{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":53634,"offset":53624,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u0412\u043e\u0442 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u043e\u0432 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0439 \u0434\u043b\u044f \u041a\u043b\u0438\u0435\u043d\u0442\u0441\u043a\u043e\u0433\u043e \u0434\u043d\u044f, \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u043d\u043e\u0433\u043e \u0432\u0430\u0448\u0438\u043c \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0430\u043c:\n\n1. **\u00ab\u0414\u0435\u043d\u044c \u0423\u0441\u043f\u0435\u0445\u0430 \u0441 \u041a\u0440\u0435\u043c\u043e\u043c \u0438 \u042d\u043d\u0435\u0440\u0433\u0438\u0435\u0439\u00bb**\n2. **\u00ab\u042d\u043d\u0435\u0440\u0433\u0438\u044f \u0423\u0434\u043e\u0432\u043e\u043b\u044c\u0441\u0442\u0432\u0438\u044f: \u041a\u0440\u0435\u043c\u044b \u0438 \u0421\u043f\u0440\u0435\u0438 \u0434\u043b\u044f \u0412\u0430\u0448\u0435\u0433\u043e \u0423\u0442\u0440\u0430\u00bb**\n3. **\u00ab\u0412\u043e\u043b\u0448\u0435\u0431\u043d\u044b\u0439 \u0423\u0433\u043e\u043b\u043e\u043a \u041a\u0440\u0430\u0441\u043e\u0442\u044b: \u041a\u0440\u0435\u043c\u044b \u0438 \u0410\u0440\u043e\u043c\u0430\u0442\u044b\u00bb**\n4. **\u00ab\u041f\u0440\u0430\u0437\u0434\u043d\u0438\u043a \u042d\u043d\u0435\u0440\u0433\u0438\u0438 \u0438 \u041d\u0435\u0436\u043d\u043e\u0441\u0442\u0438\u00bb**\n5. **\u00ab\u0421\u0438\u044f\u043d\u0438\u0435 \u041b\u0435\u0442\u0430: \u041a\u0440\u0435\u043c\u044b \u0438 \u0421\u043f\u0440\u0435\u0438 \u0434\u043b\u044f \u0412\u0430\u0448\u0435\u0439 \u041a\u0440\u0430\u0441\u043e\u0442\u044b\u00bb**\n6. **\u00ab\u0414\u0435\u043d\u044c \u0420\u043e\u0441\u043a\u043e\u0448\u0438: \u041d\u0430\u0441\u043b\u0430\u0436\u0434\u0430\u0439\u0442\u0435\u0441\u044c \u041d\u0435\u0436\u043d\u043e\u0441\u0442\u044c\u044e \u0438 \u042d\u043d\u0435\u0440\u0433\u0438\u0435\u0439\u00bb**\n7. **\u00ab\u0410\u0440\u043e\u043c\u0430\u0442\u044b \u0423\u0441\u043f\u0435\u0445\u0430: \u041f\u043e\u0435\u0437\u0434\u043a\u0430 \u0432 \u041c\u0438\u0440 \u041a\u0440\u0430\u0441\u043e\u0442\u044b\u00bb**\n8. **\u00ab\u042d\u043d\u0435\u0440\u0433\u0438\u044f \u041d\u0435\u0436\u043d\u043e\u0441\u0442\u0438: \u0423\u043d\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u0435 \u041f\u0440\u043e\u0434\u0443\u043a\u0442\u044b \u0434\u043b\u044f \u041a\u0430\u0436\u0434\u043e\u0433\u043e\u00bb**\n9. **\u00ab\u0410\u043d\u0442\u0438\u0441\u0442\u0440\u0435\u0441\u0441 \u0438 \u041d\u0435\u0436\u043d\u043e\u0441\u0442\u044c: \u041e\u0442\u043a\u0440\u043e\u0439\u0442\u0435 \u0434\u043b\u044f \u0421\u0435\u0431\u044f \u041d\u0430\u0448\u0438 \u041f\u0440\u043e\u0434\u0443\u043a\u0442\u044b\u00bb**\n10. **\u00ab\u0414\u0435\u043d\u044c \u041a\u0440\u0430\u0441\u043e\u0442\u044b \u0438 \u0423\u0441\u0442\u043e\u0439\u0447\u0438\u0432\u043e\u0441\u0442\u0438: \u0412\u0441\u0442\u0440\u0435\u0447\u0430 \u0441 \u041d\u0430\u0448\u0438\u043c\u0438 \u041d\u043e\u0432\u0438\u043d\u043a\u0430\u043c\u0438\u00bb**\n\n\u0412\u044b\u0431\u0438\u0440\u0430\u0439\u0442\u0435 \u0442\u043e, \u0447\u0442\u043e \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0434\u0443\u0445\u0443 \u0432\u0430\u0448\u0438\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432 \u0438 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u0430\u0443\u0434\u0438\u0442\u043e\u0440\u0438\u0438!","behavior":"degenerate_repetition","conversation_hash":"f75106ab430541af81d793a429fd2957","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":843,"n_turns_in_conversation":7,"n_words":113,"source_row_offset":53624,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:41:17 GMT","turn_id":"dc2b527b1bb629a04dc03423","turn_index":5,"user_text":"\u041f\u0440\u0438\u0434\u0443\u043c\u0430\u0442\u044c \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435 \u0434\u043b\u044f \u041a\u043b\u0438\u0435\u043d\u0442\u0441\u043a\u043e\u0433\u043e \u0434\u043d\u044f, \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u043d\u043e\u0433\u043e \u044d\u0442\u0438\u043c \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0430\u043c:\n\n\u041a\u0440\u0435\u043c-\u043c\u0443\u0441\u0441\n\u0421\u043f\u0440\u0435\u0439-\u044d\u043d\u0435\u0440\u0433\u0435\u0442\u0438\u043a \u0441 \u0430\u0440\u0433\u0438\u043d\u0438\u043d\u043e\u043c\n\u041a\u0440\u0435\u043c-\u0433\u0435\u043b\u044c \u0434\u043b\u044f \u0434\u0443\u0448\u0430 \u041c\u0430\u0433\u043d\u043e\u043b\u0438\u044f\n"},{"assistant_initiates":false,"asst_text":"\u0412\u043e\u0442 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0438\u0434\u0435\u0439 \u0434\u043b\u044f \u043a\u043e\u0440\u043e\u0442\u043a\u043e\u0433\u043e \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u044f \u041a\u043b\u0438\u0435\u043d\u0442\u0441\u043a\u043e\u0433\u043e \u0434\u043d\u044f:\n\n1. **\u00ab\u042d\u043d\u0435\u0440\u0433\u0438\u044f \u041a\u0440\u0430\u0441\u043e\u0442\u044b\u00bb**\n2. **\u00ab\u0414\u0435\u043d\u044c \u041c\u044f\u0433\u043a\u043e\u0441\u0442\u0438 \u0438 \u042d\u043d\u0435\u0440\u0433\u0438\u0438\u00bb**\n3. **\u00ab\u0410\u0440\u043e\u043c\u0430\u0442\u044b \u0438 \u042d\u043d\u0435\u0440\u0433\u0438\u044f\u00bb**\n4. **\u00ab\u0412\u0434\u043e\u0445\u043d\u043e\u0432\u0435\u043d\u0438\u0435 \u0438 \u0423\u0445\u043e\u0434\u00bb**\n5. **\u00ab\u0421\u0438\u043b\u0430 \u041d\u0435\u0436\u043d\u043e\u0441\u0442\u0438\u00bb**\n6. **\u00ab\u041c\u0430\u0433\u0438\u044f \u0423\u0445\u043e\u0434\u0430\u00bb**\n7. **\u00ab\u0414\u0435\u043d\u044c \u042d\u043d\u0435\u0440\u0433\u0435\u0442\u0438\u043a\u043e\u0432\u00bb**\n8. **\u00ab\u041a\u0440\u0430\u0441\u043e\u0442\u0430 \u0438 \u0416\u0438\u0432\u043b\u0435\u043d\u0438\u0435\u00bb**\n9. **\u00ab\u041e\u0441\u0432\u0435\u0436\u0430\u044e\u0449\u0438\u0439 \u0423\u0445\u043e\u0434\u00bb**\n10. **\u00ab\u0414\u0435\u043d\u044c \u041d\u0435\u0436\u043d\u043e\u0441\u0442\u0438\u00bb**\n\n\u041d\u0430\u0434\u0435\u044e\u0441\u044c, \u043e\u0434\u0438\u043d \u0438\u0437 \u044d\u0442\u0438\u0445 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u043e\u0432 \u0432\u0430\u043c \u043f\u043e\u0434\u043e\u0439\u0434\u0435\u0442!","behavior":"degenerate_repetition","conversation_hash":"f75106ab430541af81d793a429fd2957","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":512,"n_turns_in_conversation":7,"n_words":67,"source_row_offset":53625,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:41:17 GMT","turn_id":"2f017dc7823246d3ed708ef8","turn_index":6,"user_text":"\u041f\u0440\u0438\u0434\u0443\u043c\u0430\u0442\u044c \u043a\u043e\u0440\u043e\u0442\u043a\u043e\u0435 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435 \u0434\u043b\u044f \u041a\u043b\u0438\u0435\u043d\u0442\u0441\u043a\u043e\u0433\u043e \u0434\u043d\u044f, \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u043d\u043e\u0433\u043e \u044d\u0442\u0438\u043c \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0430\u043c:\n\n\u041a\u0440\u0435\u043c-\u043c\u0443\u0441\u0441\n\u0421\u043f\u0440\u0435\u0439-\u044d\u043d\u0435\u0440\u0433\u0435\u0442\u0438\u043a \u0441 \u0430\u0440\u0433\u0438\u043d\u0438\u043d\u043e\u043c\n\u041a\u0440\u0435\u043c-\u0433\u0435\u043b\u044c \u0434\u043b\u044f \u0434\u0443\u0448\u0430 \u041c\u0430\u0433\u043d\u043e\u043b\u0438\u044f\n"},{"assistant_initiates":false,"asst_text":"\u041f\u0440\u0438\u0432\u0435\u0442! \u041a\u0430\u043a \u044f \u043c\u043e\u0433\u0443 \u043f\u043e\u043c\u043e\u0447\u044c \u0442\u0435\u0431\u0435 \u0441\u0435\u0433\u043e\u0434\u043d\u044f?","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":46,"n_turns_in_conversation":20,"n_words":8,"source_row_offset":53626,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"00d6ce0db1ea4d4141a58003","turn_index":0,"user_text":"\u043f\u0440\u0438\u0432\u0435\u0442"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430, \u0441 \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u0432\u044b \u0441\u0442\u0430\u043b\u043a\u0438\u0432\u0430\u0435\u0442\u0435\u0441\u044c, \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u0435\u0442 \u0438\u0437-\u0437\u0430 \u0442\u043e\u0433\u043e, \u0447\u0442\u043e \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u043c\u044b\u0439 \u0432\u0430\u043c\u0438 `batch_size` \u043d\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f `n_steps * n_envs`. \u0412 \u0434\u0430\u043d\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435, \u0435\u0441\u043b\u0438 `n_envs=1`, \u0442\u043e `n_steps * n_envs` \u0440\u0430\u0432\u043d\u043e `n_steps`, \u0438 \u043f\u043e\u044d\u0442\u043e\u043c\u0443 `batch_size` \u0434\u043e\u043b\u0436\u0435\u043d \u0431\u044b\u0442\u044c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f `n_steps`.\n\n\u0427\u0442\u043e\u0431\u044b \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u044d\u0442\u0443 \u043e\u0448\u0438\u0431\u043a\u0443, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 `n_steps` \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f `batch_size`. \u0412\u043e\u0442, \u043a\u0430\u043a \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0437\u043c\u0435\u043d\u0438\u0442\u044c \u0441\u0432\u043e\u0439 \u043a\u043e\u0434, \u0447\u0442\u043e\u0431\u044b \u0433\u0430\u0440\u0430\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u0447\u0442\u043e `batch_size` \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u043c `n_steps`:\n\n```python\nimport numpy as np\n\ndef objective(trial):\n    n_steps = trial.suggest_int('n_steps', 16, 4096, log=True)\n    \n    # \u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u0432\u0441\u0435 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 n_steps\n    divisors = [i for i in range(1, n_steps + 1) if n_steps % i == 0]\n    batch_size = trial.suggest_categorical('batch_size', divisors)\n    \n    learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n    gamma = trial.suggest_float('gamma', 0.9, 1.0, log=False)\n\n    model = PPO('MlpPolicy', env, n_steps=n_steps, learning_rate=learning_rate,\n                batch_size=batch_size, verbose=0)\n\n    try:\n        model.learn(total_timesteps=30000)\n\n        # \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u044f\n        obs = env.reset()\n        total_reward = 0.0\n        done = False\n\n        while not done:\n            action, _states = model.predict(obs)\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n\n        return float(total_reward)  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0441\u043a\u0430\u043b\u044f\u0440\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\n    except Exception as e:\n        print(f\"An error occurred during training: {e}\")\n        return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\n\nprint(\"Best hyperparameters: \", study.best_params)\n```\n\n\u0412 \u044d\u0442\u043e\u043c \u043a\u043e\u0434\u0435 \u043c\u044b \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0432\u0441\u0435 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 `n_steps` \u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c `suggest_categorical`, \u0447\u0442\u043e\u0431\u044b \u0432\u044b\u0431\u0440\u0430\u0442\u044c `batch_size` \u0438\u0437 \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0445 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439. \u042d\u0442\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u0438\u0442 \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043e\u043a, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0445 \u0441 \u043d\u0435\u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u0435\u043c `batch_size` \u0438 `n_steps`.","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":3647,"n_turns_in_conversation":20,"n_words":390,"source_row_offset":53627,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"704f16dc18c04f28d6bb851d","turn_index":1,"user_text":"\u0435\u0441\u0442\u044c RL-\u0430\u0433\u0435\u043d\u0442, \u0433\u0438\u043f\u0435\u0440\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0438\u0440\u0443\u044e\u0442\u0441\u044f \u0447\u0435\u0440\u0435\u0437 optuna. \u0432\u043e\u0442 \u043a\u043e\u0434:\ndef objective(trial):\n    n_steps = trial.suggest_int('n_steps', 16, 4096, log=True)\n    batch_size = trial.suggest_int('batch_size', 2, 32)  # \u0418\u043b\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 n_steps\n    learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n    gamma = trial.suggest_float('gamma', 0.9, 1.0, log=False)\n    \n\n    model = PPO('MlpPolicy', env, n_steps=n_steps, learning_rate=learning_rate,\n                batch_size=batch_size, verbose=0)\n\n    try:\n        model.learn(total_timesteps=30000)\n\n        # \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u044f\n        obs = env.reset()\n        total_reward = 0.0\n        done = False\n\n        while not done:\n            action, _states = model.predict(obs)\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n\n        return float(total_reward)  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0441\u043a\u0430\u043b\u044f\u0440\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\n    except Exception as e:\n        print(f\"An error occurred during training: {e}\")\n        return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\n\nprint(\"Best hyperparameters: \", study.best_params)\n\n\u043e\u043d \u0432\u044b\u0434\u0430\u0435\u0442 \u043e\u0448\u0438\u0431\u043a\u0443:\nYou have specified a mini-batch size of 8, but because the `RolloutBuffer` is of size `n_steps * n_envs = 266`, after every 33 untruncated mini-batches, there will be a truncated mini-batch of size 2\nWe recommend using a `batch_size` that is a factor of `n_steps * n_envs`.