Viswes commited on
Commit
6d61ffa
·
1 Parent(s): 4bca39f

Upload PPO LunarLander-v2 trained agent

Browse files
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - LunarLander-v2
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: PPO
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: LunarLander-v2
16
+ type: LunarLander-v2
17
+ metrics:
18
+ - type: mean_reward
19
+ value: 263.84 +/- 24.59
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **PPO** Agent playing **LunarLander-v2**
25
+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
+
28
+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```python
33
+ from stable_baselines3 import ...
34
+ from huggingface_sb3 import load_from_hub
35
+
36
+ ...
37
+ ```
config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7fc94daeadc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc94daeae50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc94daeaee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc94daeaf70>", "_build": "<function ActorCriticPolicy._build at 0x7fc94daee040>", "forward": "<function ActorCriticPolicy.forward at 0x7fc94daee0d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc94daee160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc94daee1f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc94daee280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc94daee310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc94daee3a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc94daee430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc94dae9dc0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678458985260945297, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
ppo-lunarlander-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cc56c782f0ae51c025ae3da156d70f8726a350bebf0ab5c1b195f2f5710ad48a
3
+ size 147397
ppo-lunarlander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
ppo-lunarlander-v2/data ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
4
+ ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
5
+ "__module__": "stable_baselines3.common.policies",
6
+ "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fc94daeadc0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc94daeae50>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc94daeaee0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc94daeaf70>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fc94daee040>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fc94daee0d0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc94daee160>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc94daee1f0>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fc94daee280>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc94daee310>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc94daee3a0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc94daee430>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7fc94dae9dc0>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "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",
27
+ "dtype": "float32",
28
+ "_shape": [
29
+ 8
30
+ ],
31
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
32
+ "high": "[inf inf inf inf inf inf inf inf]",
33
+ "bounded_below": "[False False False False False False False False]",
34
+ "bounded_above": "[False False False False False False False False]",
35
+ "_np_random": null
36
+ },
37
+ "action_space": {
38
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
39
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
40
+ "n": 4,
41
+ "_shape": [],
42
+ "dtype": "int64",
43
+ "_np_random": null
44
+ },
45
+ "n_envs": 16,
46
+ "num_timesteps": 1015808,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1678458985260945297,
52
+ "learning_rate": 0.0003,
53
+ "tensorboard_log": null,
54
+ "lr_schedule": {
55
+ ":type:": "<class 'function'>",
56
+ ":serialized:": "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"
57
+ },
58
+ "_last_obs": {
59
+ ":type:": "<class 'numpy.ndarray'>",
60
+ ":serialized:": "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"
61
+ },
62
+ "_last_episode_starts": {
63
+ ":type:": "<class 'numpy.ndarray'>",
64
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
65
+ },
66
+ "_last_original_obs": null,
67
+ "_episode_num": 0,
68
+ "use_sde": false,
69
+ "sde_sample_freq": -1,
70
+ "_current_progress_remaining": -0.015808000000000044,
71
+ "ep_info_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "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"
74
+ },
75
+ "ep_success_buffer": {
76
+ ":type:": "<class 'collections.deque'>",
77
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
78
+ },
79
+ "_n_updates": 248,
80
+ "n_steps": 1024,
81
+ "gamma": 0.999,
82
+ "gae_lambda": 0.98,
83
+ "ent_coef": 0.01,
84
+ "vf_coef": 0.5,
85
+ "max_grad_norm": 0.5,
86
+ "batch_size": 64,
87
+ "n_epochs": 4,
88
+ "clip_range": {
89
+ ":type:": "<class 'function'>",
90
+ ":serialized:": "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"
91
+ },
92
+ "clip_range_vf": null,
93
+ "normalize_advantage": true,
94
+ "target_kl": null
95
+ }
ppo-lunarlander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7bba692a845c727810ca1f85de9ce40c2d7e7cd224d358c46d8790cf4f1ee0be
3
+ size 87929
ppo-lunarlander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9204d58fc2de3627f6a6056cc60678d26cbba525473812663916b5940ed9598c
3
+ size 43393
ppo-lunarlander-v2/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
ppo-lunarlander-v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.9.16
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.22.4
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (223 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 263.83946324823137, "std_reward": 24.586875885855502, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-10T15:19:07.297308"}