 +expl/Actions Min -2.3187 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 843210 +eval/num paths total 944 +eval/path length Mean 614 +eval/path length Std 0 +eval/path length Max 614 +eval/path length Min 614 +eval/Rewards Mean 0.00162866 +eval/Rewards Std 0.0403238 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0201941 +eval/Actions Std 0.740881 +eval/Actions Max 0.999516 +eval/Actions Min -0.999865 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.0552e-06 +time/evaluation sampling (s) 2.50382 +time/exploration sampling (s) 3.03269 +time/logging (s) 0.00667751 +time/saving (s) 0.0106813 +time/training (s) 13.2569 +time/epoch (s) 18.8108 +time/total (s) 23233.2 +Epoch -71 +------------------------------ ---------------- +2022-05-16 00:30:04.104385 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -70 finished +------------------------------ ---------------- +epoch -70 +replay_buffer/size 999047 +trainer/num train calls 931000 +trainer/QF1 Loss 0.73373 +trainer/QF2 Loss 0.833603 +trainer/Policy Loss 11.381 +trainer/Q1 Predictions Mean -71.1351 +trainer/Q1 Predictions Std 18.7413 +trainer/Q1 Predictions Max -2.44524 +trainer/Q1 Predictions Min -87.3193 +trainer/Q2 Predictions Mean -71.1377 +trainer/Q2 Predictions Std 18.5827 +trainer/Q2 Predictions Max -2.81298 +trainer/Q2 Predictions Min -87.3769 +trainer/Q Targets Mean -70.831 +trainer/Q Targets Std 18.7667 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.184 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.000716798 +trainer/policy/mean Std 0.74417 +trainer/policy/mean Max 0.999685 +trainer/policy/mean Min -0.999175 +trainer/policy/std Mean 0.410372 +trainer/policy/std Std 0.0205286 +trainer/policy/std Max 0.433805 +trainer/policy/std Min 0.384256 +trainer/Advantage Weights Mean 3.26681 +trainer/Advantage Weights Std 16.4875 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.38616e-22 +trainer/Advantage Score Mean -0.590289 +trainer/Advantage Score Std 0.742285 +trainer/Advantage Score Max 1.05866 +trainer/Advantage Score Min -4.97872 +trainer/V1 Predictions Mean -70.4923 +trainer/V1 Predictions Std 18.858 +trainer/V1 Predictions Max -1.93328 +trainer/V1 Predictions Min -86.8475 +trainer/VF Loss 0.102476 +expl/num steps total 931000 +expl/num paths total 1298 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0332365 +expl/Actions Std 0.835511 +expl/Actions Max 2.35583 +expl/Actions Min -2.43504 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 843846 +eval/num paths total 945 +eval/path length Mean 636 +eval/path length Std 0 +eval/path length Max 636 +eval/path length Min 636 +eval/Rewards Mean 0.00157233 +eval/Rewards Std 0.0396214 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0319241 +eval/Actions Std 0.733992 +eval/Actions Max 0.999928 +eval/Actions Min -0.999858 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.00499e-05 +time/evaluation sampling (s) 2.51987 +time/exploration sampling (s) 3.1316 +time/logging (s) 0.0103123 +time/saving (s) 0.018995 +time/training (s) 13.3933 +time/epoch (s) 19.0741 +time/total (s) 23252.3 +Epoch -70 +------------------------------ ---------------- +2022-05-16 00:30:22.892839 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -69 finished +------------------------------ ---------------- +epoch -69 +replay_buffer/size 999047 +trainer/num train calls 932000 +trainer/QF1 Loss 0.528199 +trainer/QF2 Loss 0.502817 +trainer/Policy Loss 9.6299 +trainer/Q1 Predictions Mean -72.4589 +trainer/Q1 Predictions Std 16.3418 +trainer/Q1 Predictions Max -0.385782 +trainer/Q1 Predictions Min -87.2583 +trainer/Q2 Predictions Mean -72.4485 +trainer/Q2 Predictions Std 16.179 +trainer/Q2 Predictions Max -0.42996 +trainer/Q2 Predictions Min -87.0597 +trainer/Q Targets Mean -72.4334 +trainer/Q Targets Std 16.2401 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7571 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00985826 +trainer/policy/mean Std 0.74608 +trainer/policy/mean Max 0.999236 +trainer/policy/mean Min -0.999178 +trainer/policy/std Mean 0.411337 +trainer/policy/std Std 0.0207832 +trainer/policy/std Max 0.434245 +trainer/policy/std Min 0.382667 +trainer/Advantage Weights Mean 3.2966 +trainer/Advantage Weights Std 14.1963 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.94074e-14 +trainer/Advantage Score Mean -0.398535 +trainer/Advantage Score Std 0.546802 +trainer/Advantage Score Max 1.39817 +trainer/Advantage Score Min -3.15731 +trainer/V1 Predictions Mean -72.0677 +trainer/V1 Predictions Std 16.4971 +trainer/V1 Predictions Max -0.297281 +trainer/V1 Predictions Min -86.5699 +trainer/VF Loss 0.0591185 +expl/num steps total 932000 +expl/num paths total 1300 +expl/path length Mean 500 +expl/path length Std 322 +expl/path length Max 822 +expl/path length Min 178 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0130636 +expl/Actions Std 0.813948 +expl/Actions Max 2.34348 +expl/Actions Min -2.3405 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 844332 +eval/num paths total 946 +eval/path length Mean 486 +eval/path length Std 0 +eval/path length Max 486 +eval/path length Min 486 +eval/Rewards Mean 0.00205761 +eval/Rewards Std 0.0453142 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00959886 +eval/Actions Std 0.746375 +eval/Actions Max 0.999842 +eval/Actions Min -0.999757 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.86894e-06 +time/evaluation sampling (s) 2.55358 +time/exploration sampling (s) 2.93781 +time/logging (s) 0.00683846 +time/saving (s) 0.0103752 +time/training (s) 13.2552 +time/epoch (s) 18.7638 +time/total (s) 23271.1 +Epoch -69 +------------------------------ ---------------- +2022-05-16 00:30:41.999178 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -68 finished +------------------------------ ---------------- +epoch -68 +replay_buffer/size 999047 +trainer/num train calls 933000 +trainer/QF1 Loss 1.00745 +trainer/QF2 Loss 1.19543 +trainer/Policy Loss 5.82227 +trainer/Q1 Predictions Mean -71.4123 +trainer/Q1 Predictions Std 17.2677 +trainer/Q1 Predictions Max -0.622371 +trainer/Q1 Predictions Min -87.1275 +trainer/Q2 Predictions Mean -71.519 +trainer/Q2 Predictions Std 17.2987 +trainer/Q2 Predictions Max -0.0987862 +trainer/Q2 Predictions Min -87.3407 +trainer/Q Targets Mean -71.2067 +trainer/Q Targets Std 17.3516 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.592 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00423574 +trainer/policy/mean Std 0.73603 +trainer/policy/mean Max 0.998724 +trainer/policy/mean Min -0.999823 +trainer/policy/std Mean 0.411241 +trainer/policy/std Std 0.0203032 +trainer/policy/std Max 0.432145 +trainer/policy/std Min 0.383081 +trainer/Advantage Weights Mean 2.43024 +trainer/Advantage Weights Std 14.332 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.57498e-29 +trainer/Advantage Score Mean -0.692306 +trainer/Advantage Score Std 0.790846 +trainer/Advantage Score Max 1.9003 +trainer/Advantage Score Min -6.5501 +trainer/V1 Predictions Mean -70.9778 +trainer/V1 Predictions Std 17.4734 +trainer/V1 Predictions Max -0.960573 +trainer/V1 Predictions Min -86.566 +trainer/VF Loss 0.134371 +expl/num steps total 933000 +expl/num paths total 1301 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.096514 +expl/Actions Std 0.91222 +expl/Actions Max 2.5213 +expl/Actions Min -2.46375 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 845332 +eval/num paths total 947 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0591428 +eval/Actions Std 0.714361 +eval/Actions Max 0.999858 +eval/Actions Min -0.999859 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.70201e-06 +time/evaluation sampling (s) 2.566 +time/exploration sampling (s) 2.99682 +time/logging (s) 0.00820556 +time/saving (s) 0.0112238 +time/training (s) 13.5121 +time/epoch (s) 19.0944 +time/total (s) 23290.2 +Epoch -68 +------------------------------ ---------------- +2022-05-16 00:31:00.556226 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -67 finished +------------------------------ ---------------- +epoch -67 +replay_buffer/size 999047 +trainer/num train calls 934000 +trainer/QF1 Loss 3.15211 +trainer/QF2 Loss 2.99299 +trainer/Policy Loss 25.334 +trainer/Q1 Predictions Mean -71.9834 +trainer/Q1 Predictions Std 18.4962 +trainer/Q1 Predictions Max -1.43038 +trainer/Q1 Predictions Min -87.4695 +trainer/Q2 Predictions Mean -71.8927 +trainer/Q2 Predictions Std 18.5042 +trainer/Q2 Predictions Max -0.447909 +trainer/Q2 Predictions Min -87.3407 +trainer/Q Targets Mean -71.4715 +trainer/Q Targets Std 18.694 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.0558 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0212685 +trainer/policy/mean Std 0.730063 +trainer/policy/mean Max 0.999002 +trainer/policy/mean Min -0.999483 +trainer/policy/std Mean 0.412159 +trainer/policy/std Std 0.0198103 +trainer/policy/std Max 0.431612 +trainer/policy/std Min 0.38268 +trainer/Advantage Weights Mean 5.203 +trainer/Advantage Weights Std 20.3497 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.42225e-28 +trainer/Advantage Score Mean -0.551686 +trainer/Advantage Score Std 0.872142 +trainer/Advantage Score Max 1.53306 +trainer/Advantage Score Min -6.41201 +trainer/V1 Predictions Mean -71.265 +trainer/V1 Predictions Std 18.8088 +trainer/V1 Predictions Max -0.039606 +trainer/V1 Predictions Min -86.8833 +trainer/VF Loss 0.126347 +expl/num steps total 934000 +expl/num paths total 1302 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.143189 +expl/Actions Std 0.822151 +expl/Actions Max 2.37488 +expl/Actions Min -2.5331 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 846332 +eval/num paths total 948 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.269482 +eval/Actions Std 0.709859 +eval/Actions Max 0.999706 +eval/Actions Min -0.999941 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.66824e-06 +time/evaluation sampling (s) 2.52107 +time/exploration sampling (s) 3.02868 +time/logging (s) 0.0102026 +time/saving (s) 0.00988761 +time/training (s) 12.9746 +time/epoch (s) 18.5444 +time/total (s) 23308.7 +Epoch -67 +------------------------------ ---------------- +2022-05-16 00:31:18.662962 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -66 finished +------------------------------ ---------------- +epoch -66 +replay_buffer/size 999047 +trainer/num train calls 935000 +trainer/QF1 Loss 0.835149 +trainer/QF2 Loss 0.741803 +trainer/Policy Loss 9.24495 +trainer/Q1 Predictions Mean -73.5905 +trainer/Q1 Predictions Std 15.3074 +trainer/Q1 Predictions Max -6.6197 +trainer/Q1 Predictions Min -87.7363 +trainer/Q2 Predictions Mean -73.6632 +trainer/Q2 Predictions Std 15.3388 +trainer/Q2 Predictions Max -7.1667 +trainer/Q2 Predictions Min -87.6588 +trainer/Q Targets Mean -73.1748 +trainer/Q Targets Std 15.3207 +trainer/Q Targets Max -6.09342 +trainer/Q Targets Min -86.7107 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00808885 +trainer/policy/mean Std 0.72818 +trainer/policy/mean Max 0.999365 +trainer/policy/mean Min -0.999905 +trainer/policy/std Mean 0.411576 +trainer/policy/std Std 0.0200311 +trainer/policy/std Max 0.433726 +trainer/policy/std Min 0.382832 +trainer/Advantage Weights Mean 2.20451 +trainer/Advantage Weights Std 13.588 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.63756e-14 +trainer/Advantage Score Mean -0.628773 +trainer/Advantage Score Std 0.538743 +trainer/Advantage Score Max 1.02158 +trainer/Advantage Score Min -3.1743 +trainer/V1 Predictions Mean -72.9458 +trainer/V1 Predictions Std 15.2731 +trainer/V1 Predictions Max -5.62663 +trainer/V1 Predictions Min -86.5674 +trainer/VF Loss 0.0757114 +expl/num steps total 935000 +expl/num paths total 1304 +expl/path length Mean 500 +expl/path length Std 138 +expl/path length Max 638 +expl/path length Min 362 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0153136 +expl/Actions Std 0.835501 +expl/Actions Max 2.1887 +expl/Actions Min -2.258 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 847332 +eval/num paths total 949 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.144881 +eval/Actions Std 0.72728 +eval/Actions Max 0.999535 +eval/Actions Min -0.999903 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.70829e-06 +time/evaluation sampling (s) 2.52875 +time/exploration sampling (s) 2.92475 +time/logging (s) 0.00668893 +time/saving (s) 0.00964367 +time/training (s) 12.6191 +time/epoch (s) 18.089 +time/total (s) 23326.8 +Epoch -66 +------------------------------ ---------------- +2022-05-16 00:31:37.632949 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -65 finished +------------------------------ ---------------- +epoch -65 +replay_buffer/size 999047 +trainer/num train calls 936000 +trainer/QF1 Loss 0.644759 +trainer/QF2 Loss 0.64828 +trainer/Policy Loss 30.5528 +trainer/Q1 Predictions Mean -70.5274 +trainer/Q1 Predictions Std 18.425 +trainer/Q1 Predictions Max -1.51207 +trainer/Q1 Predictions Min -86.5709 +trainer/Q2 Predictions Mean -70.4647 +trainer/Q2 Predictions Std 18.394 +trainer/Q2 Predictions Max -2.07316 +trainer/Q2 Predictions Min -86.7608 +trainer/Q Targets Mean -70.7029 +trainer/Q Targets Std 18.3626 +trainer/Q Targets Max -2.33704 +trainer/Q Targets Min -86.1923 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0330408 +trainer/policy/mean Std 0.724558 +trainer/policy/mean Max 0.999896 +trainer/policy/mean Min -0.999525 +trainer/policy/std Mean 0.409163 +trainer/policy/std Std 0.0203086 +trainer/policy/std Max 0.434116 +trainer/policy/std Min 0.382786 +trainer/Advantage Weights Mean 5.22851 +trainer/Advantage Weights Std 18.8825 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.22521e-23 +trainer/Advantage Score Mean -0.413538 +trainer/Advantage Score Std 0.699356 +trainer/Advantage Score Max 1.20822 +trainer/Advantage Score Min -5.1306 +trainer/V1 Predictions Mean -70.4403 +trainer/V1 Predictions Std 18.4756 +trainer/V1 Predictions Max -0.778118 +trainer/V1 Predictions Min -86.0729 +trainer/VF Loss 0.0918175 +expl/num steps total 936000 +expl/num paths total 1306 +expl/path length Mean 500 +expl/path length Std 284 +expl/path length Max 784 +expl/path length Min 216 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0147332 +expl/Actions Std 0.81495 +expl/Actions Max 2.54818 +expl/Actions Min -2.77185 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 847881 +eval/num paths total 950 +eval/path length Mean 549 +eval/path length Std 0 +eval/path length Max 549 +eval/path length Min 549 +eval/Rewards Mean 0.00182149 +eval/Rewards Std 0.0426401 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0246736 +eval/Actions Std 0.750839 +eval/Actions Max 0.99948 +eval/Actions Min -0.999879 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.11527e-06 +time/evaluation sampling (s) 2.44955 +time/exploration sampling (s) 2.9261 +time/logging (s) 0.00670685 +time/saving (s) 0.0114823 +time/training (s) 13.5631 +time/epoch (s) 18.9569 +time/total (s) 23345.8 +Epoch -65 +------------------------------ ---------------- +2022-05-16 00:31:57.094633 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -64 finished +------------------------------ ---------------- +epoch -64 +replay_buffer/size 999047 +trainer/num train calls 937000 +trainer/QF1 Loss 1.09877 +trainer/QF2 Loss 1.16226 +trainer/Policy Loss 19.5267 +trainer/Q1 Predictions Mean -72.3507 +trainer/Q1 Predictions Std 17.5831 +trainer/Q1 Predictions Max -1.66583 +trainer/Q1 Predictions Min -88.1329 +trainer/Q2 Predictions Mean -72.4793 +trainer/Q2 Predictions Std 17.6359 +trainer/Q2 Predictions Max -1.90473 +trainer/Q2 Predictions Min -87.9161 +trainer/Q Targets Mean -72.2462 +trainer/Q Targets Std 17.8066 +trainer/Q Targets Max -1.59215 +trainer/Q Targets Min -88.0238 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00374564 +trainer/policy/mean Std 0.745403 +trainer/policy/mean Max 0.999761 +trainer/policy/mean Min -0.999659 +trainer/policy/std Mean 0.409913 +trainer/policy/std Std 0.0195601 +trainer/policy/std Max 0.431988 +trainer/policy/std Min 0.381821 +trainer/Advantage Weights Mean 5.76307 +trainer/Advantage Weights Std 20.3491 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.36929e-14 +trainer/Advantage Score Mean -0.312557 +trainer/Advantage Score Std 0.55816 +trainer/Advantage Score Max 1.36835 +trainer/Advantage Score Min -3.05555 +trainer/V1 Predictions Mean -71.9966 +trainer/V1 Predictions Std 17.7204 +trainer/V1 Predictions Max -1.14927 +trainer/V1 Predictions Min -87.8931 +trainer/VF Loss 0.0665844 +expl/num steps total 937000 +expl/num paths total 1307 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0280415 +expl/Actions Std 0.835726 +expl/Actions Max 2.43414 +expl/Actions Min -2.38973 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 848711 +eval/num paths total 951 +eval/path length Mean 830 +eval/path length Std 0 +eval/path length Max 830 +eval/path length Min 830 +eval/Rewards Mean 0.00120482 +eval/Rewards Std 0.0346896 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0231075 +eval/Actions Std 0.732792 +eval/Actions Max 0.999812 +eval/Actions Min -0.999705 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.54974e-06 +time/evaluation sampling (s) 2.61739 +time/exploration sampling (s) 3.12721 +time/logging (s) 0.0104401 +time/saving (s) 0.0155598 +time/training (s) 13.6824 +time/epoch (s) 19.453 +time/total (s) 23365.2 +Epoch -64 +------------------------------ ---------------- +2022-05-16 00:32:16.002996 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -63 finished +------------------------------ ---------------- +epoch -63 +replay_buffer/size 999047 +trainer/num train calls 938000 +trainer/QF1 Loss 0.642679 +trainer/QF2 Loss 0.592256 +trainer/Policy Loss 17.3598 +trainer/Q1 Predictions Mean -69.6215 +trainer/Q1 Predictions Std 20.4553 +trainer/Q1 Predictions Max 0.183511 +trainer/Q1 Predictions Min -87.0218 +trainer/Q2 Predictions Mean -69.7135 +trainer/Q2 Predictions Std 20.3752 +trainer/Q2 Predictions Max -0.433225 +trainer/Q2 Predictions Min -86.9776 +trainer/Q Targets Mean -69.6072 +trainer/Q Targets Std 20.3913 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5417 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0221692 +trainer/policy/mean Std 0.725687 +trainer/policy/mean Max 0.998959 +trainer/policy/mean Min -0.999936 +trainer/policy/std Mean 0.412452 +trainer/policy/std Std 0.0201246 +trainer/policy/std Max 0.435895 +trainer/policy/std Min 0.384518 +trainer/Advantage Weights Mean 3.09749 +trainer/Advantage Weights Std 14.7551 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.01411e-20 +trainer/Advantage Score Mean -0.526914 +trainer/Advantage Score Std 0.698352 +trainer/Advantage Score Max 1.32916 +trainer/Advantage Score Min -4.53515 +trainer/V1 Predictions Mean -69.3263 +trainer/V1 Predictions Std 20.5349 +trainer/V1 Predictions Max 0.89385 +trainer/V1 Predictions Min -86.4002 +trainer/VF Loss 0.0880118 +expl/num steps total 938000 +expl/num paths total 1309 +expl/path length Mean 500 +expl/path length Std 145 +expl/path length Max 645 +expl/path length Min 355 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0782649 +expl/Actions Std 0.816947 +expl/Actions Max 2.46314 +expl/Actions Min -2.20867 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 849388 +eval/num paths total 952 +eval/path length Mean 677 +eval/path length Std 0 +eval/path length Max 677 +eval/path length Min 677 +eval/Rewards Mean 0.0014771 +eval/Rewards Std 0.0384047 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.0350036 +eval/Actions Std 0.726874 +eval/Actions Max 0.999165 +eval/Actions Min -0.999543 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.03332e-06 +time/evaluation sampling (s) 2.50583 +time/exploration sampling (s) 2.96365 +time/logging (s) 0.00673346 +time/saving (s) 0.0113977 +time/training (s) 13.3968 +time/epoch (s) 18.8844 +time/total (s) 23384.1 +Epoch -63 +------------------------------ ---------------- +2022-05-16 00:32:35.781115 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -62 finished +------------------------------ ---------------- +epoch -62 +replay_buffer/size 999047 +trainer/num train calls 939000 +trainer/QF1 Loss 10.4708 +trainer/QF2 Loss 11.0297 +trainer/Policy Loss 16.4303 +trainer/Q1 Predictions Mean -73.5891 +trainer/Q1 Predictions Std 16.6241 +trainer/Q1 Predictions Max -1.51761 +trainer/Q1 Predictions Min -91.0397 +trainer/Q2 Predictions Mean -73.5666 +trainer/Q2 Predictions Std 16.5756 +trainer/Q2 Predictions Max -0.92053 +trainer/Q2 Predictions Min -91.5217 +trainer/Q Targets Mean -73.7423 +trainer/Q Targets Std 16.3595 +trainer/Q Targets Max 0 +trainer/Q Targets Min -91.0738 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0114352 +trainer/policy/mean Std 0.734686 +trainer/policy/mean Max 0.999951 +trainer/policy/mean Min -0.999204 +trainer/policy/std Mean 0.412948 +trainer/policy/std Std 0.0184846 +trainer/policy/std Max 0.433278 +trainer/policy/std Min 0.388201 +trainer/Advantage Weights Mean 4.49783 +trainer/Advantage Weights Std 18.4949 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8e-45 +trainer/Advantage Score Mean -0.475803 +trainer/Advantage