\nInfo: (n_steps=266 and n_envs=1)"},{"assistant_initiates":false,"asst_text":"\u041f\u043e\u0436\u0430\u043b\u0443\u0439\u0441\u0442\u0430! \u0415\u0441\u043b\u0438 \u0443 \u0442\u0435\u0431\u044f \u0432\u043e\u0437\u043d\u0438\u043a\u043d\u0443\u0442 \u0435\u0449\u0435 \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u0438\u043b\u0438 \u043f\u043e\u043d\u0430\u0434\u043e\u0431\u0438\u0442\u0441\u044f \u043f\u043e\u043c\u043e\u0449\u044c, \u043d\u0435 \u0441\u0442\u0435\u0441\u043d\u044f\u0439\u0441\u044f \u043e\u0431\u0440\u0430\u0449\u0430\u0442\u044c\u0441\u044f. \u0423\u0434\u0430\u0447\u0438 \u0441 \u0442\u0432\u043e\u0438\u043c \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u043c!","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":126,"n_turns_in_conversation":20,"n_words":18,"source_row_offset":53628,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"e696bb03f735be5339ca3517","turn_index":2,"user_text":"\u0441\u043f\u0430\u0441\u0438\u0431\u043e"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `ValueError: CategoricalDistribution does not support dynamic value space` \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u0435\u0442, \u043f\u043e\u0442\u043e\u043c\u0443 \u0447\u0442\u043e `suggest_categorical` \u043d\u0435 \u043c\u043e\u0436\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438 \u0438\u0437\u043c\u0435\u043d\u044f\u0435\u043c\u044b\u043c\u0438 \u0441\u043f\u0438\u0441\u043a\u0430\u043c\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0437\u0430\u0432\u0438\u0441\u044f\u0442 \u043e\u0442 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439, \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u0435\u043c\u044b\u0445 \u0432 \u0434\u0440\u0443\u0433\u0438\u0445 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430\u0445 (\u0432 \u0432\u0430\u0448\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435, \u043e\u0442 `n_steps`).\n\n\u0414\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u044d\u0442\u043e\u0439 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u043c\u043e\u0436\u043d\u043e \u0432\u043e\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0439 \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u0435\u0439. \u0412\u043c\u0435\u0441\u0442\u043e \u0442\u043e\u0433\u043e \u0447\u0442\u043e\u0431\u044b \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u0442\u044c `batch_size` \u0438\u0437 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 \u043d\u0435\u043f\u043e\u0441\u0440\u0435\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u0432 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 `objective`, \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u0435 \u0432\u0441\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u0433\u043b\u043e\u0431\u0430\u043b\u044c\u043d\u043e\u0439 \u043e\u0431\u043b\u0430\u0441\u0442\u0438 \u0432\u0438\u0434\u0438\u043c\u043e\u0441\u0442\u0438, \u0430 \u0437\u0430\u0442\u0435\u043c \u043f\u0440\u0438 \u0432\u044b\u0431\u043e\u0440\u0435 `batch_size` \u043f\u0440\u043e\u0441\u0442\u043e \u043e\u0442\u0444\u0438\u043b\u044c\u0442\u0440\u0443\u0439\u0442\u0435 \u043f\u043e\u0434\u0445\u043e\u0434\u044f\u0449\u0438\u0435 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 \u043f\u043e \u0442\u0435\u043a\u0443\u0449\u0435\u043c\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044e `n_steps`. \u0412\u043e\u0442 \u043a\u0430\u043a \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0432\u044b\u0433\u043b\u044f\u0434\u0435\u0442\u044c:\n\n```python\ndef get_divisors(n):\n    \"\"\"\u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 \u0447\u0438\u0441\u043b\u0430 n.\"\"\"\n    return [i for i in range(1, n + 1) if n % i == 0]\n\ndef objective(trial):\n    n_steps = trial.suggest_int('n_steps', 16, 4096, log=True)\n    \n    # \u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 \u0442\u043e\u043b\u044c\u043a\u043e \u043f\u043e\u0441\u043b\u0435 \u0432\u044b\u0431\u043e\u0440\u0430 n_steps\n    divisors = get_divisors(n_steps)\n    batch_size = trial.suggest_categorical('batch_size', divisors)\n    \n    learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n    gamma = trial.suggest_float('gamma', 0.9, 1.0, log=False)\n\n    model = PPO('MlpPolicy', env, n_steps=n_steps, learning_rate=learning_rate,\n                batch_size=batch_size, verbose=0)\n\n    try:\n        model.learn(total_timesteps=30000)\n\n        # \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u044f\n        obs = env.reset()\n        total_reward = 0.0\n        done = False\n\n        while not done:\n            action, _states = model.predict(obs)\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n\n        return float(total_reward)  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0441\u043a\u0430\u043b\u044f\u0440\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\n    except Exception as e:\n        print(f\"An error occurred during training: {e}\")\n        return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\n\nprint(\"Best hyperparameters: \", study.best_params)\n```\n\n\u0412 \u0434\u0430\u043d\u043d\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0430 \u0432\u0441\u043f\u043e\u043c\u043e\u0433\u0430\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044f `get_divisors`, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 \u0447\u0438\u0441\u043b\u0430 `n_steps`. `batch_size` \u0442\u0435\u043f\u0435\u0440\u044c \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u0442\u0441\u044f \u0438\u0437 \u044d\u0442\u043e\u0439 \u043f\u0440\u0435\u0434\u0432\u0430\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439.\n\n\u042d\u0442\u0430 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u043f\u043e\u043c\u043e\u0447\u044c \u0432\u0430\u043c \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043e\u043a, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0445 \u0441 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0435\u0439 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 `Optuna`. \u0415\u0441\u043b\u0438 \u0443 \u0432\u0430\u0441 \u0432\u043e\u0437\u043d\u0438\u043a\u043d\u0443\u0442 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0432\u043e\u043f\u0440\u043e\u0441\u044b, \u043d\u0435 \u0441\u0442\u0435\u0441\u043d\u044f\u0439\u0442\u0435\u0441\u044c \u0441\u043f\u0440\u0430\u0448\u0438\u0432\u0430\u0442\u044c!","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":2581,"n_turns_in_conversation":20,"n_words":278,"source_row_offset":53629,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"4fa88f9e09ae5a812f3fd158","turn_index":3,"user_text":"\u0442\u0435\u043f\u0435\u0440\u044c \u044d\u0442\u0430 \u043e\u0448\u0438\u0431\u043a\u0430:\nValueError: CategoricalDistribution does not support dynamic value space.