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run-output/plots/io_log.jsonl
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run-output/plots/training_log.csv
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round,avg_episode_reward,max_episode_reward,min_episode_reward,avg_grader,max_grader,n_training_samples,train_loss
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1,3.138,3.62,2.504,0.3196,0.4857,46,2.4383
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round,avg_episode_reward,max_episode_reward,min_episode_reward,avg_grader,max_grader,n_training_samples,train_loss
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run-output/plots/training_summary.json
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"rounds": 1,
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"episodes_per_round": 6,
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"before": {
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"monthly_engage": 0.
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"monthly_strategic": 0.
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"monthly_competitive": 0.
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},
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"after": {
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"monthly_engage": 0.
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"monthly_strategic": 0.
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"monthly_competitive": 0.
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},
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"smart_heuristic": {
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"monthly_engage": 0.7352,
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"monthly_competitive": 0.9066
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},
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"improvement": {
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"monthly_engage": 0.
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"monthly_strategic": -0.
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"monthly_competitive":
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},
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"training_log": {
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"round": [
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"
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"
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],
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"min_episode_reward": [
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2.504
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],
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"avg_grader": [
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0.3196
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],
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"max_grader": [
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0.4857
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],
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"n_training_samples": [
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46
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],
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"train_loss": [
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2.4383
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]
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}
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}
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"rounds": 1,
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"episodes_per_round": 6,
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"before": {
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"monthly_engage": 0.1026,
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"monthly_strategic": 0.2396,
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"monthly_competitive": 0.2967
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},
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"after": {
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"monthly_engage": 0.144,
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"monthly_strategic": 0.2124,
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"monthly_competitive": 0.3122
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},
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"smart_heuristic": {
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"monthly_engage": 0.7352,
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"monthly_competitive": 0.9066
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},
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"improvement": {
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"monthly_engage": 0.04139999999999999,
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"monthly_strategic": -0.027200000000000002,
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"monthly_competitive": 0.015499999999999958
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},
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"training_log": {
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"round": [],
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"avg_episode_reward": [],
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"max_episode_reward": [],
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"min_episode_reward": [],
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"avg_grader": [],
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"max_grader": [],
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"n_training_samples": [],
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"train_loss": []
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}
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}
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run-output/training/train_grpo.executed.ipynb
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