Score Std 0.977608 +trainer/Advantage Score Max 1.51408 +trainer/Advantage Score Min -10.1561 +trainer/V1 Predictions Mean -73.2518 +trainer/V1 Predictions Std 16.9277 +trainer/V1 Predictions Max -0.489824 +trainer/V1 Predictions Min -91.2054 +trainer/VF Loss 0.137294 +expl/num steps total 939000 +expl/num paths total 1310 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.144795 +expl/Actions Std 0.850101 +expl/Actions Max 2.31415 +expl/Actions Min -2.31416 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 849996 +eval/num paths total 953 +eval/path length Mean 608 +eval/path length Std 0 +eval/path length Max 608 +eval/path length Min 608 +eval/Rewards Mean 0.00164474 +eval/Rewards Std 0.040522 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0134633 +eval/Actions Std 0.741127 +eval/Actions Max 0.999556 +eval/Actions Min -0.999935 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.07749e-05 +time/evaluation sampling (s) 2.72267 +time/exploration sampling (s) 3.30806 +time/logging (s) 0.00716026 +time/saving (s) 0.0140219 +time/training (s) 13.7143 +time/epoch (s) 19.7662 +time/total (s) 23403.9 +Epoch -62 +------------------------------ ---------------- +2022-05-16 00:32:54.596056 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -61 finished +------------------------------ ---------------- +epoch -61 +replay_buffer/size 999047 +trainer/num train calls 940000 +trainer/QF1 Loss 0.867847 +trainer/QF2 Loss 0.862028 +trainer/Policy Loss 17.31 +trainer/Q1 Predictions Mean -72.5137 +trainer/Q1 Predictions Std 16.3883 +trainer/Q1 Predictions Max -2.34516 +trainer/Q1 Predictions Min -90.0962 +trainer/Q2 Predictions Mean -72.5054 +trainer/Q2 Predictions Std 16.3765 +trainer/Q2 Predictions Max -4.31179 +trainer/Q2 Predictions Min -90.2011 +trainer/Q Targets Mean -72.2276 +trainer/Q Targets Std 16.4658 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.971 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0129803 +trainer/policy/mean Std 0.733173 +trainer/policy/mean Max 0.999431 +trainer/policy/mean Min -0.999588 +trainer/policy/std Mean 0.410806 +trainer/policy/std Std 0.020244 +trainer/policy/std Max 0.432689 +trainer/policy/std Min 0.381313 +trainer/Advantage Weights Mean 2.46788 +trainer/Advantage Weights Std 13.198 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.81933e-14 +trainer/Advantage Score Mean -0.536463 +trainer/Advantage Score Std 0.582916 +trainer/Advantage Score Max 0.909714 +trainer/Advantage Score Min -3.11997 +trainer/V1 Predictions Mean -71.9567 +trainer/V1 Predictions Std 16.6635 +trainer/V1 Predictions Max -2.5681 +trainer/V1 Predictions Min -89.8236 +trainer/VF Loss 0.0728657 +expl/num steps total 940000 +expl/num paths total 1311 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.11178 +expl/Actions Std 0.849838 +expl/Actions Max 2.29662 +expl/Actions Min -2.48336 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 850996 +eval/num paths total 954 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.027913 +eval/Actions Std 0.754041 +eval/Actions Max 0.999896 +eval/Actions Min -0.999783 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.86708e-06 +time/evaluation sampling (s) 2.64901 +time/exploration sampling (s) 3.07604 +time/logging (s) 0.0106175 +time/saving (s) 0.0159307 +time/training (s) 13.0512 +time/epoch (s) 18.8028 +time/total (s) 23422.7 +Epoch -61 +------------------------------ ---------------- +2022-05-16 00:33:13.309161 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -60 finished +------------------------------ ---------------- +epoch -60 +replay_buffer/size 999047 +trainer/num train calls 941000 +trainer/QF1 Loss 1.34394 +trainer/QF2 Loss 1.49539 +trainer/Policy Loss 16.2779 +trainer/Q1 Predictions Mean -69.5641 +trainer/Q1 Predictions Std 20.4944 +trainer/Q1 Predictions Max -0.391492 +trainer/Q1 Predictions Min -88.3366 +trainer/Q2 Predictions Mean -69.5434 +trainer/Q2 Predictions Std 20.5283 +trainer/Q2 Predictions Max -0.544255 +trainer/Q2 Predictions Min -88.6497 +trainer/Q Targets Mean -69.6134 +trainer/Q Targets Std 20.8045 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.6879 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0020289 +trainer/policy/mean Std 0.735982 +trainer/policy/mean Max 0.999229 +trainer/policy/mean Min -0.99985 +trainer/policy/std Mean 0.41038 +trainer/policy/std Std 0.0202301 +trainer/policy/std Max 0.433033 +trainer/policy/std Min 0.37891 +trainer/Advantage Weights Mean 4.01375 +trainer/Advantage Weights Std 17.6344 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.7235e-32 +trainer/Advantage Score Mean -0.531815 +trainer/Advantage Score Std 0.795422 +trainer/Advantage Score Max 1.65156 +trainer/Advantage Score Min -7.17771 +trainer/V1 Predictions Mean -69.3484 +trainer/V1 Predictions Std 20.9257 +trainer/V1 Predictions Max 0.692912 +trainer/V1 Predictions Min -88.7389 +trainer/VF Loss 0.117472 +expl/num steps total 941000 +expl/num paths total 1313 +expl/path length Mean 500 +expl/path length Std 285 +expl/path length Max 785 +expl/path length Min 215 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.000987571 +expl/Actions Std 0.841019 +expl/Actions Max 2.27784 +expl/Actions Min -2.42314 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 851662 +eval/num paths total 955 +eval/path length Mean 666 +eval/path length Std 0 +eval/path length Max 666 +eval/path length Min 666 +eval/Rewards Mean 0.0015015 +eval/Rewards Std 0.0387201 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0304189 +eval/Actions Std 0.753479 +eval/Actions Max 0.9996 +eval/Actions Min -0.999678 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 4.32739e-06 +time/evaluation sampling (s) 2.5587 +time/exploration sampling (s) 2.97729 +time/logging (s) 0.00641921 +time/saving (s) 0.0105156 +time/training (s) 13.1363 +time/epoch (s) 18.6892 +time/total (s) 23441.4 +Epoch -60 +------------------------------ ---------------- +2022-05-16 00:33:32.166363 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -59 finished +------------------------------ ---------------- +epoch -59 +replay_buffer/size 999047 +trainer/num train calls 942000 +trainer/QF1 Loss 0.518205 +trainer/QF2 Loss 0.500749 +trainer/Policy Loss 6.82305 +trainer/Q1 Predictions Mean -73.0576 +trainer/Q1 Predictions Std 16.9024 +trainer/Q1 Predictions Max -0.555993 +trainer/Q1 Predictions Min -86.7869 +trainer/Q2 Predictions Mean -73.0805 +trainer/Q2 Predictions Std 16.9406 +trainer/Q2 Predictions Max -0.4339 +trainer/Q2 Predictions Min -86.8093 +trainer/Q Targets Mean -73.201 +trainer/Q Targets Std 16.9615 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9494 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00489946 +trainer/policy/mean Std 0.734881 +trainer/policy/mean Max 0.999818 +trainer/policy/mean Min -0.999604 +trainer/policy/std Mean 0.410312 +trainer/policy/std Std 0.0199431 +trainer/policy/std Max 0.43247 +trainer/policy/std Min 0.379948 +trainer/Advantage Weights Mean 3.14636 +trainer/Advantage Weights Std 15.1966 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.2694e-17 +trainer/Advantage Score Mean -0.353938 +trainer/Advantage Score Std 0.479605 +trainer/Advantage Score Max 0.770044 +trainer/Advantage Score Min -3.83244 +trainer/V1 Predictions Mean -72.9949 +trainer/V1 Predictions Std 17.1111 +trainer/V1 Predictions Max -0.0266499 +trainer/V1 Predictions Min -86.9382 +trainer/VF Loss 0.0452504 +expl/num steps total 942000 +expl/num paths total 1314 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.13882 +expl/Actions Std 0.816285 +expl/Actions Max 2.51482 +expl/Actions Min -2.43729 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 852008 +eval/num paths total 956 +eval/path length Mean 346 +eval/path length Std 0 +eval/path length Max 346 +eval/path length Min 346 +eval/Rewards Mean 0.00289017 +eval/Rewards Std 0.0536826 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0349869 +eval/Actions Std 0.756714 +eval/Actions Max 0.999236 +eval/Actions Min -0.999587 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.76323e-06 +time/evaluation sampling (s) 2.49098 +time/exploration sampling (s) 2.94585 +time/logging (s) 0.00516216 +time/saving (s) 0.00963721 +time/training (s) 13.3893 +time/epoch (s) 18.8409 +time/total (s) 23460.2 +Epoch -59 +------------------------------ ---------------- +2022-05-16 00:33:51.481022 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -58 finished +------------------------------ ---------------- +epoch -58 +replay_buffer/size 999047 +trainer/num train calls 943000 +trainer/QF1 Loss 0.832046 +trainer/QF2 Loss 0.901833 +trainer/Policy Loss 14.4551 +trainer/Q1 Predictions Mean -73.3351 +trainer/Q1 Predictions Std 16.1147 +trainer/Q1 Predictions Max -0.456731 +trainer/Q1 Predictions Min -89.5171 +trainer/Q2 Predictions Mean -73.205 +trainer/Q2 Predictions Std 16.2239 +trainer/Q2 Predictions Max -0.458688 +trainer/Q2 Predictions Min -89.1149 +trainer/Q Targets Mean -72.9739 +trainer/Q Targets Std 15.93 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.1573 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00365733 +trainer/policy/mean Std 0.720746 +trainer/policy/mean Max 0.999073 +trainer/policy/mean Min -0.999118 +trainer/policy/std Mean 0.410169 +trainer/policy/std Std 0.0192188 +trainer/policy/std Max 0.433247 +trainer/policy/std Min 0.384502 +trainer/Advantage Weights Mean 3.10342 +trainer/Advantage Weights Std 15.6352 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.99715e-16 +trainer/Advantage Score Mean -0.546264 +trainer/Advantage Score Std 0.623722 +trainer/Advantage Score Max 1.16483 +trainer/Advantage Score Min -3.48959 +trainer/V1 Predictions Mean -72.6796 +trainer/V1 Predictions Std 16.1737 +trainer/V1 Predictions Max -0.262505 +trainer/V1 Predictions Min -88.005 +trainer/VF Loss 0.0822325 +expl/num steps total 943000 +expl/num paths total 1315 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.205398 +expl/Actions Std 0.832685 +expl/Actions Max 2.26412 +expl/Actions Min -2.30863 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 853008 +eval/num paths total 957 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.21219 +eval/Actions Std 0.793997 +eval/Actions Max 0.99928 +eval/Actions Min -0.999683 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.34997e-06 +time/evaluation sampling (s) 2.59836 +time/exploration sampling (s) 3.06579 +time/logging (s) 0.00799998 +time/saving (s) 0.013093 +time/training (s) 13.6211 +time/epoch (s) 19.3064 +time/total (s) 23479.5 +Epoch -58 +------------------------------ ---------------- +2022-05-16 00:34:10.587599 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -57 finished +------------------------------ ---------------- +epoch -57 +replay_buffer/size 999047 +trainer/num train calls 944000 +trainer/QF1 Loss 0.576084 +trainer/QF2 Loss 0.580689 +trainer/Policy Loss 18.9687 +trainer/Q1 Predictions Mean -71.6681 +trainer/Q1 Predictions Std 19.6263 +trainer/Q1 Predictions Max -0.134651 +trainer/Q1 Predictions Min -87.111 +trainer/Q2 Predictions Mean -71.5377 +trainer/Q2 Predictions Std 19.688 +trainer/Q2 Predictions Max 0.651752 +trainer/Q2 Predictions Min -87.177 +trainer/Q Targets Mean -71.4522 +trainer/Q Targets Std 19.7311 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.9002 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00984652 +trainer/policy/mean Std 0.738758 +trainer/policy/mean Max 0.999826 +trainer/policy/mean Min -0.999044 +trainer/policy/std Mean 0.410184 +trainer/policy/std Std 0.0196603 +trainer/policy/std Max 0.431859 +trainer/policy/std Min 0.383862 +trainer/Advantage Weights Mean 3.42867 +trainer/Advantage Weights Std 16.1472 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.68122e-18 +trainer/Advantage Score Mean -0.508062 +trainer/Advantage Score Std 0.756299 +trainer/Advantage Score Max 2.01758 +trainer/Advantage Score Min -3.9903 +trainer/V1 Predictions Mean -71.1701 +trainer/V1 Predictions Std 19.7685 +trainer/V1 Predictions Max 0.475289 +trainer/V1 Predictions Min -86.8101 +trainer/VF Loss 0.118327 +expl/num steps total 944000 +expl/num paths total 1317 +expl/path length Mean 500 +expl/path length Std 68 +expl/path length Max 568 +expl/path length Min 432 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0191823 +expl/Actions Std 0.840879 +expl/Actions Max 2.25885 +expl/Actions Min -2.33897 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 854008 +eval/num paths total 958 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0433879 +eval/Actions Std 0.726017 +eval/Actions Max 0.999798 +eval/Actions Min -0.999976 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.685e-06 +time/evaluation sampling (s) 2.56688 +time/exploration sampling (s) 2.91197 +time/logging (s) 0.00679832 +time/saving (s) 0.00965645 +time/training (s) 13.595 +time/epoch (s) 19.0903 +time/total (s) 23498.6 +Epoch -57 +------------------------------ ---------------- +2022-05-16 00:34:29.690776 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -56 finished +------------------------------ ---------------- +epoch -56 +replay_buffer/size 999047 +trainer/num train calls 945000 +trainer/QF1 Loss 0.811748 +trainer/QF2 Loss 0.779803 +trainer/Policy Loss 13.429 +trainer/Q1 Predictions Mean -71.398 +trainer/Q1 Predictions Std 17.0117 +trainer/Q1 Predictions Max -0.847286 +trainer/Q1 Predictions Min -86.6149 +trainer/Q2 Predictions Mean -71.4074 +trainer/Q2 Predictions Std 16.9784 +trainer/Q2 Predictions Max -1.96928 +trainer/Q2 Predictions Min -86.5102 +trainer/Q Targets Mean -71.5973 +trainer/Q Targets Std 16.7764 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.5738 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0211441 +trainer/policy/mean Std 0.733109 +trainer/policy/mean Max 0.999276 +trainer/policy/mean Min -0.999495 +trainer/policy/std Mean 0.410603 +trainer/policy/std Std 0.0200056 +trainer/policy/std Max 0.432783 +trainer/policy/std Min 0.381031 +trainer/Advantage Weights Mean 2.71331 +trainer/Advantage Weights Std 14.7346 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.05454e-15 +trainer/Advantage Score Mean -0.545774 +trainer/Advantage Score Std 0.534415 +trainer/Advantage Score Max 1.19613 +trainer/Advantage Score Min -3.23355 +trainer/V1 Predictions Mean -71.2532 +trainer/V1 Predictions Std 16.9749 +trainer/V1 Predictions Max -2.02339 +trainer/V1 Predictions Min -86.4277 +trainer/VF Loss 0.0710133 +expl/num steps total 945000 +expl/num paths total 1318 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0228715 +expl/Actions Std 0.831935 +expl/Actions Max 2.80992 +expl/Actions Min -2.47494 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 855008 +eval/num paths total 959 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0720387 +eval/Actions Std 0.747277 +eval/Actions Max 0.999494 +eval/Actions Min -0.999541 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.34485e-06 +time/evaluation sampling (s) 2.59334 +time/exploration sampling (s) 3.04119 +time/logging (s) 0.00733531 +time/saving (s) 0.0102688 +time/training (s) 13.4383 +time/epoch (s) 19.0904 +time/total (s) 23517.7 +Epoch -56 +------------------------------ ---------------- +2022-05-16 00:34:48.542988 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -55 finished +------------------------------ ---------------- +epoch -55 +replay_buffer/size 999047 +trainer/num train calls 946000 +trainer/QF1 Loss 1.36393 +trainer/QF2 Loss 1.14164 +trainer/Policy Loss 11.637 +trainer/Q1 Predictions Mean -69.7504 +trainer/Q1 Predictions Std 21.8689 +trainer/Q1 Predictions Max -1.12189 +trainer/Q1 Predictions Min -86.8827 +trainer/Q2 Predictions Mean -69.7509 +trainer/Q2 Predictions Std 21.8828 +trainer/Q2 Predictions Max -0.0933309 +trainer/Q2 Predictions Min -87.0782 +trainer/Q Targets Mean -69.7276 +trainer/Q Targets Std 21.8758 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7068 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00493859 +trainer/policy/mean Std 0.736119 +trainer/policy/mean Max 0.999253 +trainer/policy/mean Min -0.999831 +trainer/policy/std Mean 0.411471 +trainer/policy/std Std 0.0206101 +trainer/policy/std Max 0.432855 +trainer/policy/std Min 0.381865 +trainer/Advantage Weights Mean 3.5877 +trainer/Advantage Weights Std 15.5725 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.45009e-16 +trainer/Advantage Score Mean -0.398578 +trainer/Advantage Score Std 0.564043 +trainer/Advantage Score Max 1.20211 +trainer/Advantage Score Min -3.47072 +trainer/V1 Predictions Mean -69.4941 +trainer/V1 Predictions Std 21.8527 +trainer/V1 Predictions Max -0.56648 +trainer/V1 Predictions Min -86.6036 +trainer/VF Loss 0.0706783 +expl/num steps total 946000 +expl/num paths total 1320 +expl/path length Mean 500 +expl/path length Std 338 +expl/path length Max 838 +expl/path length Min 162 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0356489 +expl/Actions Std 0.825848 +expl/Actions Max 2.30722 +expl/Actions Min -2.78533 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 855905 +eval/num paths total 960 +eval/path length Mean 897 +eval/path length Std 0 +eval/path length Max 897 +eval/path length Min 897 +eval/Rewards Mean 0.00111483 +eval/Rewards Std 0.0333704 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0901087 +eval/Actions Std 0.64683 +eval/Actions Max 0.99912 +eval/Actions Min -0.999275 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.96207e-06 +time/evaluation sampling (s) 2.50892 +time/exploration sampling (s) 3.00862 +time/logging (s) 0.00856686 +time/saving (s) 0.0135482 +time/training (s) 13.3004 +time/epoch (s) 18.8401 +time/total (s) 23536.6 +Epoch -55 +------------------------------ ---------------- +2022-05-16 00:35:07.589719 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -54 finished +------------------------------ ---------------- +epoch -54 +replay_buffer/size 999047 +trainer/num train calls 947000 +trainer/QF1 Loss 0.907256 +trainer/QF2 Loss 1.06926 +trainer/Policy Loss 26.2574 +trainer/Q1 Predictions Mean -72.2431 +trainer/Q1 Predictions Std 18.295 +trainer/Q1 Predictions Max -2.94239 +trainer/Q1 Predictions Min -86.8353 +trainer/Q2 Predictions Mean -72.2513 +trainer/Q2 Predictions Std 18.317 +trainer/Q2 Predictions Max -3.25963 +trainer/Q2 Predictions Min -86.8443 +trainer/Q Targets Mean -72.4109 +trainer/Q Targets Std 18.379 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.838 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0270968 +trainer/policy/mean Std 0.73002 +trainer/policy/mean Max 0.999171 +trainer/policy/mean Min -0.999414 +trainer/policy/std Mean 0.411204 +trainer/policy/std Std 0.0204463 +trainer/policy/std Max 0.435395 +trainer/policy/std Min 0.384181 +trainer/Advantage Weights Mean 4.33527 +trainer/Advantage Weights Std 18.3703 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.65023e-11 +trainer/Advantage Score Mean -0.378435 +trainer/Advantage Score Std 0.560596 +trainer/Advantage Score Max 2.41439 +trainer/Advantage Score Min -2.48275 +trainer/V1 Predictions Mean -72.1781 +trainer/V1 Predictions Std 18.4698 +trainer/V1 Predictions Max -3.71072 +trainer/V1 Predictions Min -86.9578 +trainer/VF Loss 0.0893474 +expl/num steps total 947000 +expl/num paths total 1322 +expl/path length Mean 500 +expl/path length Std 351 +expl/path length Max 851 +expl/path length Min 149 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0258403 +expl/Actions Std 0.833121 +expl/Actions Max 2.11534 +expl/Actions Min -2.45449 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 856905 +eval/num paths total 961 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.161044 +eval/Actions Std 0.754719 +eval/Actions Max 0.999847 +eval/Actions Min -0.999947 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.83308e-06 +time/evaluation sampling (s) 2.63422 +time/exploration sampling (s) 2.9569 +time/logging (s) 0.00992291 +time/saving (s) 0.0159385 +time/training (s) 13.4141 +time/epoch (s) 19.0311 +time/total (s) 23555.6 +Epoch -54 +------------------------------ ---------------- +2022-05-16 00:35:26.693065 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -53 finished +------------------------------ ---------------- +epoch -53 +replay_buffer/size 999047 +trainer/num train calls 948000 +trainer/QF1 Loss 2.93189 +trainer/QF2 Loss 2.66316 +trainer/Policy Loss 27.5039 +trainer/Q1 Predictions Mean -70.832 +trainer/Q1 Predictions Std 18.7765 +trainer/Q1 Predictions Max -1.18482 +trainer/Q1 Predictions Min -86.974 +trainer/Q2 Predictions Mean -70.8624 +trainer/Q2 Predictions Std 18.7707 +trainer/Q2 Predictions Max -1.39949 +trainer/Q2 Predictions Min -87.2314 +trainer/Q Targets Mean -70.8556 +trainer/Q Targets Std 18.5918 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8886 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0220054 +trainer/policy/mean Std 0.711953 +trainer/policy/mean Max 0.999222 +trainer/policy/mean Min -0.999611 +trainer/policy/std Mean 0.410721 +trainer/policy/std Std 0.0203966 +trainer/policy/std Max 0.434973 +trainer/policy/std Min 0.380215 +trainer/Advantage Weights Mean 4.40277 +trainer/Advantage Weights Std 18.3314 