\n[W 2024-11-26 10:54:02,229] Trial 1 failed with value None."},{"assistant_initiates":false,"asst_text":"\u041f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u0441 `ValueError: CategoricalDistribution does not support dynamic value space` \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u0435\u0442 \u0438\u0437-\u0437\u0430 \u0442\u043e\u0433\u043e, \u0447\u0442\u043e `Optuna` \u043d\u0435 \u043c\u043e\u0436\u0435\u0442 \u043e\u0431\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0442\u044c \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438 \u0438\u0437\u043c\u0435\u043d\u044f\u044e\u0449\u0438\u0435\u0441\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u043c\u0435\u0442\u043e\u0434\u043e\u0432, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a `suggest_categorical`. \u0415\u0441\u043b\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u0435\u043c\u043e\u0435 \u0432\u0430\u043c\u0438, \u0437\u0430\u0432\u0438\u0441\u0438\u0442 \u043e\u0442 \u0434\u0440\u0443\u0433\u043e\u0433\u043e \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043d\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f (\u0432 \u0432\u0430\u0448\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435, \u043e\u0442 `n_steps`), \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0432\u044b\u0437\u044b\u0432\u0430\u0442\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b.\n\n\u0427\u0442\u043e\u0431\u044b \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u0442\u044c \u044d\u0442\u0443 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0443, \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c `suggest_int` \u0438\u043b\u0438 `suggest_float` \u0438 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0438\u0442\u044c \u0432\u044b\u0431\u043e\u0440 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0437\u0430\u0440\u0430\u043d\u0435\u0435, \u0430 \u043d\u0435 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438 \u0432\u043d\u0443\u0442\u0440\u0438 \u043a\u0430\u0436\u0434\u043e\u0439 \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u0438. \u041e\u0434\u0438\u043d \u0438\u0437 \u043f\u043e\u0434\u0445\u043e\u0434\u043e\u0432 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0442\u043e\u043c, \u0447\u0442\u043e\u0431\u044b \u043f\u0435\u0440\u0432\u043e\u043c \u044d\u0442\u0430\u043f\u0435 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0441\u043f\u0438\u0441\u043e\u043a \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0434\u043b\u044f `batch_size`, \u0430 \u0437\u0430\u0442\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 `batch_size`.\n\n\u0412\u043e\u0442 \u0441\u043f\u043e\u0441\u043e\u0431, \u043a\u0430\u043a \u044d\u0442\u043e \u043c\u043e\u0436\u043d\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c:\n\n1. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f `batch_size`, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440:\n   - \u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b `n_steps` \u0434\u043b\u044f \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0430 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 \u043e\u0434\u043d\u043e\u043c \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0435, \u043a\u043e\u0433\u0434\u0430 \u0432\u044b \u0437\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u0442\u0435 \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0435 \u0438\u0441\u043f\u044b\u0442\u0430\u043d\u0438\u0435.\n   \n2. \u0421\u043e\u0437\u0434\u0430\u0439\u0442\u0435 \u043d\u043e\u0432\u044b\u0439 \u043d\u0430\u0431\u043e\u0440, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0431\u0443\u0434\u0435\u0442 \u0432\u043a\u043b\u044e\u0447\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f `batch_size`, \u043e\u0441\u043d\u043e\u0432\u044b\u0432\u0430\u044f\u0441\u044c \u043d\u0430 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0438 `n_steps`.\n\n\u0412\u043e\u0442 \u043f\u0435\u0440\u0435\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u043d\u044b\u0439 \u043a\u043e\u0434:\n\n```python\ndef get_divisors(n):\n    \"\"\"\u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 \u0447\u0438\u0441\u043b\u0430 n.\"\"\"\n    return [i for i in range(1, n + 1) if n % i == 0]\n\ndef objective(trial):\n    # \u0424\u0438\u043a\u0441\u0438\u0440\u0443\u0435\u043c \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u044f\u0449\u0438\u043a\n    n_steps = trial.suggest_int('n_steps', 16, 4096, log=True)\n\n    # \u041f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u0435\u043c \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u043e\u043f\u0446\u0438\u043e\u043d\u0430 batch_size\n    divisors = get_divisors(n_steps)\n\n    # \u0417\u0430\u0434\u0430\u0435\u043c batch_size \u043a\u0430\u043a \u043e\u0434\u0438\u043d \u0438\u0437 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 n_steps\n    batch_size = Trial.suggest_int('batch_size', min(divisors), max(divisors), step=1)\n\n    learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n    gamma = trial.suggest_float('gamma', 0.9, 1.0)\n\n    model = PPO('MlpPolicy', env, n_steps=n_steps, learning_rate=learning_rate,\n                batch_size=batch_size, verbose=0)\n\n    try:\n        model.learn(total_timesteps=30000)\n\n        # \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u044f\n        obs = env.reset()\n        total_reward = 0.0\n        done = False\n\n        while not done:\n            action, _states = model.predict(obs)\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n\n        return float(total_reward)  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0441\u043a\u0430\u043b\u044f\u0440\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\n    except Exception as e:\n        print(f\"An error occurred during training: {e}\")\n        return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\n\nprint(\"Best hyperparameters: \", study.best_params)\n```\n\n\u0414\u0430\u043d\u043d\u0430\u044f \u0432\u0435\u0440\u0441\u0438\u044f \u043a\u043e\u0434\u0430 \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0438 \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0438\u0432\u0430\u0435\u0442 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0432\u044b\u0431\u043e\u0440\u0430 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u044f `n_steps` \u0434\u043b\u044f \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438 `batch_size`. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430 \u0434\u043b\u044f `batch_size`, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0441\u0442\u0440\u043e\u0438\u0442\u0441\u044f \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043d\u044b\u0445 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439, \u0434\u043e\u043b\u0436\u043d\u043e \u043f\u043e\u043c\u043e\u0447\u044c \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0438 \u0441 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u043e\u0439 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439. \u0415\u0441\u043b\u0438 \u044d\u0442\u043e\u0442 \u043c\u0435\u0442\u043e\u0434 \u043d\u0435 \u043f\u043e\u043c\u043e\u0436\u0435\u0442, \u043f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u044f\u0432\u043d\u043e \u0443\u043a\u0430\u0437\u0430\u0442\u044c \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f `batch_size` \u0431\u0435\u0437 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0438.","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":3230,"n_turns_in_conversation":20,"n_words":357,"source_row_offset":53630,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"b884b9d4517f1dc5b30a5c34","turn_index":4,"user_text":"\u0442\u0435\u043f\u0435\u0440\u044c \u044d\u0442\u0430 \u043e\u0448\u0438\u0431\u043a\u0430:\nValueError: CategoricalDistribution does not support dynamic value space."},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `name 'Trial' is not defined` \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u0435\u0442 \u0438\u0437-\u0437\u0430 \u043e\u043f\u0435\u0447\u0430\u0442\u043a\u0438 \u0432 \u043a\u043e\u0434\u0435. \u0412 \u0441\u0442\u0440\u043e\u043a\u0435, \u0433\u0434\u0435 \u043c\u044b \u043f\u044b\u0442\u0430\u0435\u043c\u0441\u044f \u0432\u044b\u0437\u0432\u0430\u0442\u044c `suggest_int` \u0434\u043b\u044f `batch_size`, \u0432\u044b \u0434\u043e\u043b\u0436\u043d\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e `trial`, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0432\u044b \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043b\u0438 \u043a\u0430\u043a \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 `objective`, \u0430 \u043d\u0435 `Trial`. \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u043c \u044d\u0442\u043e:\n\n```python\ndef get_divisors(n):\n    \"\"\"\u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 \u0447\u0438\u0441\u043b\u0430 n.\"\"\"\n    return [i for i in range(1, n + 1) if n % i == 0]\n\ndef objective(trial):\n    n_steps = trial.suggest_int('n_steps', 16, 4096, log=True)\n\n    # \u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 n_steps\n    divisors = get_divisors(n_steps)\n\n    # \u0422\u0435\u043f\u0435\u0440\u044c \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u0431\u0438\u0440\u0430\u0442\u044c batch_size \u0438\u0437 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439\n    batch_size = trial.suggest_categorical('batch_size', divisors)\n\n    learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n    gamma = trial.suggest_float('gamma', 0.9, 1.0)\n\n    model = PPO('MlpPolicy', env, n_steps=n_steps, learning_rate=learning_rate,\n                batch_size=batch_size, verbose=0)\n\n    try:\n        model.learn(total_timesteps=30000)\n\n        # \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u044f\n        obs = env.reset()\n        total_reward = 0.0\n        done = False\n\n        while not done:\n            action, _states = model.predict(obs)\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n\n        return float(total_reward)  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0441\u043a\u0430\u043b\u044f\u0440\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\n    except Exception as e:\n        print(f\"An error occurred during training: {e}\")\n        return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\n\nprint(\"Best hyperparameters: \", study.best_params)\n```\n\n\u0412 \u044d\u0442\u043e\u043c \u043a\u043e\u0434\u0435:\n- \u041c\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c `suggest_categorical` \u0438 \u043f\u0435\u0440\u0435\u0434\u0430\u0435\u043c \u0435\u043c\u0443 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 \u0432 \u0441\u0442\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u0442\u0438\u043f\u0435 \u0434\u0430\u043d\u043d\u044b\u0445, \u0447\u0442\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043e\u043a, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0445 \u0441 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0435\u0439 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439.\n\n\u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u0432\u0435\u0441\u044c \u043a\u043e\u0434 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e \u0438\u043d\u0442\u0435\u0433\u0440\u0438\u0440\u043e\u0432\u0430\u043d, \u0438 \u0442\u0435\u043f\u0435\u0440\u044c \u043e\u043d \u0434\u043e\u043b\u0436\u0435\u043d \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0431\u0435\u0437 \u043e\u0448\u0438\u0431\u043e\u043a. \u0415\u0441\u043b\u0438 \u0443 \u0432\u0430\u0441 \u0432\u043e\u0437\u043d\u0438\u043a\u043b\u0438 \u0434\u0440\u0443\u0433\u0438\u0435 \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u0438\u043b\u0438 \u0442\u0440\u0443\u0434\u043d\u043e\u0441\u0442\u0438, \u0434\u0430\u0439\u0442\u0435 \u0437\u043d\u0430\u0442\u044c!","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":2077,"n_turns_in_conversation":20,"n_words":231,"source_row_offset":53631,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"87345e66772fa1a5c120ffb1","turn_index":5,"user_text":"name 'Trial' is not defined"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430, \u043e \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u0438\u0434\u0435\u0442 \u0440\u0435\u0447\u044c, \u0432\u043e\u0437\u043d\u0438\u043a\u043b\u0430 \u0438\u0437-\u0437\u0430 \u0442\u043e\u0433\u043e, \u0447\u0442\u043e \u0432\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 `batch_size`, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u043d\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u043c \u043f\u0440\u043e\u0438\u0437\u0432\u0435\u0434\u0435\u043d\u0438\u044f `n_steps * n_envs`. \u0412 \u0434\u0430\u043d\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 `n_envs` \u0440\u0430\u0432\u043d\u043e 1, \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 `n_steps * n_envs` \u0440\u0430\u0432\u043d\u043e `n_steps`, \u0438, \u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e, `batch_size` \u0434\u043e\u043b\u0436\u0435\u043d \u0431\u044b\u0442\u044c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u043c \u0447\u0438\u0441\u043b\u0430 `n_steps`.