ensuring best_model is placed in ram and saved to disk and loaded from disk
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@ -62,7 +62,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
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env_id = "train_env"
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env_id = "train_env"
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num_cpu = int(dk.thread_count / 2)
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num_cpu = int(dk.thread_count / 2)
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train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
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train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
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self.CONV_WIDTH) for i in range(train_num_cpu)])
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self.CONV_WIDTH) for i in range(num_cpu)])
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eval_env_id = 'eval_env'
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eval_env_id = 'eval_env'
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eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
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eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
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