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.45605e-28 +trainer/Advantage Score Mean -0.42454 +trainer/Advantage Score Std 0.698108 +trainer/Advantage Score Max 1.75019 +trainer/Advantage Score Min -6.26074 +trainer/V1 Predictions Mean -70.6084 +trainer/V1 Predictions Std 18.7846 +trainer/V1 Predictions Max -0.930389 +trainer/V1 Predictions Min -86.7214 +trainer/VF Loss 0.105542 +expl/num steps total 948000 +expl/num paths total 1324 +expl/path length Mean 500 +expl/path length Std 140 +expl/path length Max 640 +expl/path length Min 360 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0267844 +expl/Actions Std 0.814799 +expl/Actions Max 2.48983 +expl/Actions Min -2.14833 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 857905 +eval/num paths total 962 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.024335 +eval/Actions Std 0.728848 +eval/Actions Max 0.999974 +eval/Actions Min -0.99961 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.302e-06 +time/evaluation sampling (s) 2.58995 +time/exploration sampling (s) 3.01306 +time/logging (s) 0.011905 +time/saving (s) 0.0184002 +time/training (s) 13.4537 +time/epoch (s) 19.087 +time/total (s) 23574.7 +Epoch -53 +------------------------------ ---------------- +2022-05-16 00:35:45.862982 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -52 finished +------------------------------ ---------------- +epoch -52 +replay_buffer/size 999047 +trainer/num train calls 949000 +trainer/QF1 Loss 1.08501 +trainer/QF2 Loss 1.166 +trainer/Policy Loss 7.66186 +trainer/Q1 Predictions Mean -69.9857 +trainer/Q1 Predictions Std 21.2574 +trainer/Q1 Predictions Max 0.904475 +trainer/Q1 Predictions Min -90.7902 +trainer/Q2 Predictions Mean -70.0553 +trainer/Q2 Predictions Std 21.2101 +trainer/Q2 Predictions Max 0.382027 +trainer/Q2 Predictions Min -91.3851 +trainer/Q Targets Mean -69.6041 +trainer/Q Targets Std 20.9921 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.9944 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0239245 +trainer/policy/mean Std 0.74047 +trainer/policy/mean Max 0.999897 +trainer/policy/mean Min -0.998884 +trainer/policy/std Mean 0.410212 +trainer/policy/std Std 0.0195643 +trainer/policy/std Max 0.433665 +trainer/policy/std Min 0.383109 +trainer/Advantage Weights Mean 1.45942 +trainer/Advantage Weights Std 8.9489 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.93668e-17 +trainer/Advantage Score Mean -0.7443 +trainer/Advantage Score Std 0.645178 +trainer/Advantage Score Max 0.509539 +trainer/Advantage Score Min -3.8483 +trainer/V1 Predictions Mean -69.3459 +trainer/V1 Predictions Std 21.1705 +trainer/V1 Predictions Max -0.235817 +trainer/V1 Predictions Min -90.4015 +trainer/VF Loss 0.100813 +expl/num steps total 949000 +expl/num paths total 1326 +expl/path length Mean 500 +expl/path length Std 188 +expl/path length Max 688 +expl/path length Min 312 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.031236 +expl/Actions Std 0.84043 +expl/Actions Max 2.59145 +expl/Actions Min -2.32463 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 858905 +eval/num paths total 963 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.00815316 +eval/Actions Std 0.660004 +eval/Actions Max 0.999885 +eval/Actions Min -0.999691 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.02028e-06 +time/evaluation sampling (s) 2.57226 +time/exploration sampling (s) 2.9547 +time/logging (s) 0.00857361 +time/saving (s) 0.014805 +time/training (s) 13.5929 +time/epoch (s) 19.1433 +time/total (s) 23593.9 +Epoch -52 +------------------------------ ---------------- +2022-05-16 00:36:05.466298 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -51 finished +------------------------------ ---------------- +epoch -51 +replay_buffer/size 999047 +trainer/num train calls 950000 +trainer/QF1 Loss 0.636405 +trainer/QF2 Loss 0.785216 +trainer/Policy Loss 19.0178 +trainer/Q1 Predictions Mean -72.4938 +trainer/Q1 Predictions Std 17.5639 +trainer/Q1 Predictions Max -0.805427 +trainer/Q1 Predictions Min -86.951 +trainer/Q2 Predictions Mean -72.5114 +trainer/Q2 Predictions Std 17.5754 +trainer/Q2 Predictions Max -0.638174 +trainer/Q2 Predictions Min -86.6598 +trainer/Q Targets Mean -72.3717 +trainer/Q Targets Std 17.4322 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6459 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.010971 +trainer/policy/mean Std 0.726508 +trainer/policy/mean Max 0.999461 +trainer/policy/mean Min -0.998113 +trainer/policy/std Mean 0.410149 +trainer/policy/std Std 0.0201441 +trainer/policy/std Max 0.435247 +trainer/policy/std Min 0.38153 +trainer/Advantage Weights Mean 4.26752 +trainer/Advantage Weights Std 18.2322 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.66867e-17 +trainer/Advantage Score Mean -0.462504 +trainer/Advantage Score Std 0.550323 +trainer/Advantage Score Max 1.67557 +trainer/Advantage Score Min -3.76031 +trainer/V1 Predictions Mean -72.1667 +trainer/V1 Predictions Std 17.483 +trainer/V1 Predictions Max -0.731224 +trainer/V1 Predictions Min -86.553 +trainer/VF Loss 0.0847991 +expl/num steps total 950000 +expl/num paths total 1328 +expl/path length Mean 500 +expl/path length Std 204 +expl/path length Max 704 +expl/path length Min 296 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0241055 +expl/Actions Std 0.846326 +expl/Actions Max 2.31299 +expl/Actions Min -2.58489 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 859905 +eval/num paths total 964 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.363119 +eval/Actions Std 0.734813 +eval/Actions Max 0.999909 +eval/Actions Min -0.999872 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.22564e-06 +time/evaluation sampling (s) 2.61324 +time/exploration sampling (s) 3.01956 +time/logging (s) 0.00939337 +time/saving (s) 0.0111103 +time/training (s) 13.9336 +time/epoch (s) 19.5869 +time/total (s) 23613.4 +Epoch -51 +------------------------------ ---------------- +2022-05-16 00:36:24.514451 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -50 finished +------------------------------ ---------------- +epoch -50 +replay_buffer/size 999047 +trainer/num train calls 951000 +trainer/QF1 Loss 0.816917 +trainer/QF2 Loss 0.643494 +trainer/Policy Loss 6.8885 +trainer/Q1 Predictions Mean -69.9104 +trainer/Q1 Predictions Std 19.4744 +trainer/Q1 Predictions Max -0.508664 +trainer/Q1 Predictions Min -88.022 +trainer/Q2 Predictions Mean -69.8271 +trainer/Q2 Predictions Std 19.4749 +trainer/Q2 Predictions Max -0.824925 +trainer/Q2 Predictions Min -87.3523 +trainer/Q Targets Mean -69.5528 +trainer/Q Targets Std 19.2888 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7071 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00381155 +trainer/policy/mean Std 0.724863 +trainer/policy/mean Max 0.999593 +trainer/policy/mean Min -0.99919 +trainer/policy/std Mean 0.411438 +trainer/policy/std Std 0.0199064 +trainer/policy/std Max 0.432654 +trainer/policy/std Min 0.382278 +trainer/Advantage Weights Mean 2.72888 +trainer/Advantage Weights Std 15.4194 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.57921e-22 +trainer/Advantage Score Mean -0.730603 +trainer/Advantage Score Std 0.707206 +trainer/Advantage Score Max 0.742046 +trainer/Advantage Score Min -4.97094 +trainer/V1 Predictions Mean -69.2251 +trainer/V1 Predictions Std 19.4851 +trainer/V1 Predictions Max -0.430568 +trainer/V1 Predictions Min -86.7903 +trainer/VF Loss 0.112013 +expl/num steps total 951000 +expl/num paths total 1329 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.134742 +expl/Actions Std 0.848812 +expl/Actions Max 2.81719 +expl/Actions Min -2.39033 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 860905 +eval/num paths total 965 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.318165 +eval/Actions Std 0.738814 +eval/Actions Max 0.999954 +eval/Actions Min -0.999697 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 8.92626e-06 +time/evaluation sampling (s) 2.62423 +time/exploration sampling (s) 2.96907 +time/logging (s) 0.00762791 +time/saving (s) 0.0205129 +time/training (s) 13.4053 +time/epoch (s) 19.0268 +time/total (s) 23632.5 +Epoch -50 +------------------------------ ---------------- +2022-05-16 00:36:43.464584 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -49 finished +------------------------------ ---------------- +epoch -49 +replay_buffer/size 999047 +trainer/num train calls 952000 +trainer/QF1 Loss 1.70877 +trainer/QF2 Loss 1.71895 +trainer/Policy Loss 10.9985 +trainer/Q1 Predictions Mean -70.2071 +trainer/Q1 Predictions Std 20.1678 +trainer/Q1 Predictions Max -1.65427 +trainer/Q1 Predictions Min -86.1538 +trainer/Q2 Predictions Mean -70.2762 +trainer/Q2 Predictions Std 20.1868 +trainer/Q2 Predictions Max -2.23799 +trainer/Q2 Predictions Min -86.1781 +trainer/Q Targets Mean -70.3331 +trainer/Q Targets Std 20.427 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6514 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.01571 +trainer/policy/mean Std 0.732004 +trainer/policy/mean Max 0.999698 +trainer/policy/mean Min -0.999685 +trainer/policy/std Mean 0.409453 +trainer/policy/std Std 0.0203105 +trainer/policy/std Max 0.430749 +trainer/policy/std Min 0.380091 +trainer/Advantage Weights Mean 4.3906 +trainer/Advantage Weights Std 19.0606 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.53175e-19 +trainer/Advantage Score Mean -0.513289 +trainer/Advantage Score Std 0.788168 +trainer/Advantage Score Max 1.92705 +trainer/Advantage Score Min -4.20386 +trainer/V1 Predictions Mean -69.9945 +trainer/V1 Predictions Std 20.5812 +trainer/V1 Predictions Max -0.900458 +trainer/V1 Predictions Min -86.3384 +trainer/VF Loss 0.120027 +expl/num steps total 952000 +expl/num paths total 1330 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.117287 +expl/Actions Std 0.84178 +expl/Actions Max 2.19262 +expl/Actions Min -2.27486 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 861710 +eval/num paths total 966 +eval/path length Mean 805 +eval/path length Std 0 +eval/path length Max 805 +eval/path length Min 805 +eval/Rewards Mean 0.00124224 +eval/Rewards Std 0.0352235 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean -0.000450862 +eval/Actions Std 0.740403 +eval/Actions Max 0.999953 +eval/Actions Min -0.99976 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.18699e-06 +time/evaluation sampling (s) 2.63454 +time/exploration sampling (s) 3.11353 +time/logging (s) 0.0101909 +time/saving (s) 0.0185867 +time/training (s) 13.1618 +time/epoch (s) 18.9386 +time/total (s) 23651.4 +Epoch -49 +------------------------------ ---------------- +2022-05-16 00:37:02.348713 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -48 finished +------------------------------ ---------------- +epoch -48 +replay_buffer/size 999047 +trainer/num train calls 953000 +trainer/QF1 Loss 0.928458 +trainer/QF2 Loss 0.766951 +trainer/Policy Loss 24.446 +trainer/Q1 Predictions Mean -71.164 +trainer/Q1 Predictions Std 18.0004 +trainer/Q1 Predictions Max 0.896531 +trainer/Q1 Predictions Min -88.5417 +trainer/Q2 Predictions Mean -71.1445 +trainer/Q2 Predictions Std 18.0242 +trainer/Q2 Predictions Max -0.592676 +trainer/Q2 Predictions Min -88.5327 +trainer/Q Targets Mean -71.2378 +trainer/Q Targets Std 18.0069 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.51 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0216538 +trainer/policy/mean Std 0.745473 +trainer/policy/mean Max 0.998835 +trainer/policy/mean Min -0.999338 +trainer/policy/std Mean 0.410536 +trainer/policy/std Std 0.0199926 +trainer/policy/std Max 0.430699 +trainer/policy/std Min 0.383174 +trainer/Advantage Weights Mean 6.07595 +trainer/Advantage Weights Std 20.1682 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.53253e-18 +trainer/Advantage Score Mean -0.335315 +trainer/Advantage Score Std 0.701173 +trainer/Advantage Score Max 3.39222 +trainer/Advantage Score Min -4.05173 +trainer/V1 Predictions Mean -71.0385 +trainer/V1 Predictions Std 18.0347 +trainer/V1 Predictions Max -0.0145719 +trainer/V1 Predictions Min -88.1413 +trainer/VF Loss 0.128942 +expl/num steps total 953000 +expl/num paths total 1332 +expl/path length Mean 500 +expl/path length Std 316 +expl/path length Max 816 +expl/path length Min 184 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0312089 +expl/Actions Std 0.824953 +expl/Actions Max 2.2533 +expl/Actions Min -2.52193 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 862042 +eval/num paths total 967 +eval/path length Mean 332 +eval/path length Std 0 +eval/path length Max 332 +eval/path length Min 332 +eval/Rewards Mean 0.00301205 +eval/Rewards Std 0.0547994 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0184969 +eval/Actions Std 0.760205 +eval/Actions Max 0.999365 +eval/Actions Min -0.999813 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.25916e-06 +time/evaluation sampling (s) 2.54589 +time/exploration sampling (s) 2.99207 +time/logging (s) 0.00620306 +time/saving (s) 0.0110456 +time/training (s) 13.3053 +time/epoch (s) 18.8605 +time/total (s) 23670.3 +Epoch -48 +------------------------------ ---------------- +2022-05-16 00:37:20.968954 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -47 finished +------------------------------ ---------------- +epoch -47 +replay_buffer/size 999047 +trainer/num train calls 954000 +trainer/QF1 Loss 0.635816 +trainer/QF2 Loss 0.762916 +trainer/Policy Loss 20.4831 +trainer/Q1 Predictions Mean -72.0491 +trainer/Q1 Predictions Std 18.515 +trainer/Q1 Predictions Max -0.403496 +trainer/Q1 Predictions Min -87.4447 +trainer/Q2 Predictions Mean -71.9467 +trainer/Q2 Predictions Std 18.4352 +trainer/Q2 Predictions Max -0.471668 +trainer/Q2 Predictions Min -87.155 +trainer/Q Targets Mean -72.1093 +trainer/Q Targets Std 18.5549 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5555 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0152181 +trainer/policy/mean Std 0.728587 +trainer/policy/mean Max 0.999988 +trainer/policy/mean Min -0.999373 +trainer/policy/std Mean 0.409217 +trainer/policy/std Std 0.0193429 +trainer/policy/std Max 0.428018 +trainer/policy/std Min 0.380177 +trainer/Advantage Weights Mean 4.60199 +trainer/Advantage Weights Std 18.1795 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.14438e-23 +trainer/Advantage Score Mean -0.341599 +trainer/Advantage Score Std 0.55551 +trainer/Advantage Score Max 1.06379 +trainer/Advantage Score Min -5.28246 +trainer/V1 Predictions Mean -71.8977 +trainer/V1 Predictions Std 18.6548 +trainer/V1 Predictions Max -0.344495 +trainer/V1 Predictions Min -87.445 +trainer/VF Loss 0.0610593 +expl/num steps total 954000 +expl/num paths total 1334 +expl/path length Mean 500 +expl/path length Std 257 +expl/path length Max 757 +expl/path length Min 243 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean -0.0710114 +expl/Actions Std 0.845451 +expl/Actions Max 2.21049 +expl/Actions Min -2.37255 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 862653 +eval/num paths total 968 +eval/path length Mean 611 +eval/path length Std 0 +eval/path length Max 611 +eval/path length Min 611 +eval/Rewards Mean 0.00163666 +eval/Rewards Std 0.0404226 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.020161 +eval/Actions Std 0.743653 +eval/Actions Max 0.99995 +eval/Actions Min -0.999927 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.73088e-06 +time/evaluation sampling (s) 2.50314 +time/exploration sampling (s) 2.98508 +time/logging (s) 0.00585594 +time/saving (s) 0.0096045 +time/training (s) 13.1037 +time/epoch (s) 18.6074 +time/total (s) 23688.9 +Epoch -47 +------------------------------ ---------------- +2022-05-16 00:37:40.014629 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -46 finished +------------------------------ ---------------- +epoch -46 +replay_buffer/size 999047 +trainer/num train calls 955000 +trainer/QF1 Loss 0.637747 +trainer/QF2 Loss 0.756131 +trainer/Policy Loss 39.0136 +trainer/Q1 Predictions Mean -71.5339 +trainer/Q1 Predictions Std 17.9504 +trainer/Q1 Predictions Max 0.395734 +trainer/Q1 Predictions Min -86.8572 +trainer/Q2 Predictions Mean -71.5039 +trainer/Q2 Predictions Std 17.9775 +trainer/Q2 Predictions Max -0.377209 +trainer/Q2 Predictions Min -86.7924 +trainer/Q Targets Mean -71.6868 +trainer/Q Targets Std 17.9769 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.733 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00412131 +trainer/policy/mean Std 0.735536 +trainer/policy/mean Max 0.999141 +trainer/policy/mean Min -0.999351 +trainer/policy/std Mean 0.410493 +trainer/policy/std Std 0.0200253 +trainer/policy/std Max 0.432055 +trainer/policy/std Min 0.381184 +trainer/Advantage Weights Mean 7.19605 +trainer/Advantage Weights Std 22.8395 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.82748e-16 +trainer/Advantage Score Mean -0.306652 +trainer/Advantage Score Std 0.582116 +trainer/Advantage Score Max 1.79031 +trainer/Advantage Score Min -3.45562 +trainer/V1 Predictions Mean -71.5469 +trainer/V1 Predictions Std 17.9652 +trainer/V1 Predictions Max -0.0850372 +trainer/V1 Predictions Min -86.6055 +trainer/VF Loss 0.0853419 +expl/num steps total 955000 +expl/num paths total 1336 +expl/path length Mean 500 +expl/path length Std 65 +expl/path length Max 565 +expl/path length Min 435 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.026223 +expl/Actions Std 0.837327 +expl/Actions Max 2.30472 +expl/Actions Min -2.18855 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 863543 +eval/num paths total 969 +eval/path length Mean 890 +eval/path length Std 0 +eval/path length Max 890 +eval/path length Min 890 +eval/Rewards Mean 0.0011236 +eval/Rewards Std 0.0335012 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0382976 +eval/Actions Std 0.727573 +eval/Actions Max 0.999589 +eval/Actions Min -0.999583 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.61609e-06 +time/evaluation sampling (s) 2.51745 +time/exploration sampling (s) 2.91859 +time/logging (s) 0.00793897 +time/saving (s) 0.0116822 +time/training (s) 13.5804 +time/epoch (s) 19.0361 +time/total (s) 23707.9 +Epoch -46 +------------------------------ ---------------- +2022-05-16 00:37:59.006986 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -45 finished +------------------------------ ---------------- +epoch -45 +replay_buffer/size 999047 +trainer/num train calls 956000 +trainer/QF1 Loss 0.643018 +trainer/QF2 Loss 0.532339 +trainer/Policy Loss 15.1024 +trainer/Q1 Predictions Mean -73.2726 +trainer/Q1 Predictions Std 17.9162 +trainer/Q1 Predictions Max -0.911202 +trainer/Q1 Predictions Min -86.5779 +trainer/Q2 Predictions Mean -73.1761 +trainer/Q2 Predictions Std 17.8174 +trainer/Q2 Predictions Max -1.11549 +trainer/Q2 Predictions Min -87.034 +trainer/Q Targets Mean -73.002 +trainer/Q Targets Std 18.0154 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.7077 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00293948 +trainer/policy/mean Std 0.74627 +trainer/policy/mean Max 0.999839 +trainer/policy/mean Min -0.998699 +trainer/policy/std Mean 0.410106 +trainer/policy/std Std 0.0204428 +trainer/policy/std Max 0.43151 +trainer/policy/std Min 0.381383 +trainer/Advantage Weights Mean 4.549 +trainer/Advantage Weights Std 18.2175 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.76242e-21 +trainer/Advantage Score Mean -0.53874 +trainer/Advantage Score Std 0.735138 +trainer/Advantage Score Max 1.11697 +trainer/Advantage Score Min -4.61838 +trainer/V1 Predictions Mean -72.7505 +trainer/V1 Predictions Std 18.1043 +trainer/V1 Predictions Max 0.570444 +trainer/V1 Predictions Min -86.3497 +trainer/VF Loss 0.102538 +expl/num steps total 956000 +expl/num paths total 1337 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.103757 +expl/Actions Std 0.853639 +expl/Actions Max 2.40061 +expl/Actions Min -2.2046 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 864543 +eval/num paths total 970 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.257269 +eval/Actions Std 0.501201 +eval/Actions Max 0.999561 +eval/Actions Min -0.999911 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93087e-06 +time/evaluation sampling (s) 2.47266 +time/exploration sampling (s) 3.01934 +time/logging (s) 0.00884717 +time/saving (s) 0.0113009 +time/training (s) 13.4659 +time/epoch (s) 18.978 +time/total (s) 23726.9 +Epoch -45 +------------------------------ ---------------- +2022-05-16 00:38:17.420873 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -44 finished +------------------------------ ---------------- +epoch -44 +replay_buffer/size 999047 +trainer/num train calls 957000 +trainer/QF1 Loss 0.55332 +trainer/QF2 Loss 0.489851 +trainer/Policy Loss 19.5461 +trainer/Q1 Predictions Mean -71.6752 +trainer/Q1 Predictions Std 18.2825 +trainer/Q1 Predictions Max -0.842484 +trainer/Q1 Predictions Min -86.7614 +trainer/Q2 Predictions Mean -71.6106 +trainer/Q2 Predictions Std 18.15 +trainer/Q2 Predictions Max -1.68039 +trainer/Q2 Predictions Min -86.4901 +trainer/Q Targets Mean -71.5463 +trainer/Q Targets Std 18.2773 +trainer/Q Targets Max -2.27423 +trainer/Q Targets Min -86.3591 