\n\n\u0427\u0442\u043e\u0431\u044b \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u044d\u0442\u043e\u0439 \u043e\u0448\u0438\u0431\u043a\u0438, \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0438\u0442\u044c \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u044b\u0439 \u0432\u044b\u0431\u043e\u0440 `batch_size`, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 \u0447\u0438\u0441\u043b\u0430 `n_steps`. \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u043d\u0435\u043c\u043d\u043e\u0433\u043e \u0434\u043e\u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u043a\u043e\u0434, \u0447\u0442\u043e\u0431\u044b \u0431\u043e\u043b\u0435\u0435 \u0447\u0435\u0442\u043a\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0442\u044c \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f `batch_size`.\n\n\u0412\u043e\u0442 \u043a\u0430\u043a \u044d\u0442\u043e \u043c\u043e\u0436\u043d\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c:\n\n1. \u041f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 `n_steps`.\n2. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 `suggest_categorical`, \u0447\u0442\u043e\u0431\u044b \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 `batch_size`.\n\n```python\nimport optuna\nfrom stable_baselines3 import PPO  # \u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u0432\u044b \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043b\u0438 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438\n\ndef get_divisors(n):\n    \"\"\"\u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 \u0447\u0438\u0441\u043b\u0430 n.\"\"\"\n    return [i for i in range(1, n + 1) if n % i == 0]\n\ndef objective(trial):\n    n_steps = trial.suggest_int('n_steps', 16, 4096, log=True)\n\n    # \u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 n_steps\n    divisors = get_divisors(n_steps)\n\n    # \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c suggest_categorical \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 batch_size \u0442\u043e\u043b\u044c\u043a\u043e \u0438\u0437 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 n_steps\n    batch_size = trial.suggest_categorical('batch_size', divisors)\n\n    learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n    gamma = trial.suggest_float('gamma', 0.9, 1.0)\n\n    model = PPO('MlpPolicy', env, n_steps=n_steps, learning_rate=learning_rate,\n                batch_size=batch_size, verbose=0)\n\n    try:\n        model.learn(total_timesteps=30000)\n\n        # \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u044f\n        obs = env.reset()\n        total_reward = 0.0\n        done = False\n\n        while not done:\n            action, _states = model.predict(obs)\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n\n        return float(total_reward)  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0441\u043a\u0430\u043b\u044f\u0440\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\n    except Exception as e:\n        print(f\"An error occurred during training: {e}\")\n        return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\n\nprint(\"Best hyperparameters: \", study.best_params)\n```\n\n### \u041f\u043e\u044f\u0441\u043d\u0435\u043d\u0438\u044f:\n1. **get_divisors** - \u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 `n_steps`. \u042d\u0442\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0434\u043b\u044f \u0442\u043e\u0433\u043e, \u0447\u0442\u043e\u0431\u044b \u0432\u044b\u0431\u0440\u0430\u0442\u044c `batch_size`, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u043c.\n2. **suggest_categorical** - \u0412\u044b\u0431\u043e\u0440 `batch_size` \u0438\u0437 \u0437\u0430\u0440\u0430\u043d\u0435\u0435 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044b\u0445 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439.\n\n\u0422\u0435\u043f\u0435\u0440\u044c, \u0435\u0441\u043b\u0438 \u0432\u044b \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 `n_steps`, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, 1034, `batch_size` \u043c\u043e\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0438\u0437 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 1034, \u0447\u0442\u043e \u0443\u0441\u0442\u0440\u0430\u043d\u0438\u0442 \u043e\u0448\u0438\u0431\u043a\u0443, \u043e \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u0432\u044b \u0441\u043e\u043e\u0431\u0449\u0430\u043b\u0438.\n\n\u0421\u043b\u0435\u0434\u0438\u0442\u0435 \u0442\u0430\u043a\u0436\u0435 \u0437\u0430 \u0442\u0435\u043c, \u0447\u0442\u043e\u0431\u044b `n_steps` \u0432\u0441\u0435\u0433\u0434\u0430 \u0431\u044b\u043b\u043e \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u044b\u043c \u0434\u043b\u044f \u0432\u0430\u0448\u0438\u0445 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u0439 \u043a `batch_size`. \u0415\u0441\u043b\u0438 \u0443 \u0432\u0430\u0441 \u0432\u0441\u0451 \u0435\u0449\u0451 \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u044e\u0442 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b, \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u0441\u0442\u043e\u0438\u0442 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0438\u0442\u044c \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u0434\u043b\u044f `n_steps` \u0438\u043b\u0438 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0444\u0438\u043b\u044c\u0442\u0440\u043e\u0432\u0430\u0442\u044c \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u044f.","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":3173,"n_turns_in_conversation":20,"n_words":371,"source_row_offset":53632,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"a43a49d2d13b1f97b1a245bb","turn_index":6,"user_text":"You have specified a mini-batch size of 559, but because the `RolloutBuffer` is of size `n_steps * n_envs = 1034`, after every 1 untruncated mini-batches, there will be a truncated mini-batch of size 475\nWe recommend using a `batch_size` that is a factor of `n_steps * n_envs`.