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00742774 +trainer/policy/mean Std 0.729796 +trainer/policy/mean Max 0.999677 +trainer/policy/mean Min -0.999245 +trainer/policy/std Mean 0.40992 +trainer/policy/std Std 0.0212523 +trainer/policy/std Max 0.431538 +trainer/policy/std Min 0.37784 +trainer/Advantage Weights Mean 5.25574 +trainer/Advantage Weights Std 18.9189 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.71273e-21 +trainer/Advantage Score Mean -0.388167 +trainer/Advantage Score Std 0.756122 +trainer/Advantage Score Max 2.24865 +trainer/Advantage Score Min -4.78162 +trainer/V1 Predictions Mean -71.2395 +trainer/V1 Predictions Std 18.4712 +trainer/V1 Predictions Max -1.72257 +trainer/V1 Predictions Min -86.2242 +trainer/VF Loss 0.110248 +expl/num steps total 957000 +expl/num paths total 1339 +expl/path length Mean 500 +expl/path length Std 211 +expl/path length Max 711 +expl/path length Min 289 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0219579 +expl/Actions Std 0.822983 +expl/Actions Max 2.22671 +expl/Actions Min -2.34662 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 865350 +eval/num paths total 971 +eval/path length Mean 807 +eval/path length Std 0 +eval/path length Max 807 +eval/path length Min 807 +eval/Rewards Mean 0.00123916 +eval/Rewards Std 0.0351799 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0330738 +eval/Actions Std 0.742118 +eval/Actions Max 0.999966 +eval/Actions Min -0.999824 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.22005e-06 +time/evaluation sampling (s) 2.46669 +time/exploration sampling (s) 2.91546 +time/logging (s) 0.00689976 +time/saving (s) 0.011025 +time/training (s) 12.9974 +time/epoch (s) 18.3975 +time/total (s) 23745.3 +Epoch -44 +------------------------------ ---------------- +2022-05-16 00:38:36.193255 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -43 finished +------------------------------ ---------------- +epoch -43 +replay_buffer/size 999047 +trainer/num train calls 958000 +trainer/QF1 Loss 1.09049 +trainer/QF2 Loss 1.20389 +trainer/Policy Loss 13.3423 +trainer/Q1 Predictions Mean -71.7533 +trainer/Q1 Predictions Std 18.6292 +trainer/Q1 Predictions Max 0.505255 +trainer/Q1 Predictions Min -87.4584 +trainer/Q2 Predictions Mean -71.6906 +trainer/Q2 Predictions Std 18.4897 +trainer/Q2 Predictions Max -0.495812 +trainer/Q2 Predictions Min -87.5267 +trainer/Q Targets Mean -71.3812 +trainer/Q Targets Std 18.8492 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.8498 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00201245 +trainer/policy/mean Std 0.729238 +trainer/policy/mean Max 0.998682 +trainer/policy/mean Min -0.999594 +trainer/policy/std Mean 0.410333 +trainer/policy/std Std 0.0207102 +trainer/policy/std Max 0.433245 +trainer/policy/std Min 0.376846 +trainer/Advantage Weights Mean 2.95012 +trainer/Advantage Weights Std 14.9982 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.02699e-17 +trainer/Advantage Score Mean -0.632026 +trainer/Advantage Score Std 0.714372 +trainer/Advantage Score Max 1.8808 +trainer/Advantage Score Min -3.91173 +trainer/V1 Predictions Mean -71.1978 +trainer/V1 Predictions Std 18.7141 +trainer/V1 Predictions Max 0.120614 +trainer/V1 Predictions Min -87.5135 +trainer/VF Loss 0.111505 +expl/num steps total 958000 +expl/num paths total 1341 +expl/path length Mean 500 +expl/path length Std 352 +expl/path length Max 852 +expl/path length Min 148 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0259464 +expl/Actions Std 0.816428 +expl/Actions Max 2.44916 +expl/Actions Min -2.30306 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 865855 +eval/num paths total 972 +eval/path length Mean 505 +eval/path length Std 0 +eval/path length Max 505 +eval/path length Min 505 +eval/Rewards Mean 0.0019802 +eval/Rewards Std 0.0444553 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0219425 +eval/Actions Std 0.729655 +eval/Actions Max 0.999821 +eval/Actions Min -0.999867 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.02627e-05 +time/evaluation sampling (s) 2.53413 +time/exploration sampling (s) 3.07755 +time/logging (s) 0.00572241 +time/saving (s) 0.0102981 +time/training (s) 13.1294 +time/epoch (s) 18.7571 +time/total (s) 23764.1 +Epoch -43 +------------------------------ ---------------- +2022-05-16 00:38:54.657851 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -42 finished +------------------------------ ---------------- +epoch -42 +replay_buffer/size 999047 +trainer/num train calls 959000 +trainer/QF1 Loss 0.921889 +trainer/QF2 Loss 0.753611 +trainer/Policy Loss 27.359 +trainer/Q1 Predictions Mean -71.379 +trainer/Q1 Predictions Std 18.1598 +trainer/Q1 Predictions Max -2.88256 +trainer/Q1 Predictions Min -90.2723 +trainer/Q2 Predictions Mean -71.4015 +trainer/Q2 Predictions Std 18.2227 +trainer/Q2 Predictions Max -1.39372 +trainer/Q2 Predictions Min -90.304 +trainer/Q Targets Mean -71.7612 +trainer/Q Targets Std 18.1895 +trainer/Q Targets Max -3.12577 +trainer/Q Targets Min -90.245 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0096465 +trainer/policy/mean Std 0.728428 +trainer/policy/mean Max 0.999417 +trainer/policy/mean Min -0.99878 +trainer/policy/std Mean 0.411287 +trainer/policy/std Std 0.0207909 +trainer/policy/std Max 0.432517 +trainer/policy/std Min 0.378723 +trainer/Advantage Weights Mean 8.40685 +trainer/Advantage Weights Std 24.9255 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.47037e-13 +trainer/Advantage Score Mean -0.222962 +trainer/Advantage Score Std 0.61636 +trainer/Advantage Score Max 2.37897 +trainer/Advantage Score Min -2.86893 +trainer/V1 Predictions Mean -71.5288 +trainer/V1 Predictions Std 18.2775 +trainer/V1 Predictions Max -1.17917 +trainer/V1 Predictions Min -90.5583 +trainer/VF Loss 0.122897 +expl/num steps total 959000 +expl/num paths total 1343 +expl/path length Mean 500 +expl/path length Std 434 +expl/path length Max 934 +expl/path length Min 66 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0431273 +expl/Actions Std 0.849038 +expl/Actions Max 2.19737 +expl/Actions Min -2.5776 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 866394 +eval/num paths total 973 +eval/path length Mean 539 +eval/path length Std 0 +eval/path length Max 539 +eval/path length Min 539 +eval/Rewards Mean 0.00185529 +eval/Rewards Std 0.0430331 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0343313 +eval/Actions Std 0.739347 +eval/Actions Max 0.99989 +eval/Actions Min -0.999953 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.84007e-06 +time/evaluation sampling (s) 2.51798 +time/exploration sampling (s) 2.9398 +time/logging (s) 0.00568051 +time/saving (s) 0.0126663 +time/training (s) 12.9769 +time/epoch (s) 18.453 +time/total (s) 23782.5 +Epoch -42 +------------------------------ ---------------- +2022-05-16 00:39:13.532384 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -41 finished +------------------------------ ---------------- +epoch -41 +replay_buffer/size 999047 +trainer/num train calls 960000 +trainer/QF1 Loss 2.69355 +trainer/QF2 Loss 2.51964 +trainer/Policy Loss 18.1212 +trainer/Q1 Predictions Mean -72.4292 +trainer/Q1 Predictions Std 17.3864 +trainer/Q1 Predictions Max -0.537195 +trainer/Q1 Predictions Min -87.1053 +trainer/Q2 Predictions Mean -72.3613 +trainer/Q2 Predictions Std 17.3853 +trainer/Q2 Predictions Max -0.765793 +trainer/Q2 Predictions Min -87.0241 +trainer/Q Targets Mean -71.9331 +trainer/Q Targets Std 17.1531 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4901 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00895601 +trainer/policy/mean Std 0.731913 +trainer/policy/mean Max 0.998326 +trainer/policy/mean Min -0.999798 +trainer/policy/std Mean 0.411478 +trainer/policy/std Std 0.0204814 +trainer/policy/std Max 0.434842 +trainer/policy/std Min 0.381686 +trainer/Advantage Weights Mean 4.75907 +trainer/Advantage Weights Std 19.8987 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.57081e-25 +trainer/Advantage Score Mean -0.429508 +trainer/Advantage Score Std 0.613116 +trainer/Advantage Score Max 1.25424 +trainer/Advantage Score Min -5.66204 +trainer/V1 Predictions Mean -71.837 +trainer/V1 Predictions Std 17.1881 +trainer/V1 Predictions Max -0.677457 +trainer/V1 Predictions Min -86.471 +trainer/VF Loss 0.0746042 +expl/num steps total 960000 +expl/num paths total 1344 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.210216 +expl/Actions Std 0.835762 +expl/Actions Max 2.45498 +expl/Actions Min -2.19524 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 867183 +eval/num paths total 974 +eval/path length Mean 789 +eval/path length Std 0 +eval/path length Max 789 +eval/path length Min 789 +eval/Rewards Mean 0.00126743 +eval/Rewards Std 0.0355784 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0197195 +eval/Actions Std 0.760876 +eval/Actions Max 0.99999 +eval/Actions Min -0.999828 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.33693e-06 +time/evaluation sampling (s) 2.60098 +time/exploration sampling (s) 3.10835 +time/logging (s) 0.00728677 +time/saving (s) 0.0126574 +time/training (s) 13.133 +time/epoch (s) 18.8623 +time/total (s) 23801.4 +Epoch -41 +------------------------------ ---------------- +2022-05-16 00:39:32.551402 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -40 finished +------------------------------ ---------------- +epoch -40 +replay_buffer/size 999047 +trainer/num train calls 961000 +trainer/QF1 Loss 0.739671 +trainer/QF2 Loss 0.659156 +trainer/Policy Loss 9.88044 +trainer/Q1 Predictions Mean -73.3306 +trainer/Q1 Predictions Std 17.8278 +trainer/Q1 Predictions Max -0.938914 +trainer/Q1 Predictions Min -87.307 +trainer/Q2 Predictions Mean -73.362 +trainer/Q2 Predictions Std 17.8139 +trainer/Q2 Predictions Max -1.09211 +trainer/Q2 Predictions Min -86.9705 +trainer/Q Targets Mean -73.1313 +trainer/Q Targets Std 18.0844 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8728 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0109936 +trainer/policy/mean Std 0.734696 +trainer/policy/mean Max 0.999037 +trainer/policy/mean Min -0.999377 +trainer/policy/std Mean 0.409663 +trainer/policy/std Std 0.0207821 +trainer/policy/std Max 0.432273 +trainer/policy/std Min 0.377886 +trainer/Advantage Weights Mean 2.35213 +trainer/Advantage Weights Std 12.9101 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.918e-18 +trainer/Advantage Score Mean -0.520565 +trainer/Advantage Score Std 0.617913 +trainer/Advantage Score Max 0.940007 +trainer/Advantage Score Min -4.07953 +trainer/V1 Predictions Mean -72.8951 +trainer/V1 Predictions Std 18.0646 +trainer/V1 Predictions Max -0.556031 +trainer/V1 Predictions Min -86.7668 +trainer/VF Loss 0.0733114 +expl/num steps total 961000 +expl/num paths total 1345 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0961946 +expl/Actions Std 0.883134 +expl/Actions Max 2.4303 +expl/Actions Min -2.35115 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 868183 +eval/num paths total 975 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0969599 +eval/Actions Std 0.766441 +eval/Actions Max 0.999935 +eval/Actions Min -0.999623 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.88292e-06 +time/evaluation sampling (s) 2.58179 +time/exploration sampling (s) 2.95374 +time/logging (s) 0.00729203 +time/saving (s) 0.010562 +time/training (s) 13.45 +time/epoch (s) 19.0034 +time/total (s) 23820.4 +Epoch -40 +------------------------------ ---------------- +2022-05-16 00:39:51.576719 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -39 finished +------------------------------ ---------------- +epoch -39 +replay_buffer/size 999047 +trainer/num train calls 962000 +trainer/QF1 Loss 0.540335 +trainer/QF2 Loss 0.530386 +trainer/Policy Loss 14.1175 +trainer/Q1 Predictions Mean -72.2078 +trainer/Q1 Predictions Std 17.2692 +trainer/Q1 Predictions Max 0.511354 +trainer/Q1 Predictions Min -86.2749 +trainer/Q2 Predictions Mean -72.2208 +trainer/Q2 Predictions Std 17.3806 +trainer/Q2 Predictions Max -1.20689 +trainer/Q2 Predictions Min -86.4247 +trainer/Q Targets Mean -72.327 +trainer/Q Targets Std 17.4479 +trainer/Q Targets Max -1.65968 +trainer/Q Targets Min -87.1511 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000847326 +trainer/policy/mean Std 0.727939 +trainer/policy/mean Max 0.998031 +trainer/policy/mean Min -0.999964 +trainer/policy/std Mean 0.411024 +trainer/policy/std Std 0.0201571 +trainer/policy/std Max 0.433925 +trainer/policy/std Min 0.381533 +trainer/Advantage Weights Mean 3.63583 +trainer/Advantage Weights Std 15.6787 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.16766e-15 +trainer/Advantage Score Mean -0.464945 +trainer/Advantage Score Std 0.680421 +trainer/Advantage Score Max 1.40049 +trainer/Advantage Score Min -3.43838 +trainer/V1 Predictions Mean -72.0864 +trainer/V1 Predictions Std 17.5811 +trainer/V1 Predictions Max -0.211538 +trainer/V1 Predictions Min -86.9296 +trainer/VF Loss 0.0897066 +expl/num steps total 962000 +expl/num paths total 1346 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0104796 +expl/Actions Std 0.834952 +expl/Actions Max 2.13884 +expl/Actions Min -2.36512 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 869183 +eval/num paths total 976 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.066937 +eval/Actions Std 0.784817 +eval/Actions Max 0.999735 +eval/Actions Min -0.999824 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.94531e-06 +time/evaluation sampling (s) 2.57197 +time/exploration sampling (s) 2.93127 +time/logging (s) 0.00852907 +time/saving (s) 0.0149124 +time/training (s) 13.4862 +time/epoch (s) 19.0129 +time/total (s) 23839.4 +Epoch -39 +------------------------------ ---------------- +2022-05-16 00:40:10.812813 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -38 finished +------------------------------ ---------------- +epoch -38 +replay_buffer/size 999047 +trainer/num train calls 963000 +trainer/QF1 Loss 1.25782 +trainer/QF2 Loss 1.24425 +trainer/Policy Loss 9.15469 +trainer/Q1 Predictions Mean -71.3292 +trainer/Q1 Predictions Std 18.7108 +trainer/Q1 Predictions Max -1.2669 +trainer/Q1 Predictions Min -87.0523 +trainer/Q2 Predictions Mean -71.3688 +trainer/Q2 Predictions Std 18.7215 +trainer/Q2 Predictions Max -1.89287 +trainer/Q2 Predictions Min -86.9268 +trainer/Q Targets Mean -71.4562 +trainer/Q Targets Std 18.5161 +trainer/Q Targets Max -2.06903 +trainer/Q Targets Min -86.5731 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0103366 +trainer/policy/mean Std 0.73359 +trainer/policy/mean Max 0.99777 +trainer/policy/mean Min -0.998929 +trainer/policy/std Mean 0.410175 +trainer/policy/std Std 0.0211027 +trainer/policy/std Max 0.433004 +trainer/policy/std Min 0.378991 +trainer/Advantage Weights Mean 3.28391 +trainer/Advantage Weights Std 16.4377 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.32509e-17 +trainer/Advantage Score Mean -0.44999 +trainer/Advantage Score Std 0.659044 +trainer/Advantage Score Max 3.57796 +trainer/Advantage Score Min -3.83002 +trainer/V1 Predictions Mean -71.0728 +trainer/V1 Predictions Std 18.9025 +trainer/V1 Predictions Max -1.64909 +trainer/V1 Predictions Min -86.6488 +trainer/VF Loss 0.119496 +expl/num steps total 963000 +expl/num paths total 1347 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0192119 +expl/Actions Std 0.819008 +expl/Actions Max 2.4879 +expl/Actions Min -2.44395 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 870183 +eval/num paths total 977 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.150201 +eval/Actions Std 0.623365 +eval/Actions Max 0.999656 +eval/Actions Min -0.999802 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.4133e-06 +time/evaluation sampling (s) 2.60185 +time/exploration sampling (s) 2.95013 +time/logging (s) 0.00736171 +time/saving (s) 0.0105573 +time/training (s) 13.6481 +time/epoch (s) 19.218 +time/total (s) 23858.7 +Epoch -38 +------------------------------ ---------------- +2022-05-16 00:40:29.292729 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -37 finished +------------------------------ ---------------- +epoch -37 +replay_buffer/size 999047 +trainer/num train calls 964000 +trainer/QF1 Loss 1.01552 +trainer/QF2 Loss 0.944098 +trainer/Policy Loss 18.1864 +trainer/Q1 Predictions Mean -69.7049 +trainer/Q1 Predictions Std 20.5094 +trainer/Q1 Predictions Max 1.67387 +trainer/Q1 Predictions Min -89.1264 +trainer/Q2 Predictions Mean -69.8264 +trainer/Q2 Predictions Std 20.5337 +trainer/Q2 Predictions Max -0.160549 +trainer/Q2 Predictions Min -89.9703 +trainer/Q Targets Mean -69.6332 +trainer/Q Targets Std 20.3601 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.2958 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00149908 +trainer/policy/mean Std 0.731213 +trainer/policy/mean Max 0.999401 +trainer/policy/mean Min -0.99967 +trainer/policy/std Mean 0.409144 +trainer/policy/std Std 0.0216532 +trainer/policy/std Max 0.433123 +trainer/policy/std Min 0.376371 +trainer/Advantage Weights Mean 2.91576 +trainer/Advantage Weights Std 15.9065 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.33993e-16 +trainer/Advantage Score Mean -0.715647 +trainer/Advantage Score Std 0.617904 +trainer/Advantage Score Max 1.36565 +trainer/Advantage Score Min -3.47203 +trainer/V1 Predictions Mean -69.3336 +trainer/V1 Predictions Std 20.4189 +trainer/V1 Predictions Max 0.225279 +trainer/V1 Predictions Min -89.3231 +trainer/VF Loss 0.110272 +expl/num steps total 964000 +expl/num paths total 1349 +expl/path length Mean 500 +expl/path length Std 368 +expl/path length Max 868 +expl/path length Min 132 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0244966 +expl/Actions Std 0.833158 +expl/Actions Max 2.33494 +expl/Actions Min -2.4862 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 870746 +eval/num paths total 978 +eval/path length Mean 563 +eval/path length Std 0 +eval/path length Max 563 +eval/path length Min 563 +eval/Rewards Mean 0.0017762 +eval/Rewards Std 0.0421075 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0220033 +eval/Actions Std 0.728374 +eval/Actions Max 0.999859 +eval/Actions Min -0.999485 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.01267e-05 +time/evaluation sampling (s) 2.54856 +time/exploration sampling (s) 2.92427 +time/logging (s) 0.00608585 +time/saving (s) 0.0116924 +time/training (s) 12.9739 +time/epoch (s) 18.4645 +time/total (s) 23877.1 +Epoch -37 +------------------------------ ---------------- +2022-05-16 00:40:48.491337 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -36 finished +------------------------------ ---------------- +epoch -36 +replay_buffer/size 999047 +trainer/num train calls 965000 +trainer/QF1 Loss 1.0281 +trainer/QF2 Loss 1.02039 +trainer/Policy Loss 13.5322 +trainer/Q1 Predictions Mean -72.1241 +trainer/Q1 Predictions Std 19.2519 +trainer/Q1 Predictions Max 0.448722 +trainer/Q1 Predictions Min -91.0456 +trainer/Q2 Predictions Mean -72.104 +trainer/Q2 Predictions Std 19.2364 +trainer/Q2 Predictions Max 0.494072 +trainer/Q2 Predictions Min -91.1435 +trainer/Q Targets Mean -71.8026 +trainer/Q Targets Std 19.2968 +trainer/Q Targets Max 0.336009 +trainer/Q Targets Min -90.9648 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0232612 +trainer/policy/mean Std 0.731726 +trainer/policy/mean Max 0.999835 +trainer/policy/mean Min -0.99972 +trainer/policy/std Mean 0.410372 +trainer/policy/std Std 0.0210689 +trainer/policy/std Max 0.433664 +trainer/policy/std Min 0.379948 +trainer/Advantage Weights Mean 2.81187 +trainer/Advantage Weights Std 13.9981 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.04197e-18 +trainer/Advantage Score Mean -0.544407 +trainer/Advantage Score Std 0.768392 +trainer/Advantage Score Max 1.28891 +trainer/Advantage Score Min -4.14054 +trainer/V1 Predictions Mean -71.5254 +trainer/V1 Predictions Std 19.6272 +trainer/V1 Predictions Max 1.86935 +trainer/V1 Predictions Min -90.9841 +trainer/VF Loss 0.10194 +expl/num steps total 965000 +expl/num paths total 1351 +expl/path length Mean 500 +expl/path length Std 159 +expl/path length Max 659 +expl/path length Min 341 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.095162 +expl/Actions Std 0.82113 +expl/Actions Max 2.21927 +expl/Actions Min -2.65569 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 871746 +eval/num paths total 979 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.353755 +eval/Actions Std 0.667594 +eval/Actions Max 0.997696 +eval/Actions Min -0.999871 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95881e-06 +time/evaluation sampling (s) 2.77904 +time/exploration sampling (s) 2.9836 +time/logging (s) 0.00751824 +time/saving (s) 0.0122244 +time/training (s) 13.4049 +time/epoch (s) 19.1873 +time/total (s) 23896.3 +Epoch -36 +------------------------------ ---------------- +2022-05-16 00:41:07.527811 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -35 finished +------------------------------ ---------------- +epoch -35 +replay_buffer/size 999047 +trainer/num train calls 966000 +trainer/QF1 Loss 0.852781 +trainer/QF2 Loss 0.999134 +trainer/Policy Loss 23.4669 +trainer/Q1 Predictions Mean -72.7376 +trainer/Q1 Predictions Std 16.1654 +trainer/Q1 Predictions Max -1.42754 +trainer/Q1 Predictions Min -87.6159 +trainer/Q2 Predictions Mean -72.7945 +trainer/Q2 Predictions Std 16.2682 +trainer/Q2 Predictions Max -3.61464 +trainer/Q2 Predictions Min -87.3194 +trainer/Q Targets Mean -72.9153 +trainer/Q Targets Std 16.0845 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.398 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0141908 +trainer/policy/mean Std 0.729152 +trainer/policy/mean Max 0.99882 +trainer/policy/mean Min -0.998943 +trainer/policy/std Mean 0.411434 +trainer/policy/std Std 0.020397 +trainer/policy/std Max 0.434196 +trainer/policy/std Min 0.383192 +trainer/Advantage Weights Mean 6.62173 +trainer/Advantage Weights Std 20.5602 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.67087e-27 +trainer/Advantage Score Mean -0.265206 +trainer/Advantage Score Std 0.687925 +trainer/Advantage Score Max 3.12863 +trainer/Advantage Score Min -6.08694 +trainer/V1 Predictions Mean -72.692 +trainer/V1 Predictions Std 16.2841 +trainer/V1 Predictions Max -1.71415 +trainer/V1 Predictions Min -87.2699 +trainer/VF Loss 0.110576 +expl/num steps total 966000 +expl/num paths total 1353 +expl/path length Mean 500 +expl/path length Std 451 +expl/path length Max 951 +expl/path length Min 49 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0122874 +expl/Actions Std 0.831255 +expl/Actions Max 2.29245 +expl/Actions Min -2.49091 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 872746 +eval/num paths total 980 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.186904 +eval/Actions Std 0.644555 +eval/Actions Max 0.999914 +eval/Actions Min -0.999891 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.1502e-06 +time/evaluation sampling (s) 2.64842 +time/exploration sampling (s) 2.90008 +time/logging (s) 0.00726751 +time/saving (s) 0.0113071 +time/training (s) 13.4549 +time/epoch (s) 19.0219 +time/total (s) 23915.3 +Epoch -35 +------------------------------ ---------------- +2022-05-16 00:41:26.273165 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -34 finished +------------------------------ ---------------- +epoch -34 +replay_buffer/size 999047 +trainer/num train calls 967000 +trainer/QF1 Loss 0.926721 +trainer/QF2 Loss 0.868436 +trainer/Policy Loss 18.9069 +trainer/Q1 Predictions Mean -69.4335 +trainer/Q1 Predictions Std 19.5569 +trainer/Q1 Predictions Max -0.887101 +trainer/Q1 Predictions Min -86.5518 +trainer/Q2 Predictions Mean -69.4779 +trainer/Q2 Predictions Std 19.555 +trainer/Q2 Predictions Max -0.858187 +trainer/Q2 Predictions Min -86.7015 +trainer/Q Targets Mean -69.6402 +trainer/Q Targets Std 19.6205 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1318 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00253487 +trainer/policy/mean Std 0.733839 +trainer/policy/mean Max 0.999797 +trainer/policy/mean Min -0.999483 +trainer/policy/std Mean 0.411086 +trainer/policy/std Std 0.0192961 +trainer/policy/std Max 0.431037 +trainer/policy/std Min 0.384549 +trainer/Advantage Weights Mean 5.3896 +trainer/Advantage Weights Std 20.5866 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.21037e-20 +trainer/Advantage Score Mean -0.473814 +trainer/Advantage Score Std 0.785238 +trainer/Advantage Score Max 1.98118 +trainer/Advantage Score Min -4.52585 +trainer/V1 Predictions Mean -69.3469 +trainer/V1 Predictions Std 19.6575 +trainer/V1 Predictions Max -0.924589 +trainer/V1 Predictions Min -86.8359 +trainer/VF Loss 0.131362 +expl/num steps total 967000 +expl/num paths total 1355 +expl/path length Mean 500 +expl/path length Std 431 +expl/path length Max 931 +expl/path length Min 69 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.030087 +expl/Actions Std 0.83262 +expl/Actions Max 2.4118 +expl/Actions Min -2.31211 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 873746 +eval/num paths total 981 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0655884 +eval/Actions Std 0.768008 +eval/Actions Max 0.999892 +eval/Actions Min -0.999893 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.02879e-05 +time/evaluation sampling (s) 2.51346 +time/exploration sampling (s) 2.9504 +time/logging (s) 0.00795478 +time/saving (s) 0.0144335 +time/training (s) 13.2446 +time/epoch (s) 18.7309 +time/total (s) 23934.1 +Epoch -34 +------------------------------ ---------------- +2022-05-16 00:41:45.071263 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -33 finished +------------------------------ ---------------- +epoch -33 +replay_buffer/size 999047 +trainer/num train calls 968000 +trainer/QF1 Loss 0.911687 +trainer/QF2 Loss 1.02817 +trainer/Policy Loss 10.2948 +trainer/Q1 Predictions Mean -72.025 +trainer/Q1 Predictions Std 18.4404 +trainer/Q1 Predictions Max -0.870888 +trainer/Q1 Predictions Min -86.9523 +trainer/Q2 Predictions Mean -72.1258 +trainer/Q2 Predictions Std 18.3905 +trainer/Q2 Predictions Max -0.166528 +trainer/Q2 Predictions Min -87.2059 +trainer/Q Targets Mean -71.7145 +trainer/Q Targets Std 18.5797 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6365 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0236027 +trainer/policy/mean Std 0.731979 +trainer/policy/mean Max 0.999723 +trainer/policy/mean Min -0.99877 +trainer/policy/std Mean 0.410948 +trainer/policy/std Std 0.019643 +trainer/policy/std Max 0.432629 +trainer/policy/std Min 0.38415 +trainer/Advantage Weights Mean 3.20492 +trainer/Advantage Weights Std 14.1335 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.47438e-14 +trainer/Advantage Score Mean -0.5071 +trainer/Advantage Score Std 0.672403 +trainer/Advantage Score Max 1.35956 +trainer/Advantage Score Min -3.1848 +trainer/V1 Predictions Mean -71.4169 +trainer/V1 Predictions Std 18.5782 +trainer/V1 Predictions Max 0.499745 +trainer/V1 Predictions Min -86.7386 +trainer/VF Loss 0.0848881 +expl/num steps total 968000 +expl/num paths total 1356 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0550055 +expl/Actions Std 0.828311 +expl/Actions Max 2.39563 +expl/Actions Min -2.41118 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 874443 +eval/num paths total 982 +eval/path length Mean 697 +eval/path length Std 0 +eval/path length Max 697 +eval/path length Min 697 +eval/Rewards Mean 0.00143472 +eval/Rewards Std 0.0378505 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0331025 +eval/Actions Std 0.732429 +eval/Actions Max 0.999793 +eval/Actions Min -0.999854 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.03704e-06 +time/evaluation sampling (s) 2.58472 +time/exploration sampling (s) 2.92363 +time/logging (s) 0.00741633 +time/saving (s) 0.0143399 +time/training (s) 13.2353 +time/epoch (s) 18.7654 +time/total (s) 23952.9 +Epoch -33 +------------------------------ ---------------- +2022-05-16 00:42:04.000141 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -32 finished +------------------------------ ---------------- +epoch -32 +replay_buffer/size 999047 +trainer/num train calls 969000 +trainer/QF1 Loss 0.742275 +trainer/QF2 Loss 0.889874 +trainer/Policy Loss 48.173 +trainer/Q1 Predictions Mean -72.3203 +trainer/Q1 Predictions Std 17.3062 +trainer/Q1 Predictions Max -1.70578 +trainer/Q1 Predictions Min -86.6637 +trainer/Q2 Predictions Mean -72.2789 +trainer/Q2 Predictions Std 17.2433 +trainer/Q2 Predictions Max -2.16405 +trainer/Q2 Predictions Min -86.9052 +trainer/Q Targets Mean -72.7167 +trainer/Q Targets Std 17.2913 +trainer/Q Targets Max -3.1061 +trainer/Q Targets Min -88.6845 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00809401 +trainer/policy/mean Std 0.73922 +trainer/policy/mean Max 0.999695 +trainer/policy/mean Min -0.999073 +trainer/policy/std Mean 0.41104 +trainer/policy/std Std 0.0202554 +trainer/policy/std Max 0.434211 +trainer/policy/std Min 0.382405 +trainer/Advantage Weights Mean 11.9455 +trainer/Advantage Weights Std 27.7931 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.27792e-18 +trainer/Advantage Score Mean -0.187612 +trainer/Advantage Score Std 0.69588 +trainer/Advantage Score Max 1.5131 +trainer/Advantage Score Min -4.02593 +trainer/V1 Predictions Mean -72.4275 +trainer/V1 Predictions Std 17.4093 +trainer/V1 Predictions Max -1.95262 +trainer/V1 Predictions Min -87.0078 +trainer/VF Loss 0.101198 +expl/num steps total 969000 +expl/num paths total 1358 +expl/path length Mean 500 +expl/path length Std 456 +expl/path length Max 956 +expl/path length Min 44 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0568188 +expl/Actions Std 0.827827 +expl/Actions Max 2.35975 +expl/Actions Min -2.3076 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 874918 +eval/num paths total 983 +eval/path length Mean 475 +eval/path length Std 0 +eval/path length Max 475 +eval/path length Min 475 +eval/Rewards Mean 0.00210526 +eval/Rewards Std 0.0458348 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00656068 +eval/Actions Std 0.750605 +eval/Actions Max 0.999775 +eval/Actions Min -0.999878 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.14228e-06 +time/evaluation sampling (s) 2.67913 +time/exploration sampling (s) 3.09851 +time/logging (s) 0.00748017 +time/saving (s) 0.014051 +time/training (s) 13.116 +time/epoch (s) 18.9152 +time/total (s) 23971.8 +Epoch -32 +------------------------------ ---------------- +2022-05-16 00:42:22.609244 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -31 finished +------------------------------ ---------------- +epoch -31 +replay_buffer/size 999047 +trainer/num train calls 970000 +trainer/QF1 Loss 1.79833 +trainer/QF2 Loss 1.8461 +trainer/Policy Loss 55.6715 +trainer/Q1 Predictions Mean -73.0839 +trainer/Q1 Predictions Std 15.0324 +trainer/Q1 Predictions Max -4.34105 +trainer/Q1 Predictions Min -86.6075 +trainer/Q2 Predictions Mean -73.1323 +trainer/Q2 Predictions Std 14.9316 +trainer/Q2 Predictions Max -4.55394 +trainer/Q2 Predictions Min -86.627 +trainer/Q Targets Mean -73.8354 +trainer/Q Targets Std 14.9379 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3497 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00123992 +trainer/policy/mean Std 0.730956 +trainer/policy/mean Max 0.999817 +trainer/policy/mean Min -0.999426 +trainer/policy/std Mean 0.410459 +trainer/policy/std Std 0.0195659 +trainer/policy/std Max 0.432016 +trainer/policy/std Min 0.382746 +trainer/Advantage Weights Mean 11.3289 +trainer/Advantage Weights Std 27.506 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.15054e-12 +trainer/Advantage Score Mean -0.175393 +trainer/Advantage Score Std 0.529674 +trainer/Advantage Score Max 1.12398 +trainer/Advantage Score Min -2.74908 +trainer/V1 Predictions Mean -73.5448 +trainer/V1 Predictions Std 15.1182 +trainer/V1 Predictions Max -3.5016 +trainer/V1 Predictions Min -87.1236 +trainer/VF Loss 0.0696439 +expl/num steps total 970000 +expl/num paths total 1360 +expl/path length Mean 500 +expl/path length Std 153 +expl/path length Max 653 +expl/path length Min 347 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0645982 +expl/Actions Std 0.83038 +expl/Actions Max 2.42398 +expl/Actions Min -2.32007 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 875918 +eval/num paths total 984 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0632659 +eval/Actions Std 0.761456 +eval/Actions Max 0.999958 +eval/Actions Min -0.998866 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.97092e-06 +time/evaluation sampling (s) 2.66417 +time/exploration sampling (s) 2.82029 +time/logging (s) 0.00800729 +time/saving (s) 0.0121685 +time/training (s) 13.0882 +time/epoch (s) 18.5928 +time/total (s) 23990.4 +Epoch -31 +------------------------------ ---------------- +2022-05-16 00:42:41.293903 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -30 finished +------------------------------ ---------------- +epoch -30 +replay_buffer/size 999047 +trainer/num train calls 971000 +trainer/QF1 Loss 1.67571 +trainer/QF2 Loss 1.54221 +trainer/Policy Loss 16.2394 +trainer/Q1 Predictions Mean -72.9104 +trainer/Q1 Predictions Std 16.7628 +trainer/Q1 Predictions Max -2.33491 +trainer/Q1 Predictions Min -86.796 +trainer/Q2 Predictions Mean -72.7871 +trainer/Q2 Predictions Std 16.778 +trainer/Q2 Predictions Max -1.83819 +trainer/Q2 Predictions Min -87.0767 +trainer/Q Targets Mean -72.7425 +trainer/Q Targets Std 17.2739 +trainer/Q Targets Max -1.61837 +trainer/Q Targets Min -87.0425 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00787872 +trainer/policy/mean Std 0.728005 +trainer/policy/mean Max 0.999502 +trainer/policy/mean Min -0.999128 +trainer/policy/std Mean 0.410094 +trainer/policy/std Std 0.0204937 +trainer/policy/std Max 0.433285 +trainer/policy/std Min 0.382781 +trainer/Advantage Weights Mean 4.42583 +trainer/Advantage Weights Std 16.0907 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.53756e-27 +trainer/Advantage Score Mean -0.369331 +trainer/Advantage Score Std 0.759998 +trainer/Advantage Score Max 1.93229 +trainer/Advantage Score Min -6.12386 +trainer/V1 Predictions Mean -72.5088 +trainer/V1 Predictions Std 17.2252 +trainer/V1 Predictions Max -0.258026 +trainer/V1 Predictions Min -86.9865 +trainer/VF Loss 0.103141 +expl/num steps total 971000 +expl/num paths total 1361 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0151287 +expl/Actions Std 0.823019 +expl/Actions Max 2.17347 +expl/Actions Min -2.33831 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 876918 +eval/num paths total 985 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.448158 +eval/Actions Std 0.680611 +eval/Actions Max 0.999945 +eval/Actions Min -0.999967 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.71015e-06 +time/evaluation sampling (s) 2.57013 +time/exploration sampling (s) 2.87991 +time/logging (s) 0.00742856 +time/saving (s) 0.0107586 +time/training (s) 13.2011 +time/epoch (s) 18.6693 +time/total (s) 24009.1 +Epoch -30 +------------------------------ ---------------- +2022-05-16 00:42:59.581051 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -29 finished +------------------------------ ---------------- +epoch -29 +replay_buffer/size 999047 +trainer/num train calls 972000 +trainer/QF1 Loss 0.731869 +trainer/QF2 Loss 0.605039 +trainer/Policy Loss 13.4707 +trainer/Q1 Predictions Mean -72.1407 +trainer/Q1 Predictions Std 17.2727 +trainer/Q1 Predictions Max -0.76652 +trainer/Q1 Predictions Min -87.3395 +trainer/Q2 Predictions Mean -72.1803 +trainer/Q2 Predictions Std 17.2063 +trainer/Q2 Predictions Max -0.761807 +trainer/Q2 Predictions Min -87.1744 +trainer/Q Targets Mean -72.024 +trainer/Q Targets Std 17.2603 +trainer/Q Targets Max -1.22019 +trainer/Q Targets Min -87.2063 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00105021 +trainer/policy/mean Std 0.742414 +trainer/policy/mean Max 0.998959 +trainer/policy/mean Min -0.999386 +trainer/policy/std Mean 0.412138 +trainer/policy/std Std 0.0202493 +trainer/policy/std Max 0.43503 +trainer/policy/std Min 0.384784 +trainer/Advantage Weights Mean 3.17181 +trainer/Advantage Weights Std 14.8127 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.99315e-18 +trainer/Advantage Score Mean -0.493687 +trainer/Advantage Score Std 0.585832 +trainer/Advantage Score Max 0.84273 +trainer/Advantage Score Min -3.96559 +trainer/V1 Predictions Mean -71.7787 +trainer/V1 Predictions Std 17.2806 +trainer/V1 Predictions Max 1.57942 +trainer/V1 Predictions Min -87.1048 +trainer/VF Loss 0.0691796 +expl/num steps total 972000 +expl/num paths total 1363 +expl/path length Mean 500 +expl/path length Std 460 +expl/path length Max 960 +expl/path length Min 40 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0460999 +expl/Actions Std 0.832581 +expl/Actions Max 2.14639 +expl/Actions Min -2.46046 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 877500 +eval/num paths total 986 +eval/path length Mean 582 +eval/path length Std 0 +eval/path length Max 582 +eval/path length Min 582 +eval/Rewards Mean 0.00171821 +eval/Rewards Std 0.0414157 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.00623178 +eval/Actions Std 0.729558 +eval/Actions Max 0.999778 +eval/Actions Min -0.999749 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.32901e-06 +time/evaluation sampling (s) 2.51742 +time/exploration sampling (s) 2.76802 +time/logging (s) 0.00712275 +time/saving (s) 0.0135172 +time/training (s) 12.9667 +time/epoch (s) 18.2728 +time/total (s) 24027.3 +Epoch -29 +------------------------------ ---------------- +2022-05-16 00:43:18.365728 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -28 finished +------------------------------ ---------------- +epoch -28 +replay_buffer/size 999047 +trainer/num train calls 973000 +trainer/QF1 Loss 0.71156 +trainer/QF2 Loss 0.601059 +trainer/Policy Loss 4.33113 +trainer/Q1 Predictions Mean -72.4581 +trainer/Q1 Predictions Std 17.2473 +trainer/Q1 Predictions Max -1.34504 +trainer/Q1 Predictions Min -87.9903 +trainer/Q2 Predictions Mean -72.3849 +trainer/Q2 Predictions Std 17.2861 +trainer/Q2 Predictions Max -1.07362 +trainer/Q2 Predictions Min -87.757 +trainer/Q Targets Mean -72.1444 +trainer/Q Targets Std 17.1487 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.188 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0304 +trainer/policy/mean Std 0.736182 +trainer/policy/mean Max 0.999954 +trainer/policy/mean Min -0.998817 +trainer/policy/std Mean 0.410423 +trainer/policy/std Std 0.0210698 +trainer/policy/std Max 0.434349 +trainer/policy/std Min 0.379602 +trainer/Advantage Weights Mean 2.3886 +trainer/Advantage Weights Std 13.1075 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.07789e-35 +trainer/Advantage Score Mean -0.555436 +trainer/Advantage Score Std 0.842923 +trainer/Advantage Score Max 1.62088 +trainer/Advantage Score Min -7.98591 +trainer/V1 Predictions Mean -71.8898 +trainer/V1 Predictions Std 17.23 +trainer/V1 Predictions Max -1.15459 +trainer/V1 Predictions Min -87.1892 +trainer/VF Loss 0.121859 +expl/num steps total 973000 +expl/num paths total 1365 +expl/path length Mean 500 +expl/path length Std 380 +expl/path length Max 880 +expl/path length Min 120 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0270309 +expl/Actions Std 0.808411 +expl/Actions Max 2.39301 +expl/Actions Min -2.354 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 878500 +eval/num paths total 987 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.163621 +eval/Actions Std 0.755885 +eval/Actions Max 0.999907 +eval/Actions Min -0.999906 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.2289e-06 +time/evaluation sampling (s) 2.63179 +time/exploration sampling (s) 2.76584 +time/logging (s) 0.0110259 +time/saving (s) 0.0156321 +time/training (s) 13.3485 +time/epoch (s) 18.7728 +time/total (s) 24046.1 +Epoch -28 +------------------------------ ---------------- +2022-05-16 00:43:37.102235 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -27 finished +------------------------------ ---------------- +epoch -27 +replay_buffer/size 999047 +trainer/num train calls 974000 +trainer/QF1 Loss 0.734152 +trainer/QF2 Loss 0.662217 +trainer/Policy Loss 14.7263 +trainer/Q1 Predictions Mean -73.8973 +trainer/Q1 Predictions Std 16.9677 +trainer/Q1 Predictions Max -1.99521 +trainer/Q1 Predictions Min -89.3388 +trainer/Q2 Predictions Mean -73.8321 +trainer/Q2 Predictions Std 17.0008 +trainer/Q2 Predictions Max -1.84217 +trainer/Q2 Predictions Min -89.2801 +trainer/Q Targets Mean -73.6309 +trainer/Q Targets Std 17.0857 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.4572 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0407061 +trainer/policy/mean Std 0.741071 +trainer/policy/mean Max 0.999944 +trainer/policy/mean Min -0.999617 +trainer/policy/std Mean 0.408972 +trainer/policy/std Std 0.0208711 +trainer/policy/std Max 0.433897 +trainer/policy/std Min 0.37668 +trainer/Advantage Weights Mean 4.12972 +trainer/Advantage Weights Std 17.6057 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.91366e-15 +trainer/Advantage Score Mean -0.412028 +trainer/Advantage Score Std 0.63125 +trainer/Advantage Score Max 2.38159 +trainer/Advantage Score Min -3.29468 +trainer/V1 Predictions Mean -73.4167 +trainer/V1 Predictions Std 17.1987 +trainer/V1 Predictions Max -2.03251 +trainer/V1 Predictions Min -89.2761 +trainer/VF Loss 0.0882559 +expl/num steps total 974000 +expl/num paths total 1367 +expl/path length Mean 500 +expl/path length Std 91 +expl/path length Max 591 +expl/path length Min 409 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0161964 +expl/Actions Std 0.845226 +expl/Actions Max 2.33256 +expl/Actions Min -2.2766 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 879500 +eval/num paths total 988 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.183521 +eval/Actions Std 0.800149 +eval/Actions Max 0.999743 +eval/Actions Min -0.999404 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.26522e-06 +time/evaluation sampling (s) 2.52664 +time/exploration sampling (s) 2.77618 +time/logging (s) 0.00742239 +time/saving (s) 0.0106653 +time/training (s) 13.3931 +time/epoch (s) 18.714 +time/total (s) 24064.8 +Epoch -27 +------------------------------ ---------------- +2022-05-16 00:43:55.676083 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -26 