\nInfo: (n_steps=1034 and n_envs=1)"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `ValueError: CategoricalDistribution does not support dynamic value space` \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u0435\u0442, \u043a\u043e\u0433\u0434\u0430 \u0432\u044b \u043f\u044b\u0442\u0430\u0435\u0442\u0435\u0441\u044c \u043f\u0435\u0440\u0435\u0434\u0430\u0442\u044c \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438 \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u044b\u0439 \u0441\u043f\u0438\u0441\u043e\u043a \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 `Optuna` \u0434\u043b\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e `suggest_categorical`. \u042d\u0442\u043e \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442 \u043f\u043e\u0442\u043e\u043c\u0443, \u0447\u0442\u043e `Optuna` \u043f\u044b\u0442\u0430\u0435\u0442\u0441\u044f \u0441\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 (\u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0438) \u0434\u043b\u044f \u0433\u0438\u043f\u0435\u0440\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 \u043c\u0435\u0436\u0434\u0443 \u0438\u0441\u043f\u044b\u0442\u0430\u043d\u0438\u044f\u043c\u0438, \u0438 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0441\u043f\u0438\u0441\u043e\u043a \u043d\u0435 \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d \u0434\u043b\u044f \u044d\u0442\u043e\u0433\u043e.\n\n\u0414\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u044d\u0442\u043e\u0439 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u0434\u0438\u043d \u0438\u0437 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043f\u043e\u0434\u0445\u043e\u0434\u043e\u0432:\n\n1. **\u0417\u0430\u0440\u0430\u043d\u0435\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f**: \u0432\u043c\u0435\u0441\u0442\u043e \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438 \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c\u044b\u0445 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 `n_steps` \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u043d\u0430\u0431\u043e\u0440 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0439 \u0434\u043b\u044f `batch_size`.\n\n2. **\u0424\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u0434\u043b\u044f n_steps**: \u0437\u0430\u0440\u0430\u043d\u0435\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u0434\u043b\u044f `n_steps`, \u0442\u0430\u043a \u0447\u0442\u043e\u0431\u044b \u044d\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432\u0441\u0435\u0433\u0434\u0430 \u0438\u043c\u0435\u043b\u043e \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0443\u0435\u043c\u044b\u0435 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438 \u043f\u0440\u0438 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0438 `study`.\n\n\u0412\u043e\u0442 \u043a\u0430\u043a \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0435\u0440\u0432\u043e\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435:\n\n### \u041f\u0440\u0438\u043c\u0435\u0440 \u043a\u043e\u0434\u0430 \u0441 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u043c \u043d\u0430\u0431\u043e\u0440\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0434\u043b\u044f `batch_size`\n```python\nimport optuna\nfrom stable_baselines3 import PPO  # \u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u0432\u044b \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043b\u0438 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438\n\ndef get_possible_batch_sizes():\n    \"\"\"\u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0435\u043d\u0438\u044f \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0434\u043b\u044f batch_size.\"\"\"\n    return [2, 4, 8, 16, 32]  # \u0417\u0430\u0434\u0430\u0439\u0442\u0435 \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f batch_size\n\ndef objective(trial):\n    # \u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f n_steps\n    n_steps = trial.suggest_int('n_steps', 16, 4096, log=True)\n    \n    # \u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f batch_size\n    batch_sizes = get_possible_batch_sizes()\n    \n    # \u0412\u044b\u0431\u0438\u0440\u0430\u0435\u043c batch_size \u0438\u0437 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439\n    batch_size = trial.suggest_categorical('batch_size', batch_sizes)\n\n    # \u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0430 \u0434\u0440\u0443\u0433\u0438\u0445 \u0433\u0438\u043f\u0435\u0440\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u043e\u0432\n    learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n    gamma = trial.suggest_float('gamma', 0.9, 1.0)\n\n    model = PPO('MlpPolicy', env, n_steps=n_steps, learning_rate=learning_rate,\n                batch_size=batch_size, verbose=0)\n\n    try:\n        model.learn(total_timesteps=30000)\n\n        # \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u044f\n        obs = env.reset()\n        total_reward = 0.0\n        done = False\n\n        while not done:\n            action, _states = model.predict(obs)\n            obs, reward, done, info = env.step(action)\n            total_reward += reward\n\n        return float(total_reward)  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0441\u043a\u0430\u043b\u044f\u0440\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\n    except Exception as e:\n        print(f\"An error occurred during training: {e}\")\n        return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=100)\n\nprint(\"Best hyperparameters: \", study.best_params)\n```\n\n### \u041e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u044f:\n1. **get_possible_batch_sizes**: \u042d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u043d\u0430\u0431\u043e\u0440 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0434\u043b\u044f `batch_size`, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c. \u041f\u043e\u0434\u0431\u0435\u0440\u0438\u0442\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0430\u043a, \u0447\u0442\u043e\u0431\u044b \u0432 \u043d\u0438\u0445 \u0431\u044b\u043b\u0438 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0438.\n\n2. **suggest_categorical**: \u0422\u0435\u043f\u0435\u0440\u044c \u043c\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u0437\u0430\u0440\u0430\u043d\u0435\u0435 \u0437\u0430\u0434\u0430\u043d\u043d\u044b\u0439 \u0441\u043f\u0438\u0441\u043e\u043a \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439, \u0447\u0442\u043e\u0431\u044b `Optuna` \u043c\u043e\u0433 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e \u043e\u0431\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0442\u044c \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435.