finished +------------------------------ ---------------- +epoch -26 +replay_buffer/size 999047 +trainer/num train calls 975000 +trainer/QF1 Loss 0.985326 +trainer/QF2 Loss 0.851616 +trainer/Policy Loss 38.6395 +trainer/Q1 Predictions Mean -72.6853 +trainer/Q1 Predictions Std 16.0762 +trainer/Q1 Predictions Max -7.91408 +trainer/Q1 Predictions Min -89.2591 +trainer/Q2 Predictions Mean -72.7922 +trainer/Q2 Predictions Std 16.0977 +trainer/Q2 Predictions Max -8.87734 +trainer/Q2 Predictions Min -90.1754 +trainer/Q Targets Mean -73.0723 +trainer/Q Targets Std 15.8169 +trainer/Q Targets Max -7.81371 +trainer/Q Targets Min -90.0369 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0340209 +trainer/policy/mean Std 0.737566 +trainer/policy/mean Max 0.999672 +trainer/policy/mean Min -0.999495 +trainer/policy/std Mean 0.409015 +trainer/policy/std Std 0.0206825 +trainer/policy/std Max 0.431953 +trainer/policy/std Min 0.377103 +trainer/Advantage Weights Mean 8.11882 +trainer/Advantage Weights Std 25.3012 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.09685e-15 +trainer/Advantage Score Mean -0.261409 +trainer/Advantage Score Std 0.574793 +trainer/Advantage Score Max 2.60194 +trainer/Advantage Score Min -3.44463 +trainer/V1 Predictions Mean -72.8117 +trainer/V1 Predictions Std 16.0147 +trainer/V1 Predictions Max -9.0032 +trainer/V1 Predictions Min -90.4353 +trainer/VF Loss 0.102207 +expl/num steps total 975000 +expl/num paths total 1368 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0796946 +expl/Actions Std 0.836823 +expl/Actions Max 2.18326 +expl/Actions Min -2.41718 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 880500 +eval/num paths total 989 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.239829 +eval/Actions Std 0.789895 +eval/Actions Max 0.999903 +eval/Actions Min -0.999782 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.70689e-06 +time/evaluation sampling (s) 2.52092 +time/exploration sampling (s) 2.76954 +time/logging (s) 0.0066743 +time/saving (s) 0.00959606 +time/training (s) 13.2528 +time/epoch (s) 18.5595 +time/total (s) 24083.4 +Epoch -26 +------------------------------ ---------------- +2022-05-16 00:44:14.429744 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -25 finished +------------------------------ ---------------- +epoch -25 +replay_buffer/size 999047 +trainer/num train calls 976000 +trainer/QF1 Loss 1.12335 +trainer/QF2 Loss 0.89389 +trainer/Policy Loss 13.233 +trainer/Q1 Predictions Mean -70.3739 +trainer/Q1 Predictions Std 18.8045 +trainer/Q1 Predictions Max -0.128909 +trainer/Q1 Predictions Min -88.1292 +trainer/Q2 Predictions Mean -70.3268 +trainer/Q2 Predictions Std 18.6739 +trainer/Q2 Predictions Max -0.24346 +trainer/Q2 Predictions Min -87.9298 +trainer/Q Targets Mean -70.2689 +trainer/Q Targets Std 18.4699 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.4943 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0015167 +trainer/policy/mean Std 0.728883 +trainer/policy/mean Max 0.999457 +trainer/policy/mean Min -0.998108 +trainer/policy/std Mean 0.410376 +trainer/policy/std Std 0.0200998 +trainer/policy/std Max 0.433825 +trainer/policy/std Min 0.377903 +trainer/Advantage Weights Mean 2.27504 +trainer/Advantage Weights Std 14.0867 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.5101e-18 +trainer/Advantage Score Mean -0.720758 +trainer/Advantage Score Std 0.763954 +trainer/Advantage Score Max 2.86271 +trainer/Advantage Score Min -4.10344 +trainer/V1 Predictions Mean -69.9532 +trainer/V1 Predictions Std 18.7018 +trainer/V1 Predictions Max -0.0215999 +trainer/V1 Predictions Min -87.5322 +trainer/VF Loss 0.152427 +expl/num steps total 976000 +expl/num paths total 1370 +expl/path length Mean 500 +expl/path length Std 236 +expl/path length Max 736 +expl/path length Min 264 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0375816 +expl/Actions Std 0.833752 +expl/Actions Max 2.57867 +expl/Actions Min -2.43479 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 881270 +eval/num paths total 990 +eval/path length Mean 770 +eval/path length Std 0 +eval/path length Max 770 +eval/path length Min 770 +eval/Rewards Mean 0.0012987 +eval/Rewards Std 0.0360141 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0113687 +eval/Actions Std 0.736208 +eval/Actions Max 0.999926 +eval/Actions Min -0.999967 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.37185e-06 +time/evaluation sampling (s) 2.56503 +time/exploration sampling (s) 2.73826 +time/logging (s) 0.00695817 +time/saving (s) 0.0120915 +time/training (s) 13.4182 +time/epoch (s) 18.7405 +time/total (s) 24102.1 +Epoch -25 +------------------------------ ---------------- +2022-05-16 00:44:33.427391 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -24 finished +------------------------------ ---------------- +epoch -24 +replay_buffer/size 999047 +trainer/num train calls 977000 +trainer/QF1 Loss 11.733 +trainer/QF2 Loss 11.5245 +trainer/Policy Loss 30.607 +trainer/Q1 Predictions Mean -73.1386 +trainer/Q1 Predictions Std 16.8197 +trainer/Q1 Predictions Max -0.675586 +trainer/Q1 Predictions Min -87.2796 +trainer/Q2 Predictions Mean -73.0733 +trainer/Q2 Predictions Std 16.7443 +trainer/Q2 Predictions Max -1.79184 +trainer/Q2 Predictions Min -87.0803 +trainer/Q Targets Mean -73.0333 +trainer/Q Targets Std 16.7013 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4858 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0159981 +trainer/policy/mean Std 0.741504 +trainer/policy/mean Max 0.999887 +trainer/policy/mean Min -0.999519 +trainer/policy/std Mean 0.409694 +trainer/policy/std Std 0.0205276 +trainer/policy/std Max 0.433412 +trainer/policy/std Min 0.377291 +trainer/Advantage Weights Mean 4.75439 +trainer/Advantage Weights Std 17.9326 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.8553e-26 +trainer/Advantage Score Mean -0.355029 +trainer/Advantage Score Std 0.699774 +trainer/Advantage Score Max 2.07351 +trainer/Advantage Score Min -5.8818 +trainer/V1 Predictions Mean -72.546 +trainer/V1 Predictions Std 17.1446 +trainer/V1 Predictions Max 0.356702 +trainer/V1 Predictions Min -86.4259 +trainer/VF Loss 0.0913127 +expl/num steps total 977000 +expl/num paths total 1372 +expl/path length Mean 500 +expl/path length Std 252 +expl/path length Max 752 +expl/path length Min 248 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0387299 +expl/Actions Std 0.845819 +expl/Actions Max 2.61463 +expl/Actions Min -2.35351 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 882270 +eval/num paths total 991 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.062803 +eval/Actions Std 0.789018 +eval/Actions Max 0.999844 +eval/Actions Min -0.999989 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.07121e-05 +time/evaluation sampling (s) 2.83175 +time/exploration sampling (s) 2.75067 +time/logging (s) 0.00889242 +time/saving (s) 0.0128644 +time/training (s) 13.3813 +time/epoch (s) 18.9855 +time/total (s) 24121.1 +Epoch -24 +------------------------------ ---------------- +2022-05-16 00:44:52.081518 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -23 finished +------------------------------ ---------------- +epoch -23 +replay_buffer/size 999047 +trainer/num train calls 978000 +trainer/QF1 Loss 1.38646 +trainer/QF2 Loss 1.35085 +trainer/Policy Loss 24.3223 +trainer/Q1 Predictions Mean -73.1536 +trainer/Q1 Predictions Std 15.9065 +trainer/Q1 Predictions Max -7.03992 +trainer/Q1 Predictions Min -87.4025 +trainer/Q2 Predictions Mean -73.2219 +trainer/Q2 Predictions Std 15.8805 +trainer/Q2 Predictions Max -7.27589 +trainer/Q2 Predictions Min -87.7694 +trainer/Q Targets Mean -73.5329 +trainer/Q Targets Std 15.9602 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.7471 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00667546 +trainer/policy/mean Std 0.730838 +trainer/policy/mean Max 0.997819 +trainer/policy/mean Min -0.999687 +trainer/policy/std Mean 0.40905 +trainer/policy/std Std 0.0191204 +trainer/policy/std Max 0.43175 +trainer/policy/std Min 0.381573 +trainer/Advantage Weights Mean 5.18811 +trainer/Advantage Weights Std 19.4732 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.05606e-37 +trainer/Advantage Score Mean -0.43069 +trainer/Advantage Score Std 0.749849 +trainer/Advantage Score Max 1.68187 +trainer/Advantage Score Min -8.40785 +trainer/V1 Predictions Mean -73.3419 +trainer/V1 Predictions Std 15.8568 +trainer/V1 Predictions Max -9.12451 +trainer/V1 Predictions Min -87.7228 +trainer/VF Loss 0.103119 +expl/num steps total 978000 +expl/num paths total 1373 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0760508 +expl/Actions Std 0.929776 +expl/Actions Max 2.56814 +expl/Actions Min -2.34365 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 882972 +eval/num paths total 992 +eval/path length Mean 702 +eval/path length Std 0 +eval/path length Max 702 +eval/path length Min 702 +eval/Rewards Mean 0.0014245 +eval/Rewards Std 0.0377157 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.046598 +eval/Actions Std 0.724968 +eval/Actions Max 0.999589 +eval/Actions Min -0.999724 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.46918e-06 +time/evaluation sampling (s) 2.50586 +time/exploration sampling (s) 2.74409 +time/logging (s) 0.00608818 +time/saving (s) 0.0107043 +time/training (s) 13.3706 +time/epoch (s) 18.6373 +time/total (s) 24139.8 +Epoch -23 +------------------------------ ---------------- +2022-05-16 00:45:10.625800 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -22 finished +------------------------------ ---------------- +epoch -22 +replay_buffer/size 999047 +trainer/num train calls 979000 +trainer/QF1 Loss 0.754478 +trainer/QF2 Loss 0.670923 +trainer/Policy Loss 13.1384 +trainer/Q1 Predictions Mean -72.5781 +trainer/Q1 Predictions Std 19.2742 +trainer/Q1 Predictions Max -0.593541 +trainer/Q1 Predictions Min -88.106 +trainer/Q2 Predictions Mean -72.5501 +trainer/Q2 Predictions Std 19.2103 +trainer/Q2 Predictions Max -1.04548 +trainer/Q2 Predictions Min -88.1989 +trainer/Q Targets Mean -72.132 +trainer/Q Targets Std 19.263 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.6243 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0150415 +trainer/policy/mean Std 0.734284 +trainer/policy/mean Max 0.999753 +trainer/policy/mean Min -0.998983 +trainer/policy/std Mean 0.409361 +trainer/policy/std Std 0.0185903 +trainer/policy/std Max 0.430184 +trainer/policy/std Min 0.382744 +trainer/Advantage Weights Mean 1.6047 +trainer/Advantage Weights Std 11.1625 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 3.55185e-18 +trainer/Advantage Score Mean -0.551466 +trainer/Advantage Score Std 0.588501 +trainer/Advantage Score Max 2.1965 +trainer/Advantage Score Min -4.01791 +trainer/V1 Predictions Mean -71.9088 +trainer/V1 Predictions Std 19.2027 +trainer/V1 Predictions Max -1.01516 +trainer/V1 Predictions Min -87.5441 +trainer/VF Loss 0.0860803 +expl/num steps total 979000 +expl/num paths total 1375 +expl/path length Mean 500 +expl/path length Std 336 +expl/path length Max 836 +expl/path length Min 164 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0272025 +expl/Actions Std 0.841915 +expl/Actions Max 2.48501 +expl/Actions Min -2.96642 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 883972 +eval/num paths total 993 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.412288 +eval/Actions Std 0.583206 +eval/Actions Max 0.999616 +eval/Actions Min -0.999926 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.14392e-06 +time/evaluation sampling (s) 2.52522 +time/exploration sampling (s) 2.67656 +time/logging (s) 0.00914621 +time/saving (s) 0.0141395 +time/training (s) 13.3106 +time/epoch (s) 18.5357 +time/total (s) 24158.3 +Epoch -22 +------------------------------ ---------------- +2022-05-16 00:45:29.004589 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -21 finished +------------------------------ ---------------- +epoch -21 +replay_buffer/size 999047 +trainer/num train calls 980000 +trainer/QF1 Loss 0.429509 +trainer/QF2 Loss 0.418416 +trainer/Policy Loss 15.5672 +trainer/Q1 Predictions Mean -73.972 +trainer/Q1 Predictions Std 16.1419 +trainer/Q1 Predictions Max -0.709673 +trainer/Q1 Predictions Min -87.3954 +trainer/Q2 Predictions Mean -74.0798 +trainer/Q2 Predictions Std 16.1401 +trainer/Q2 Predictions Max -1.27635 +trainer/Q2 Predictions Min -87.4564 +trainer/Q Targets Mean -73.8748 +trainer/Q Targets Std 16.1884 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2334 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0086798 +trainer/policy/mean Std 0.732889 +trainer/policy/mean Max 0.999763 +trainer/policy/mean Min -0.999465 +trainer/policy/std Mean 0.408954 +trainer/policy/std Std 0.0198524 +trainer/policy/std Max 0.4323 +trainer/policy/std Min 0.38093 +trainer/Advantage Weights Mean 5.6311 +trainer/Advantage Weights Std 21.1368 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.79605e-11 +trainer/Advantage Score Mean -0.335515 +trainer/Advantage Score Std 0.574569 +trainer/Advantage Score Max 1.87741 +trainer/Advantage Score Min -2.37606 +trainer/V1 Predictions Mean -73.7114 +trainer/V1 Predictions Std 16.1 +trainer/V1 Predictions Max 0.241359 +trainer/V1 Predictions Min -87.0907 +trainer/VF Loss 0.0866889 +expl/num steps total 980000 +expl/num paths total 1377 +expl/path length Mean 500 +expl/path length Std 252 +expl/path length Max 752 +expl/path length Min 248 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0343097 +expl/Actions Std 0.834384 +expl/Actions Max 2.61679 +expl/Actions Min -2.41522 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 884972 +eval/num paths total 994 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.443486 +eval/Actions Std 0.550725 +eval/Actions Max 0.999516 +eval/Actions Min -0.999934 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.97232e-06 +time/evaluation sampling (s) 2.49389 +time/exploration sampling (s) 2.82026 +time/logging (s) 0.00793354 +time/saving (s) 0.0103515 +time/training (s) 13.0296 +time/epoch (s) 18.362 +time/total (s) 24176.7 +Epoch -21 +------------------------------ ---------------- +2022-05-16 00:45:47.743155 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -20 finished +------------------------------ ---------------- +epoch -20 +replay_buffer/size 999047 +trainer/num train calls 981000 +trainer/QF1 Loss 0.394256 +trainer/QF2 Loss 0.433801 +trainer/Policy Loss 4.40229 +trainer/Q1 Predictions Mean -73.2355 +trainer/Q1 Predictions Std 17.0487 +trainer/Q1 Predictions Max 0.423509 +trainer/Q1 Predictions Min -90.2507 +trainer/Q2 Predictions Mean -73.224 +trainer/Q2 Predictions Std 16.9485 +trainer/Q2 Predictions Max -0.355162 +trainer/Q2 Predictions Min -89.693 +trainer/Q Targets Mean -72.9907 +trainer/Q Targets Std 16.9674 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.2769 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0220817 +trainer/policy/mean Std 0.736112 +trainer/policy/mean Max 0.999867 +trainer/policy/mean Min -0.999738 +trainer/policy/std Mean 0.408367 +trainer/policy/std Std 0.0200893 +trainer/policy/std Max 0.431145 +trainer/policy/std Min 0.378386 +trainer/Advantage Weights Mean 1.41649 +trainer/Advantage Weights Std 9.24342 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.02849e-17 +trainer/Advantage Score Mean -0.506914 +trainer/Advantage Score Std 0.595943 +trainer/Advantage Score Max 1.87586 +trainer/Advantage Score Min -3.73475 +trainer/V1 Predictions Mean -72.7028 +trainer/V1 Predictions Std 17.1567 +trainer/V1 Predictions Max 0.83744 +trainer/V1 Predictions Min -89.7407 +trainer/VF Loss 0.0786831 +expl/num steps total 981000 +expl/num paths total 1378 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.141957 +expl/Actions Std 0.854912 +expl/Actions Max 2.1724 +expl/Actions Min -2.22519 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 885972 +eval/num paths total 995 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.204873 +eval/Actions Std 0.735436 +eval/Actions Max 0.999743 +eval/Actions Min -0.999772 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.95695e-06 +time/evaluation sampling (s) 2.52835 +time/exploration sampling (s) 2.82918 +time/logging (s) 0.00740481 +time/saving (s) 0.0120098 +time/training (s) 13.3466 +time/epoch (s) 18.7235 +time/total (s) 24195.4 +Epoch -20 +------------------------------ ---------------- +2022-05-16 00:46:06.218849 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -19 finished +------------------------------ ---------------- +epoch -19 +replay_buffer/size 999047 +trainer/num train calls 982000 +trainer/QF1 Loss 1.09812 +trainer/QF2 Loss 0.934754 +trainer/Policy Loss 38.8197 +trainer/Q1 Predictions Mean -71.5937 +trainer/Q1 Predictions Std 19.0011 +trainer/Q1 Predictions Max -0.0121583 +trainer/Q1 Predictions Min -86.333 +trainer/Q2 Predictions Mean -71.6549 +trainer/Q2 Predictions Std 19.0809 +trainer/Q2 Predictions Max -0.352593 +trainer/Q2 Predictions Min -86.264 +trainer/Q Targets Mean -72.032 +trainer/Q Targets Std 18.854 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.937 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000198314 +trainer/policy/mean Std 0.72993 +trainer/policy/mean Max 0.998816 +trainer/policy/mean Min -0.99854 +trainer/policy/std Mean 0.410761 +trainer/policy/std Std 0.0212256 +trainer/policy/std Max 0.435561 +trainer/policy/std Min 0.379765 +trainer/Advantage Weights Mean 7.37433 +trainer/Advantage Weights Std 22.8123 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.16492e-13 +trainer/Advantage Score Mean -0.318008 +trainer/Advantage Score Std 0.610963 +trainer/Advantage Score Max 1.84889 +trainer/Advantage Score Min -2.91612 +trainer/V1 Predictions Mean -71.7116 +trainer/V1 Predictions Std 18.9901 +trainer/V1 Predictions Max -0.603342 +trainer/V1 Predictions Min -86.5764 +trainer/VF Loss 0.0962627 +expl/num steps total 982000 +expl/num paths total 1380 +expl/path length Mean 500 +expl/path length Std 240 +expl/path length Max 740 +expl/path length Min 260 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0267678 +expl/Actions Std 0.827984 +expl/Actions Max 2.39115 +expl/Actions Min -2.22304 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 886972 +eval/num paths total 996 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0167203 +eval/Actions Std 0.723989 +eval/Actions Max 0.999888 +eval/Actions Min -0.999846 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.93879e-06 +time/evaluation sampling (s) 2.41663 +time/exploration sampling (s) 2.71354 +time/logging (s) 0.0115809 +time/saving (s) 0.0160434 +time/training (s) 13.3073 +time/epoch (s) 18.4651 +time/total (s) 24213.9 +Epoch -19 +------------------------------ ---------------- +2022-05-16 00:46:25.007517 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -18 finished +------------------------------ ---------------- +epoch -18 +replay_buffer/size 999047 +trainer/num train calls 983000 +trainer/QF1 Loss 1.11518 +trainer/QF2 Loss 1.11038 +trainer/Policy Loss 48.3778 +trainer/Q1 Predictions Mean -71.9425 +trainer/Q1 Predictions Std 17.7303 +trainer/Q1 Predictions Max 0.832133 +trainer/Q1 Predictions Min -87.4098 +trainer/Q2 Predictions Mean -71.9701 +trainer/Q2 Predictions Std 17.7062 +trainer/Q2 Predictions Max 0.59435 +trainer/Q2 Predictions Min -87.3931 +trainer/Q Targets Mean -72.5959 +trainer/Q Targets Std 17.7624 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.2887 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0111724 +trainer/policy/mean Std 0.729948 +trainer/policy/mean Max 0.998515 +trainer/policy/mean Min -0.999481 +trainer/policy/std Mean 0.409701 +trainer/policy/std Std 0.0198539 +trainer/policy/std Max 0.432987 +trainer/policy/std Min 0.382403 +trainer/Advantage Weights Mean 13.7782 +trainer/Advantage Weights Std 29.6838 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.19978e-21 +trainer/Advantage Score Mean -0.140204 +trainer/Advantage Score Std 0.711866 +trainer/Advantage Score Max 2.64094 +trainer/Advantage Score Min -4.81722 +trainer/V1 Predictions Mean -72.3666 +trainer/V1 Predictions Std 17.9088 +trainer/V1 Predictions Max -0.517379 +trainer/V1 Predictions Min -88.1821 +trainer/VF Loss 0.136583 +expl/num steps total 983000 +expl/num paths total 1382 +expl/path length Mean 500 +expl/path length Std 351 +expl/path length Max 851 +expl/path length Min 149 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.00584559 +expl/Actions Std 0.839769 +expl/Actions Max 2.27523 +expl/Actions Min -2.22689 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 887972 +eval/num paths total 997 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.126547 +eval/Actions Std 0.699292 +eval/Actions Max 0.999695 +eval/Actions Min -0.999721 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.37185e-06 +time/evaluation sampling (s) 2.46593 +time/exploration sampling (s) 2.82674 +time/logging (s) 0.00675952 +time/saving (s) 0.00951634 +time/training (s) 13.4552 +time/epoch (s) 18.7641 +time/total (s) 24232.6 +Epoch -18 +------------------------------ ---------------- +2022-05-16 00:46:44.135501 