\n\n\u0412 \u044d\u0442\u043e\u043c \u043f\u043e\u0434\u0445\u043e\u0434\u0435 \u0438\u0437\u0431\u0435\u0433\u0430\u0435\u0442\u0441\u044f \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0438 \u0443\u0441\u0442\u0440\u0430\u043d\u044f\u044e\u0442\u0441\u044f \u043e\u0448\u0438\u0431\u043a\u0438, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0435 \u0441 \u043d\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c\u044e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0439. \u0412\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u0442\u0435 \u0430\u0434\u0430\u043f\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432\u043d\u0443\u0442\u0440\u0438 `get_possible_batch_sizes` \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u043a\u0438 \u0432\u0430\u0448\u0435\u0439 \u0437\u0430\u0434\u0430\u0447\u0438 \u0438 \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439.","behavior":"degenerate_repetition","conversation_hash":"4fd481492db8119f03f563fa8a8d39e9","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":9248,"n_turns_in_conversation":20,"n_words":868,"source_row_offset":53633,"source_shard":"turns-00053.parquet","timestamp":"Tue, 26 Nov 2024 09:43:13 GMT","turn_id":"620c26e5053d313840b0bf9e","turn_index":7,"user_text":"---------------------------------------------------------------------------\nValueError                                Traceback (most recent call last)\nCell In[19], line 39\n     36         return float('-inf')  # \u0412\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043e\u0448\u0438\u0431\u043a\u0438\n     38 study = optuna.create_study(direction='maximize')\n---> 39 study.optimize(objective, n_trials=100)\n     41 print(\"Best hyperparameters: \", study.best_params)\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/study/study.py:475, in Study.optimize(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\n    373 def optimize(\n    374     self,\n    375     func: ObjectiveFuncType,\n   (...)\n    382     show_progress_bar: bool = False,\n    383 ) -> None:\n    384     \"\"\"Optimize an objective function.\n    385 \n    386     Optimization is done by choosing a suitable set of hyperparameter values from a given\n   (...)\n    473             If nested invocation of this method occurs.\n    474     \"\"\"\n--> 475     _optimize(\n    476         study=self,\n    477         func=func,\n    478         n_trials=n_trials,\n    479         timeout=timeout,\n    480         n_jobs=n_jobs,\n    481         catch=tuple(catch) if isinstance(catch, Iterable) else (catch,),\n    482         callbacks=callbacks,\n    483         gc_after_trial=gc_after_trial,\n    484         show_progress_bar=show_progress_bar,\n    485     )\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:63, in _optimize(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\n     61 try:\n     62     if n_jobs == 1:\n---> 63         _optimize_sequential(\n     64             study,\n     65             func,\n     66             n_trials,\n     67             timeout,\n     68             catch,\n     69             callbacks,\n     70             gc_after_trial,\n     71             reseed_sampler_rng=False,\n     72             time_start=None,\n     73             progress_bar=progress_bar,\n     74         )\n     75     else:\n     76         if n_jobs == -1:\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:160, in _optimize_sequential(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\n    157         break\n    159 try:\n--> 160     frozen_trial = _run_trial(study, func, catch)\n    161 finally:\n    162     # The following line mitigates memory problems that can be occurred in some\n    163     # environments (e.g., services that use computing containers such as GitHub Actions).\n    164     # Please refer to the following PR for further details:\n    165     # https://github.com/optuna/optuna/pull/325.\n    166     if gc_after_trial:\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:248, in _run_trial(study, func, catch)\n    241         assert False, \"Should not reach.\"\n    243 if (\n    244     frozen_trial.state == TrialState.FAIL\n    245     and func_err is not None\n    246     and not isinstance(func_err, catch)\n    247 ):\n--> 248     raise func_err\n    249 return frozen_trial\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:197, in _run_trial(study, func, catch)\n    195 with get_heartbeat_thread(trial._trial_id, study._storage):\n    196     try:\n--> 197         value_or_values = func(trial)\n    198     except exceptions.TrialPruned as e:\n    199         # TODO(mamu): Handle multi-objective cases.\n    200         state = TrialState.PRUNED\n\nCell In[19], line 12, in objective(trial)\n      9 divisors = get_divisors(n_steps)\n     11 # \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c suggest_categorical \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 batch_size \u0442\u043e\u043b\u044c\u043a\u043e \u0438\u0437 \u0434\u0435\u043b\u0438\u0442\u0435\u043b\u0435\u0439 n_steps\n---> 12 batch_size = trial.suggest_categorical('batch_size', divisors)\n     14 learning_rate = trial.suggest_float('learning_rate', 1e-6, 1e-1, log=True)\n     15 gamma = trial.suggest_float('gamma', 0.9, 1.0)\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/trial/_trial.py:402, in Trial.suggest_categorical(self, name, choices)\n    351 \"\"\"Suggest a value for the categorical parameter.\n    352 \n    353 The value is sampled from ``choices``.\n   (...)\n    397     :ref:`configurations` tutorial describes more details and flexible usages.\n    398 \"\"\"\n    399 # There is no need to call self._check_distribution because\n    400 # CategoricalDistribution does not support dynamic value space.\n--> 402 return self._suggest(name, CategoricalDistribution(choices=choices))\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/trial/_trial.py:637, in Trial._suggest(self, name, distribution)\n    635 # `param_value` is validated here (invalid value like `np.nan` raises ValueError).\n    636 param_value_in_internal_repr = distribution.to_internal_repr(param_value)\n--> 637 storage.set_trial_param(trial_id, name, param_value_in_internal_repr, distribution)\n    639 self._cached_frozen_trial.distributions[name] = distribution\n    640 self._cached_frozen_trial.params[name] = param_value\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/storages/_in_memory.py:202, in InMemoryStorage.set_trial_param(self, trial_id, param_name, param_value_internal, distribution)\n    200 # Check param distribution compatibility with previous trial(s).\n    201 if param_name in self._studies[study_id].param_distribution:\n--> 202     distributions.check_distribution_compatibility(\n    203         self._studies[study_id].param_distribution[param_name], distribution\n    204     )\n    206 # Set param distribution.\n    207 self._studies[study_id].param_distribution[param_name] = distribution\n\nFile ~/anaconda3/lib/python3.12/site-packages/optuna/distributions.py:678, in check_distribution_compatibility(dist_old, dist_new)\n    676     return\n    677 if dist_old != dist_new:\n--> 678     raise ValueError(\n    679         CategoricalDistribution.__name__ + \" does not support dynamic value space.\"\n    680     )\n\nValueError: CategoricalDistribution does not support dynamic value space."}],"shard":{"index":53,"name":"turns-00053.parquet","rows":69386}}