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -17 finished +------------------------------ ---------------- +epoch -17 +replay_buffer/size 999047 +trainer/num train calls 984000 +trainer/QF1 Loss 1.18664 +trainer/QF2 Loss 1.09424 +trainer/Policy Loss 27.9715 +trainer/Q1 Predictions Mean -72.3934 +trainer/Q1 Predictions Std 17.8315 +trainer/Q1 Predictions Max -0.394731 +trainer/Q1 Predictions Min -89.3978 +trainer/Q2 Predictions Mean -72.3518 +trainer/Q2 Predictions Std 17.8795 +trainer/Q2 Predictions Max -0.127191 +trainer/Q2 Predictions Min -89.3615 +trainer/Q Targets Mean -72.8233 +trainer/Q Targets Std 17.9123 +trainer/Q Targets Max 0 +trainer/Q Targets Min -89.6634 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0203301 +trainer/policy/mean Std 0.742035 +trainer/policy/mean Max 0.998868 +trainer/policy/mean Min -0.999643 +trainer/policy/std Mean 0.408961 +trainer/policy/std Std 0.0200508 +trainer/policy/std Max 0.429272 +trainer/policy/std Min 0.379977 +trainer/Advantage Weights Mean 8.27383 +trainer/Advantage Weights Std 22.7951 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.17396e-19 +trainer/Advantage Score Mean -0.17985 +trainer/Advantage Score Std 0.530179 +trainer/Advantage Score Max 1.7025 +trainer/Advantage Score Min -4.16482 +trainer/V1 Predictions Mean -72.5568 +trainer/V1 Predictions Std 17.97 +trainer/V1 Predictions Max 0.184367 +trainer/V1 Predictions Min -89.737 +trainer/VF Loss 0.0726243 +expl/num steps total 984000 +expl/num paths total 1383 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.05848 +expl/Actions Std 0.881496 +expl/Actions Max 2.34824 +expl/Actions Min -2.5665 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 888673 +eval/num paths total 998 +eval/path length Mean 701 +eval/path length Std 0 +eval/path length Max 701 +eval/path length Min 701 +eval/Rewards Mean 0.00142653 +eval/Rewards Std 0.0377425 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0388725 +eval/Actions Std 0.737537 +eval/Actions Max 0.999917 +eval/Actions Min -0.99996 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.6091e-06 +time/evaluation sampling (s) 2.35775 +time/exploration sampling (s) 2.76338 +time/logging (s) 0.00685998 +time/saving (s) 0.0135827 +time/training (s) 13.973 +time/epoch (s) 19.1146 +time/total (s) 24251.8 +Epoch -17 +------------------------------ ---------------- +2022-05-16 00:47:02.973521 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -16 finished +------------------------------ ---------------- +epoch -16 +replay_buffer/size 999047 +trainer/num train calls 985000 +trainer/QF1 Loss 1.17558 +trainer/QF2 Loss 1.24023 +trainer/Policy Loss 29.284 +trainer/Q1 Predictions Mean -70.1691 +trainer/Q1 Predictions Std 19.0654 +trainer/Q1 Predictions Max -1.93997 +trainer/Q1 Predictions Min -86.4585 +trainer/Q2 Predictions Mean -70.2503 +trainer/Q2 Predictions Std 19.101 +trainer/Q2 Predictions Max -1.61041 +trainer/Q2 Predictions Min -87.2332 +trainer/Q Targets Mean -70.7058 +trainer/Q Targets Std 19.1229 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.2208 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.028533 +trainer/policy/mean Std 0.729557 +trainer/policy/mean Max 0.998849 +trainer/policy/mean Min -0.999448 +trainer/policy/std Mean 0.408975 +trainer/policy/std Std 0.0191956 +trainer/policy/std Max 0.428053 +trainer/policy/std Min 0.384058 +trainer/Advantage Weights Mean 6.73504 +trainer/Advantage Weights Std 22.2862 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.55868e-23 +trainer/Advantage Score Mean -0.443164 +trainer/Advantage Score Std 0.851791 +trainer/Advantage Score Max 1.09086 +trainer/Advantage Score Min -5.08125 +trainer/V1 Predictions Mean -70.3112 +trainer/V1 Predictions Std 19.4763 +trainer/V1 Predictions Max -1.74524 +trainer/V1 Predictions Min -87.2289 +trainer/VF Loss 0.115915 +expl/num steps total 985000 +expl/num paths total 1384 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean -0.0373501 +expl/Actions Std 0.8564 +expl/Actions Max 2.20187 +expl/Actions Min -2.33173 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 889673 +eval/num paths total 999 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.0337726 +eval/Actions Std 0.708565 +eval/Actions Max 0.99911 +eval/Actions Min -0.999541 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.04589e-06 +time/evaluation sampling (s) 2.31318 +time/exploration sampling (s) 2.8523 +time/logging (s) 0.0179584 +time/saving (s) 0.0204032 +time/training (s) 13.6313 +time/epoch (s) 18.8351 +time/total (s) 24270.6 +Epoch -16 +------------------------------ ---------------- +2022-05-16 00:47:21.816143 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -15 finished +------------------------------ ---------------- +epoch -15 +replay_buffer/size 999047 +trainer/num train calls 986000 +trainer/QF1 Loss 0.70199 +trainer/QF2 Loss 0.666825 +trainer/Policy Loss 21.592 +trainer/Q1 Predictions Mean -70.8492 +trainer/Q1 Predictions Std 19.7403 +trainer/Q1 Predictions Max -1.41842 +trainer/Q1 Predictions Min -87.4993 +trainer/Q2 Predictions Mean -70.8978 +trainer/Q2 Predictions Std 19.735 +trainer/Q2 Predictions Max -1.25631 +trainer/Q2 Predictions Min -87.3835 +trainer/Q Targets Mean -70.9611 +trainer/Q Targets Std 19.9838 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.1948 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00406689 +trainer/policy/mean Std 0.744816 +trainer/policy/mean Max 0.999625 +trainer/policy/mean Min -0.999579 +trainer/policy/std Mean 0.408379 +trainer/policy/std Std 0.0205358 +trainer/policy/std Max 0.431841 +trainer/policy/std Min 0.379873 +trainer/Advantage Weights Mean 4.71151 +trainer/Advantage Weights Std 19.3379 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.23879e-18 +trainer/Advantage Score Mean -0.457197 +trainer/Advantage Score Std 0.724105 +trainer/Advantage Score Max 1.27597 +trainer/Advantage Score Min -3.96157 +trainer/V1 Predictions Mean -70.7273 +trainer/V1 Predictions Std 20.0398 +trainer/V1 Predictions Max -0.269156 +trainer/V1 Predictions Min -87.2944 +trainer/VF Loss 0.0968539 +expl/num steps total 986000 +expl/num paths total 1386 +expl/path length Mean 500 +expl/path length Std 483 +expl/path length Max 983 +expl/path length Min 17 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.041109 +expl/Actions Std 0.82477 +expl/Actions Max 2.20768 +expl/Actions Min -2.44412 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 890335 +eval/num paths total 1000 +eval/path length Mean 662 +eval/path length Std 0 +eval/path length Max 662 +eval/path length Min 662 +eval/Rewards Mean 0.00151057 +eval/Rewards Std 0.0388367 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0238657 +eval/Actions Std 0.734331 +eval/Actions Max 0.999696 +eval/Actions Min -0.99959 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 1.25533e-05 +time/evaluation sampling (s) 2.41016 +time/exploration sampling (s) 2.8842 +time/logging (s) 0.00658088 +time/saving (s) 0.010715 +time/training (s) 13.5016 +time/epoch (s) 18.8133 +time/total (s) 24289.4 +Epoch -15 +------------------------------ ---------------- +2022-05-16 00:47:40.559065 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -14 finished +------------------------------ ---------------- +epoch -14 +replay_buffer/size 999047 +trainer/num train calls 987000 +trainer/QF1 Loss 0.532135 +trainer/QF2 Loss 0.509778 +trainer/Policy Loss 29.4838 +trainer/Q1 Predictions Mean -71.169 +trainer/Q1 Predictions Std 18.4428 +trainer/Q1 Predictions Max -1.29447 +trainer/Q1 Predictions Min -86.8497 +trainer/Q2 Predictions Mean -71.1762 +trainer/Q2 Predictions Std 18.4527 +trainer/Q2 Predictions Max -1.96789 +trainer/Q2 Predictions Min -86.9123 +trainer/Q Targets Mean -71.1641 +trainer/Q Targets Std 18.4597 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.8783 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00787234 +trainer/policy/mean Std 0.729653 +trainer/policy/mean Max 0.999221 +trainer/policy/mean Min -0.998769 +trainer/policy/std Mean 0.408262 +trainer/policy/std Std 0.0188884 +trainer/policy/std Max 0.431736 +trainer/policy/std Min 0.382066 +trainer/Advantage Weights Mean 5.1186 +trainer/Advantage Weights Std 20.4465 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.27828e-18 +trainer/Advantage Score Mean -0.438266 +trainer/Advantage Score Std 0.72641 +trainer/Advantage Score Max 3.00093 +trainer/Advantage Score Min -3.9993 +trainer/V1 Predictions Mean -70.9118 +trainer/V1 Predictions Std 18.5014 +trainer/V1 Predictions Max -1.66038 +trainer/V1 Predictions Min -86.7044 +trainer/VF Loss 0.158094 +expl/num steps total 987000 +expl/num paths total 1388 +expl/path length Mean 500 +expl/path length Std 59 +expl/path length Max 559 +expl/path length Min 441 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0241209 +expl/Actions Std 0.839975 +expl/Actions Max 2.47145 +expl/Actions Min -2.14642 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 891335 +eval/num paths total 1001 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0227269 +eval/Actions Std 0.745601 +eval/Actions Max 0.999949 +eval/Actions Min -0.999886 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.15579e-06 +time/evaluation sampling (s) 2.43451 +time/exploration sampling (s) 2.87592 +time/logging (s) 0.0114632 +time/saving (s) 0.018482 +time/training (s) 13.3953 +time/epoch (s) 18.7357 +time/total (s) 24308.2 +Epoch -14 +------------------------------ ---------------- +2022-05-16 00:47:59.100592 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -13 finished +------------------------------ ---------------- +epoch -13 +replay_buffer/size 999047 +trainer/num train calls 988000 +trainer/QF1 Loss 0.948031 +trainer/QF2 Loss 0.788681 +trainer/Policy Loss 18.1252 +trainer/Q1 Predictions Mean -73.9399 +trainer/Q1 Predictions Std 15.7358 +trainer/Q1 Predictions Max -0.211329 +trainer/Q1 Predictions Min -87.2498 +trainer/Q2 Predictions Mean -73.9171 +trainer/Q2 Predictions Std 15.7475 +trainer/Q2 Predictions Max -0.395526 +trainer/Q2 Predictions Min -87.2084 +trainer/Q Targets Mean -73.7073 +trainer/Q Targets Std 15.8458 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6286 +trainer/rewards Mean -0.988281 +trainer/rewards Std 0.107617 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0117188 +trainer/terminals Std 0.107617 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00770272 +trainer/policy/mean Std 0.743141 +trainer/policy/mean Max 0.999311 +trainer/policy/mean Min -0.999293 +trainer/policy/std Mean 0.409148 +trainer/policy/std Std 0.0204683 +trainer/policy/std Max 0.433587 +trainer/policy/std Min 0.379662 +trainer/Advantage Weights Mean 5.1351 +trainer/Advantage Weights Std 19.2961 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.27732e-19 +trainer/Advantage Score Mean -0.426109 +trainer/Advantage Score Std 0.68878 +trainer/Advantage Score Max 1.36195 +trainer/Advantage Score Min -4.35044 +trainer/V1 Predictions Mean -73.4667 +trainer/V1 Predictions Std 16.0004 +trainer/V1 Predictions Max 2.1292 +trainer/V1 Predictions Min -86.4936 +trainer/VF Loss 0.0861051 +expl/num steps total 988000 +expl/num paths total 1389 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.146287 +expl/Actions Std 0.850693 +expl/Actions Max 2.24982 +expl/Actions Min -2.25401 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 892332 +eval/num paths total 1003 +eval/path length Mean 498.5 +eval/path length Std 161.5 +eval/path length Max 660 +eval/path length Min 337 +eval/Rewards Mean 0.00200602 +eval/Rewards Std 0.0447436 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0293594 +eval/Actions Std 0.748777 +eval/Actions Max 0.999916 +eval/Actions Min -0.999915 +eval/Num Paths 2 +eval/Average Returns 1 +time/data storing (s) 3.57395e-06 +time/evaluation sampling (s) 2.38165 +time/exploration sampling (s) 2.92147 +time/logging (s) 0.0112292 +time/saving (s) 0.0150017 +time/training (s) 13.1881 +time/epoch (s) 18.5175 +time/total (s) 24326.7 +Epoch -13 +------------------------------ ---------------- +2022-05-16 00:48:17.161098 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -12 finished +------------------------------ ---------------- +epoch -12 +replay_buffer/size 999047 +trainer/num train calls 989000 +trainer/QF1 Loss 0.916287 +trainer/QF2 Loss 0.76067 +trainer/Policy Loss 17.4704 +trainer/Q1 Predictions Mean -72.5429 +trainer/Q1 Predictions Std 16.5766 +trainer/Q1 Predictions Max -0.729286 +trainer/Q1 Predictions Min -86.7276 +trainer/Q2 Predictions Mean -72.5155 +trainer/Q2 Predictions Std 16.5599 +trainer/Q2 Predictions Max -0.211292 +trainer/Q2 Predictions Min -86.6015 +trainer/Q Targets Mean -72.6649 +trainer/Q Targets Std 16.4473 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.952 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00123614 +trainer/policy/mean Std 0.73175 +trainer/policy/mean Max 0.999041 +trainer/policy/mean Min -0.998784 +trainer/policy/std Mean 0.410079 +trainer/policy/std Std 0.0203543 +trainer/policy/std Max 0.435271 +trainer/policy/std Min 0.381621 +trainer/Advantage Weights Mean 4.06003 +trainer/Advantage Weights Std 17.4446 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.70472e-16 +trainer/Advantage Score Mean -0.512615 +trainer/Advantage Score Std 0.637621 +trainer/Advantage Score Max 3.51878 +trainer/Advantage Score Min -3.45687 +trainer/V1 Predictions Mean -72.3821 +trainer/V1 Predictions Std 16.5567 +trainer/V1 Predictions Max -0.533788 +trainer/V1 Predictions Min -86.6234 +trainer/VF Loss 0.125289 +expl/num steps total 989000 +expl/num paths total 1391 +expl/path length Mean 500 +expl/path length Std 270 +expl/path length Max 770 +expl/path length Min 230 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0312054 +expl/Actions Std 0.824042 +expl/Actions Max 2.35918 +expl/Actions Min -2.36146 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 893332 +eval/num paths total 1004 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0257064 +eval/Actions Std 0.738046 +eval/Actions Max 0.999794 +eval/Actions Min -0.999672 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.45497e-06 +time/evaluation sampling (s) 2.43412 +time/exploration sampling (s) 2.89386 +time/logging (s) 0.00842114 +time/saving (s) 0.0136673 +time/training (s) 12.6874 +time/epoch (s) 18.0375 +time/total (s) 24344.7 +Epoch -12 +------------------------------ ---------------- +2022-05-16 00:48:35.195268 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -11 finished +------------------------------ ---------------- +epoch -11 +replay_buffer/size 999047 +trainer/num train calls 990000 +trainer/QF1 Loss 0.627996 +trainer/QF2 Loss 0.778737 +trainer/Policy Loss 20.6556 +trainer/Q1 Predictions Mean -71.5558 +trainer/Q1 Predictions Std 17.8414 +trainer/Q1 Predictions Max -1.692 +trainer/Q1 Predictions Min -89.9237 +trainer/Q2 Predictions Mean -71.4933 +trainer/Q2 Predictions Std 17.8745 +trainer/Q2 Predictions Max -1.79368 +trainer/Q2 Predictions Min -89.9234 +trainer/Q Targets Mean -71.5925 +trainer/Q Targets Std 17.5993 +trainer/Q Targets Max -3.4464 +trainer/Q Targets Min -89.9316 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00772013 +trainer/policy/mean Std 0.727088 +trainer/policy/mean Max 0.999506 +trainer/policy/mean Min -0.999491 +trainer/policy/std Mean 0.410182 +trainer/policy/std Std 0.0199498 +trainer/policy/std Max 0.433671 +trainer/policy/std Min 0.382939 +trainer/Advantage Weights Mean 5.51982 +trainer/Advantage Weights Std 20.6778 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 4.03653e-16 +trainer/Advantage Score Mean -0.392863 +trainer/Advantage Score Std 0.563248 +trainer/Advantage Score Max 1.49544 +trainer/Advantage Score Min -3.5446 +trainer/V1 Predictions Mean -71.3033 +trainer/V1 Predictions Std 17.7272 +trainer/V1 Predictions Max -1.74793 +trainer/V1 Predictions Min -88.4231 +trainer/VF Loss 0.0762183 +expl/num steps total 990000 +expl/num paths total 1393 +expl/path length Mean 500 +expl/path length Std 94 +expl/path length Max 594 +expl/path length Min 406 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0357945 +expl/Actions Std 0.844812 +expl/Actions Max 2.42078 +expl/Actions Min -2.24125 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 894332 +eval/num paths total 1005 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.454256 +eval/Actions Std 0.641281 +eval/Actions Max 0.999252 +eval/Actions Min -0.999379 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.67709e-06 +time/evaluation sampling (s) 2.42332 +time/exploration sampling (s) 2.93358 +time/logging (s) 0.00689135 +time/saving (s) 0.0116487 +time/training (s) 12.6421 +time/epoch (s) 18.0176 +time/total (s) 24362.8 +Epoch -11 +------------------------------ ---------------- +2022-05-16 00:48:53.701323 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -10 finished +------------------------------ ---------------- +epoch -10 +replay_buffer/size 999047 +trainer/num train calls 991000 +trainer/QF1 Loss 0.470944 +trainer/QF2 Loss 0.421158 +trainer/Policy Loss 19.5498 +trainer/Q1 Predictions Mean -72.8993 +trainer/Q1 Predictions Std 16.9193 +trainer/Q1 Predictions Max -0.586215 +trainer/Q1 Predictions Min -87.1823 +trainer/Q2 Predictions Mean -72.7855 +trainer/Q2 Predictions Std 16.8862 +trainer/Q2 Predictions Max -0.934426 +trainer/Q2 Predictions Min -87.3723 +trainer/Q Targets Mean -72.7763 +trainer/Q Targets Std 17.0301 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.5706 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00627848 +trainer/policy/mean Std 0.722843 +trainer/policy/mean Max 0.999807 +trainer/policy/mean Min -0.99897 +trainer/policy/std Mean 0.410553 +trainer/policy/std Std 0.0214016 +trainer/policy/std Max 0.434426 +trainer/policy/std Min 0.382663 +trainer/Advantage Weights Mean 4.49326 +trainer/Advantage Weights Std 18.2874 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 9.35756e-20 +trainer/Advantage Score Mean -0.428502 +trainer/Advantage Score Std 0.733963 +trainer/Advantage Score Max 1.702 +trainer/Advantage Score Min -4.38155 +trainer/V1 Predictions Mean -72.4838 +trainer/V1 Predictions Std 17.2379 +trainer/V1 Predictions Max 0.931769 +trainer/V1 Predictions Min -87.653 +trainer/VF Loss 0.0943648 +expl/num steps total 991000 +expl/num paths total 1395 +expl/path length Mean 500 +expl/path length Std 316 +expl/path length Max 816 +expl/path length Min 184 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0457827 +expl/Actions Std 0.828851 +expl/Actions Max 2.40211 +expl/Actions Min -2.25629 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 895332 +eval/num paths total 1006 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.100541 +eval/Actions Std 0.678123 +eval/Actions Max 0.999895 +eval/Actions Min -0.999738 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 3.06685e-06 +time/evaluation sampling (s) 2.36407 +time/exploration sampling (s) 2.97925 +time/logging (s) 0.0068208 +time/saving (s) 0.0103875 +time/training (s) 13.1322 +time/epoch (s) 18.4927 +time/total (s) 24381.2 +Epoch -10 +------------------------------ ---------------- +2022-05-16 00:49:11.727519 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -9 finished +------------------------------ ---------------- +epoch -9 +replay_buffer/size 999047 +trainer/num train calls 992000 +trainer/QF1 Loss 0.639539 +trainer/QF2 Loss 0.591308 +trainer/Policy Loss 19.703 +trainer/Q1 Predictions Mean -71.8866 +trainer/Q1 Predictions Std 18.629 +trainer/Q1 Predictions Max -1.93826 +trainer/Q1 Predictions Min -90.8398 +trainer/Q2 Predictions Mean -71.8061 +trainer/Q2 Predictions Std 18.6991 +trainer/Q2 Predictions Max -1.76276 +trainer/Q2 Predictions Min -91.1687 +trainer/Q Targets Mean -71.6886 +trainer/Q Targets Std 18.692 +trainer/Q Targets Max 0 +trainer/Q Targets Min -90.9249 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00968561 +trainer/policy/mean Std 0.724939 +trainer/policy/mean Max 0.999604 +trainer/policy/mean Min -0.999699 +trainer/policy/std Mean 0.408181 +trainer/policy/std Std 0.0202709 +trainer/policy/std Max 0.430605 +trainer/policy/std Min 0.38003 +trainer/Advantage Weights Mean 3.24424 +trainer/Advantage Weights Std 14.5063 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.56916e-16 +trainer/Advantage Score Mean -0.486157 +trainer/Advantage Score Std 0.670422 +trainer/Advantage Score Max 1.28899 +trainer/Advantage Score Min -3.63908 +trainer/V1 Predictions Mean -71.4506 +trainer/V1 Predictions Std 18.8749 +trainer/V1 Predictions Max -1.976 +trainer/V1 Predictions Min -90.3743 +trainer/VF Loss 0.0810753 +expl/num steps total 992000 +expl/num paths total 1397 +expl/path length Mean 500 +expl/path length Std 468 +expl/path length Max 968 +expl/path length Min 32 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0541329 +expl/Actions Std 0.823627 +expl/Actions Max 2.27837 +expl/Actions Min -2.40154 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 895931 +eval/num paths total 1007 +eval/path length Mean 599 +eval/path length Std 0 +eval/path length Max 599 +eval/path length Min 599 +eval/Rewards Mean 0.00166945 +eval/Rewards Std 0.0408248 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0437515 +eval/Actions Std 0.725256 +eval/Actions Max 0.99983 +eval/Actions Min -0.999805 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.00119e-06 +time/evaluation sampling (s) 2.42468 +time/exploration sampling (s) 2.92379 +time/logging (s) 0.00644414 +time/saving (s) 0.0119657 +time/training (s) 12.6452 +time/epoch (s) 18.0121 +time/total (s) 24399.3 +Epoch -9 +------------------------------ ---------------- +2022-05-16 00:49:29.235866 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -8 finished +------------------------------ ---------------- +epoch -8 +replay_buffer/size 999047 +trainer/num train calls 993000 +trainer/QF1 Loss 0.548947 +trainer/QF2 Loss 0.576951 +trainer/Policy Loss 31.7397 +trainer/Q1 Predictions Mean -73.5277 +trainer/Q1 Predictions Std 14.9021 +trainer/Q1 Predictions Max -1.21739 +trainer/Q1 Predictions Min -86.8515 +trainer/Q2 Predictions Mean -73.5789 +trainer/Q2 Predictions Std 14.928 +trainer/Q2 Predictions Max -0.908463 +trainer/Q2 Predictions Min -86.8957 +trainer/Q Targets Mean -73.2976 +trainer/Q Targets Std 14.8756 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.4561 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.0235525 +trainer/policy/mean Std 0.72317 +trainer/policy/mean Max 0.999232 +trainer/policy/mean Min -0.99856 +trainer/policy/std Mean 0.408955 +trainer/policy/std Std 0.0196604 +trainer/policy/std Max 0.431284 +trainer/policy/std Min 0.381927 +trainer/Advantage Weights Mean 6.96978 +trainer/Advantage Weights Std 23.6909 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 8.09153e-20 +trainer/Advantage Score Mean -0.418235 +trainer/Advantage Score Std 0.684219 +trainer/Advantage Score Max 1.64391 +trainer/Advantage Score Min -4.39609 +trainer/V1 Predictions Mean -73.0307 +trainer/V1 Predictions Std 15.0139 +trainer/V1 Predictions Max -1.17492 +trainer/V1 Predictions Min -86.2092 +trainer/VF Loss 0.101283 +expl/num steps total 993000 +expl/num paths total 1399 +expl/path length Mean 500 +expl/path length Std 285 +expl/path length Max 785 +expl/path length Min 215 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0371841 +expl/Actions Std 0.849445 +expl/Actions Max 2.38353 +expl/Actions Min -2.65679 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 896562 +eval/num paths total 1008 +eval/path length Mean 631 +eval/path length Std 0 +eval/path length Max 631 +eval/path length Min 631 +eval/Rewards Mean 0.00158479 +eval/Rewards Std 0.0397778 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0381291 +eval/Actions Std 0.741192 +eval/Actions Max 0.999757 +eval/Actions Min -0.999845 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.68314e-06 +time/evaluation sampling (s) 2.31294 +time/exploration sampling (s) 2.52538 +time/logging (s) 0.00631183 +time/saving (s) 0.0122449 +time/training (s) 12.6363 +time/epoch (s) 17.4932 +time/total (s) 24416.8 +Epoch -8 +------------------------------ ---------------- +2022-05-16 00:49:46.540410 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -7 finished +------------------------------ ---------------- +epoch -7 +replay_buffer/size 999047 +trainer/num train calls 994000 +trainer/QF1 Loss 0.730251 +trainer/QF2 Loss 0.669362 +trainer/Policy Loss 29.2995 +trainer/Q1 Predictions Mean -71.2739 +trainer/Q1 Predictions Std 18.4857 +trainer/Q1 Predictions Max 0.0607489 +trainer/Q1 Predictions Min -86.3221 +trainer/Q2 Predictions Mean -71.2893 +trainer/Q2 Predictions Std 18.4259 +trainer/Q2 Predictions Max -0.532896 +trainer/Q2 Predictions Min -86.4428 +trainer/Q Targets Mean -71.5913 +trainer/Q Targets Std 18.5984 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.6706 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00105294 +trainer/policy/mean Std 0.730512 +trainer/policy/mean Max 0.999553 +trainer/policy/mean Min -0.999204 +trainer/policy/std Mean 0.409096 +trainer/policy/std Std 0.0187115 +trainer/policy/std Max 0.429568 +trainer/policy/std Min 0.381774 +trainer/Advantage Weights Mean 7.72093 +trainer/Advantage Weights Std 24.2041 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.22397e-21 +trainer/Advantage Score Mean -0.308821 +trainer/Advantage Score Std 0.656951 +trainer/Advantage Score Max 1.51677 +trainer/Advantage Score Min -4.65259 +trainer/V1 Predictions Mean -71.332 +trainer/V1 Predictions Std 18.6619 +trainer/V1 Predictions Max 1.39321 +trainer/V1 Predictions Min -86.8437 +trainer/VF Loss 0.0958665 +expl/num steps total 994000 +expl/num paths total 1401 +expl/path length Mean 500 +expl/path length Std 327 +expl/path length Max 827 +expl/path length Min 173 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0519388 +expl/Actions Std 0.81313 +expl/Actions Max 2.25395 +expl/Actions Min -2.33765 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 897415 +eval/num paths total 1009 +eval/path length Mean 853 +eval/path length Std 0 +eval/path length Max 853 +eval/path length Min 853 +eval/Rewards Mean 0.00117233 +eval/Rewards Std 0.0342193 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0364846 +eval/Actions Std 0.74324 +eval/Actions Max 0.999814 +eval/Actions Min -0.999847 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.07197e-06 +time/evaluation sampling (s) 2.31361 +time/exploration sampling (s) 2.55031 +time/logging (s) 0.00768081 +time/saving (s) 0.0131108 +time/training (s) 12.4059 +time/epoch (s) 17.2906 +time/total (s) 24434.1 +Epoch -7 +------------------------------ ---------------- +2022-05-16 00:50:03.339398 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -6 finished +------------------------------ ---------------- +epoch -6 +replay_buffer/size 999047 +trainer/num train calls 995000 +trainer/QF1 Loss 0.626113 +trainer/QF2 Loss 0.577862 +trainer/Policy Loss 12.6269 +trainer/Q1 Predictions Mean -71.2099 +trainer/Q1 Predictions Std 20.0997 +trainer/Q1 Predictions Max -0.123744 +trainer/Q1 Predictions Min -86.9883 +trainer/Q2 Predictions Mean -71.1888 +trainer/Q2 Predictions Std 20.1214 +trainer/Q2 Predictions Max -0.531095 +trainer/Q2 Predictions Min -87.1315 +trainer/Q Targets Mean -71.0825 +trainer/Q Targets Std 20.1908 +trainer/Q Targets Max 0 +trainer/Q Targets Min -86.985 +trainer/rewards Mean -0.984375 +trainer/rewards Std 0.12402 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.015625 +trainer/terminals Std 0.12402 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.000775875 +trainer/policy/mean Std 0.737295 +trainer/policy/mean Max 0.999645 +trainer/policy/mean Min -0.99937 +trainer/policy/std Mean 0.408872 +trainer/policy/std Std 0.0211262 +trainer/policy/std Max 0.431146 +trainer/policy/std Min 0.381094 +trainer/Advantage Weights Mean 4.82509 +trainer/Advantage Weights Std 19.3952 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.88515e-16 +trainer/Advantage Score Mean -0.380503 +trainer/Advantage Score Std 0.606732 +trainer/Advantage Score Max 1.39897 +trainer/Advantage Score Min -3.57818 +trainer/V1 Predictions Mean -70.8343 +trainer/V1 Predictions Std 20.1921 +trainer/V1 Predictions Max 0.432676 +trainer/V1 Predictions Min -86.6173 +trainer/VF Loss 0.072895 +expl/num steps total 995000 +expl/num paths total 1402 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0331966 +expl/Actions Std 0.831205 +expl/Actions Max 2.21866 +expl/Actions Min -2.32525 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 898415 +eval/num paths total 1010 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean -0.144123 +eval/Actions Std 0.77219 +eval/Actions Max 0.9994 +eval/Actions Min -0.999671 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.88896e-06 +time/evaluation sampling (s) 2.36347 +time/exploration sampling (s) 2.51721 +time/logging (s) 0.00886624 +time/saving (s) 0.0141072 +time/training (s) 11.8805 +time/epoch (s) 16.7841 +time/total (s) 24450.8 +Epoch -6 +------------------------------ ---------------- +2022-05-16 00:50:20.665746 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -5 finished +------------------------------ ---------------- +epoch -5 +replay_buffer/size 999047 +trainer/num train calls 996000 +trainer/QF1 Loss 3.83156 +trainer/QF2 Loss 3.53688 +trainer/Policy Loss 48.6126 +trainer/Q1 Predictions Mean -69.9612 +trainer/Q1 Predictions Std 18.6373 +trainer/Q1 Predictions Max -2.49099 +trainer/Q1 Predictions Min -87.0554 +trainer/Q2 Predictions Mean -69.971 +trainer/Q2 Predictions Std 18.7207 +trainer/Q2 Predictions Max -2.64634 +trainer/Q2 Predictions Min -86.8481 +trainer/Q Targets Mean -70.2409 +trainer/Q Targets Std 18.7849 +trainer/Q Targets Max 0 +trainer/Q Targets Min -87.3905 +trainer/rewards Mean -0.996094 +trainer/rewards Std 0.0623778 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.00390625 +trainer/terminals Std 0.0623778 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.025641 +trainer/policy/mean Std 0.73349 +trainer/policy/mean Max 0.998521 +trainer/policy/mean Min -0.99969 +trainer/policy/std Mean 0.408059 +trainer/policy/std Std 0.0212564 +trainer/policy/std Max 0.430443 +trainer/policy/std Min 0.378862 +trainer/Advantage Weights Mean 9.26376 +trainer/Advantage Weights Std 23.0872 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.37862e-20 +trainer/Advantage Score Mean -0.225227 +trainer/Advantage Score Std 0.678716 +trainer/Advantage Score Max 2.03543 +trainer/Advantage Score Min -4.43693 +trainer/V1 Predictions Mean -70.051 +trainer/V1 Predictions Std 18.9048 +trainer/V1 Predictions Max -1.93673 +trainer/V1 Predictions Min -87.4416 +trainer/VF Loss 0.0963069 +expl/num steps total 996000 +expl/num paths total 1403 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.010624 +expl/Actions Std 0.877419 +expl/Actions Max 2.29284 +expl/Actions Min -2.52344 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 898963 +eval/num paths total 1011 +eval/path length Mean 548 +eval/path length Std 0 +eval/path length Max 548 +eval/path length Min 548 +eval/Rewards Mean 0.00182482 +eval/Rewards Std 0.0426789 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0271488 +eval/Actions Std 0.746665 +eval/Actions Max 0.999835 +eval/Actions Min -0.999622 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 2.96999e-06 +time/evaluation sampling (s) 2.24465 +time/exploration sampling (s) 2.50865 +time/logging (s) 0.00551535 +time/saving (s) 0.00954964 +time/training (s) 12.5409 +time/epoch (s) 17.3092 +time/total (s) 24468.2 +Epoch -5 +------------------------------ ---------------- +2022-05-16 00:50:37.541769 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -4 finished +------------------------------ ---------------- +epoch -4 +replay_buffer/size 999047 +trainer/num train calls 997000 +trainer/QF1 Loss 0.690819 +trainer/QF2 Loss 0.683701 +trainer/Policy Loss 8.8665 +trainer/Q1 Predictions Mean -71.6606 +trainer/Q1 Predictions Std 16.9831 +trainer/Q1 Predictions Max -3.44699 +trainer/Q1 Predictions Min -87.6074 +trainer/Q2 Predictions Mean -71.6612 +trainer/Q2 Predictions Std 16.958 +trainer/Q2 Predictions Max -2.99929 +trainer/Q2 Predictions Min -87.6217 +trainer/Q Targets Mean -71.7017 +trainer/Q Targets Std 16.9039 +trainer/Q Targets Max -2.63486 +trainer/Q Targets Min -87.1671 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00465146 +trainer/policy/mean Std 0.744748 +trainer/policy/mean Max 0.999433 +trainer/policy/mean Min -0.999054 +trainer/policy/std Mean 0.409324 +trainer/policy/std Std 0.0205735 +trainer/policy/std Max 0.433903 +trainer/policy/std Min 0.381374 +trainer/Advantage Weights Mean 3.58743 +trainer/Advantage Weights Std 17.1523 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 2.33526e-17 +trainer/Advantage Score Mean -0.559693 +trainer/Advantage Score Std 0.665146 +trainer/Advantage Score Max 2.29195 +trainer/Advantage Score Min -3.82958 +trainer/V1 Predictions Mean -71.4365 +trainer/V1 Predictions Std 16.9099 +trainer/V1 Predictions Max -3.63063 +trainer/V1 Predictions Min -87.1351 +trainer/VF Loss 0.112069 +expl/num steps total 997000 +expl/num paths total 1405 +expl/path length Mean 500 +expl/path length Std 146 +expl/path length Max 646 +expl/path length Min 354 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0307636 +expl/Actions Std 0.831782 +expl/Actions Max 2.78003 +expl/Actions Min -2.21947 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 899522 +eval/num paths total 1012 +eval/path length Mean 559 +eval/path length Std 0 +eval/path length Max 559 +eval/path length Min 559 +eval/Rewards Mean 0.00178891 +eval/Rewards Std 0.0422576 +eval/Rewards Max 1 +eval/Rewards Min 0 +eval/Returns Mean 1 +eval/Returns Std 0 +eval/Returns Max 1 +eval/Returns Min 1 +eval/Actions Mean 0.0412698 +eval/Actions Std 0.736746 +eval/Actions Max 0.999972 +eval/Actions Min -0.999729 +eval/Num Paths 1 +eval/Average Returns 1 +time/data storing (s) 3.1474e-06 +time/evaluation sampling (s) 2.2693 +time/exploration sampling (s) 2.52541 +time/logging (s) 0.00564243 +time/saving (s) 0.0118461 +time/training (s) 12.0522 +time/epoch (s) 16.8644 +time/total (s) 24485 +Epoch -4 +------------------------------ ---------------- +2022-05-16 00:50:54.256887 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -3 finished +------------------------------ ---------------- +epoch -3 +replay_buffer/size 999047 +trainer/num train calls 998000 +trainer/QF1 Loss 0.761801 +trainer/QF2 Loss 0.696035 +trainer/Policy Loss 13.6153 +trainer/Q1 Predictions Mean -72.5465 +trainer/Q1 Predictions Std 18.589 +trainer/Q1 Predictions Max -1.86041 +trainer/Q1 Predictions Min -88.77 +trainer/Q2 Predictions Mean -72.4879 +trainer/Q2 Predictions Std 18.5707 +trainer/Q2 Predictions Max -2.03685 +trainer/Q2 Predictions Min -88.5727 +trainer/Q Targets Mean -72.2347 +trainer/Q Targets Std 18.3729 +trainer/Q Targets Max -0.638154 +trainer/Q Targets Min -88.1577 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.0127502 +trainer/policy/mean Std 0.728129 +trainer/policy/mean Max 0.999157 +trainer/policy/mean Min -0.999576 +trainer/policy/std Mean 0.408805 +trainer/policy/std Std 0.020521 +trainer/policy/std Max 0.432045 +trainer/policy/std Min 0.378654 +trainer/Advantage Weights Mean 3.28905 +trainer/Advantage Weights Std 14.4289 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 6.19397e-18 +trainer/Advantage Score Mean -0.478361 +trainer/Advantage Score Std 0.713449 +trainer/Advantage Score Max 2.42893 +trainer/Advantage Score Min -3.9623 +trainer/V1 Predictions Mean -71.9682 +trainer/V1 Predictions Std 18.6117 +trainer/V1 Predictions Max -0.633507 +trainer/V1 Predictions Min -88.3099 +trainer/VF Loss 0.0995146 +expl/num steps total 998000 +expl/num paths total 1407 +expl/path length Mean 500 +expl/path length Std 460 +expl/path length Max 960 +expl/path length Min 40 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0166586 +expl/Actions Std 0.818251 +expl/Actions Max 2.09073 +expl/Actions Min -2.2744 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 900522 +eval/num paths total 1013 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0155076 +eval/Actions Std 0.721286 +eval/Actions Max 0.999499 +eval/Actions Min -0.999198 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 1.00401e-05 +time/evaluation sampling (s) 2.3023 +time/exploration sampling (s) 2.55913 +time/logging (s) 0.00783001 +time/saving (s) 0.0129604 +time/training (s) 11.8202 +time/epoch (s) 16.7024 +time/total (s) 24501.7 +Epoch -3 +------------------------------ ---------------- +2022-05-16 00:51:11.279463 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -2 finished +------------------------------ ---------------- +epoch -2 +replay_buffer/size 999047 +trainer/num train calls 999000 +trainer/QF1 Loss 1.05489 +trainer/QF2 Loss 1.25227 +trainer/Policy Loss 11.3577 +trainer/Q1 Predictions Mean -70.4289 +trainer/Q1 Predictions Std 18.6154 +trainer/Q1 Predictions Max -2.28044 +trainer/Q1 Predictions Min -87.0573 +trainer/Q2 Predictions Mean -70.5716 +trainer/Q2 Predictions Std 18.5878 +trainer/Q2 Predictions Max -3.7037 +trainer/Q2 Predictions Min -87.6685 +trainer/Q Targets Mean -69.9597 +trainer/Q Targets Std 18.6374 +trainer/Q Targets Max -4.01168 +trainer/Q Targets Min -86.5177 +trainer/rewards Mean -1 +trainer/rewards Std 0 +trainer/rewards Max -1 +trainer/rewards Min -1 +trainer/terminals Mean 0 +trainer/terminals Std 0 +trainer/terminals Max 0 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean 0.00553912 +trainer/policy/mean Std 0.73149 +trainer/policy/mean Max 0.999305 +trainer/policy/mean Min -0.999468 +trainer/policy/std Mean 0.409085 +trainer/policy/std Std 0.0202965 +trainer/policy/std Max 0.43068 +trainer/policy/std Min 0.379035 +trainer/Advantage Weights Mean 2.77268 +trainer/Advantage Weights Std 14.271 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 1.77826e-23 +trainer/Advantage Score Mean -0.60221 +trainer/Advantage Score Std 0.694997 +trainer/Advantage Score Max 1.31332 +trainer/Advantage Score Min -5.23838 +trainer/V1 Predictions Mean -69.6799 +trainer/V1 Predictions Std 18.7313 +trainer/V1 Predictions Max -2.29583 +trainer/V1 Predictions Min -86.5994 +trainer/VF Loss 0.10155 +expl/num steps total 999000 +expl/num paths total 1409 +expl/path length Mean 500 +expl/path length Std 395 +expl/path length Max 895 +expl/path length Min 105 +expl/Rewards Mean 0.001 +expl/Rewards Std 0.031607 +expl/Rewards Max 1 +expl/Rewards Min 0 +expl/Returns Mean 0.5 +expl/Returns Std 0.5 +expl/Returns Max 1 +expl/Returns Min 0 +expl/Actions Mean 0.0269392 +expl/Actions Std 0.833947 +expl/Actions Max 2.16291 +expl/Actions Min -2.32916 +expl/Num Paths 2 +expl/Average Returns 0.5 +eval/num steps total 901522 +eval/num paths total 1014 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.0102906 +eval/Actions Std 0.74287 +eval/Actions Max 0.999849 +eval/Actions Min -0.999945 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 4.17791e-06 +time/evaluation sampling (s) 2.26099 +time/exploration sampling (s) 2.5083 +time/logging (s) 0.00662602 +time/saving (s) 0.00915839 +time/training (s) 12.2209 +time/epoch (s) 17.006 +time/total (s) 24518.7 +Epoch -2 +------------------------------ ---------------- +2022-05-16 00:51:27.478903 PDT | [05-15-iql-normalized-antmaze-large-diverse-v0_2022_05_15_18_02_30_0000--s-1] Epoch -1 finished +------------------------------ ---------------- +epoch -1 +replay_buffer/size 999047 +trainer/num train calls 1e+06 +trainer/QF1 Loss 0.861696 +trainer/QF2 Loss 0.977262 +trainer/Policy Loss 7.72052 +trainer/Q1 Predictions Mean -69.7217 +trainer/Q1 Predictions Std 20.1013 +trainer/Q1 Predictions Max -1.36843 +trainer/Q1 Predictions Min -89.7776 +trainer/Q2 Predictions Mean -69.812 +trainer/Q2 Predictions Std 19.9916 +trainer/Q2 Predictions Max -0.459061 +trainer/Q2 Predictions Min -89.6377 +trainer/Q Targets Mean -69.3621 +trainer/Q Targets Std 19.9195 +trainer/Q Targets Max 0 +trainer/Q Targets Min -88.9045 +trainer/rewards Mean -0.992188 +trainer/rewards Std 0.0880424 +trainer/rewards Max 0 +trainer/rewards Min -1 +trainer/terminals Mean 0.0078125 +trainer/terminals Std 0.0880424 +trainer/terminals Max 1 +trainer/terminals Min 0 +trainer/replay_buffer_len 999047 +trainer/policy/mean Mean -0.00181453 +trainer/policy/mean Std 0.725252 +trainer/policy/mean Max 0.999531 +trainer/policy/mean Min -0.998917 +trainer/policy/std Mean 0.409526 +trainer/policy/std Std 0.0215467 +trainer/policy/std Max 0.433412 +trainer/policy/std Min 0.376405 +trainer/Advantage Weights Mean 2.23295 +trainer/Advantage Weights Std 13.0038 +trainer/Advantage Weights Max 100 +trainer/Advantage Weights Min 5.73551e-17 +trainer/Advantage Score Mean -0.603531 +trainer/Advantage Score Std 0.593912 +trainer/Advantage Score Max 0.807122 +trainer/Advantage Score Min -3.73973 +trainer/V1 Predictions Mean -69.1033 +trainer/V1 Predictions Std 20.1046 +trainer/V1 Predictions Max -0.155463 +trainer/V1 Predictions Min -88.6623 +trainer/VF Loss 0.0783119 +expl/num steps total 1e+06 +expl/num paths total 1410 +expl/path length Mean 1000 +expl/path length Std 0 +expl/path length Max 1000 +expl/path length Min 1000 +expl/Rewards Mean 0 +expl/Rewards Std 0 +expl/Rewards Max 0 +expl/Rewards Min 0 +expl/Returns Mean 0 +expl/Returns Std 0 +expl/Returns Max 0 +expl/Returns Min 0 +expl/Actions Mean 0.0504045 +expl/Actions Std 0.864594 +expl/Actions Max 2.2877 +expl/Actions Min -2.45579 +expl/Num Paths 1 +expl/Average Returns 0 +eval/num steps total 902522 +eval/num paths total 1015 +eval/path length Mean 1000 +eval/path length Std 0 +eval/path length Max 1000 +eval/path length Min 1000 +eval/Rewards Mean 0 +eval/Rewards Std 0 +eval/Rewards Max 0 +eval/Rewards Min 0 +eval/Returns Mean 0 +eval/Returns Std 0 +eval/Returns Max 0 +eval/Returns Min 0 +eval/Actions Mean 0.00260002 +eval/Actions Std 0.710294 +eval/Actions Max 0.999857 +eval/Actions Min -0.999901 +eval/Num Paths 1 +eval/Average Returns 0 +time/data storing (s) 2.54624e-06 +time/evaluation sampling (s) 2.25645 +time/exploration sampling (s) 2.53835 +time/logging (s) 0.0116011 +time/saving (s) 0.0170155 +time/training (s) 11.3684 +time/epoch (s) 16.1918 +time/total (s) 24534.9 +Epoch -1 +------------------------------ ----------------