{ "policy_class": { ":type:": "", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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__": "", "_get_constructor_parameters": "", "reset_noise": "", "_build_mlp_extractor": "", "_build": "", "forward": "", "_get_action_dist_from_latent": "", "_predict": "", "evaluate_actions": "", "get_distribution": "", "predict_values": "", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe5a7389840>" }, "verbose": 1, "policy_kwargs": {}, "observation_space": { ":type:": "", ":serialized:": "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", "dtype": "float32", "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False]", "_np_random": null, "_shape": [ 26 ] }, "action_space": { ":type:": "", ":serialized:": "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", "dtype": "float32", "low": "[-1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True]", "bounded_above": "[ True True True True True True]", "_np_random": "RandomState(MT19937)", "_shape": [ 6 ] }, "n_envs": 2, "num_timesteps": 2000896, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": 0, "action_noise": null, "start_time": 1640769228.769289, "learning_rate": { ":type:": "", ":serialized:": "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" }, "tensorboard_log": null, "lr_schedule": { ":type:": "", ":serialized:": "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" }, "_last_obs": null, "_last_episode_starts": { ":type:": "", ":serialized:": "gASVigAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwKFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDAgAAlHSUYi4=" }, "_last_original_obs": { ":type:": "", ":serialized:": "gASVWgEAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwJLGoaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUPQAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIAcYnu+AAAAAOIs2j0AAAAA+OEuvgAAAACjtac+AAAAAAz3sT0AAAAAK0aiPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgNdAlb4AAAAAO3/BvQAAAAAEWr6+AAAAACJWgD4AAAAA4br4PAAAAADmp5I/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJR0lGIu" }, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "ep_info_buffer": { ":type:": "", ":serialized:": "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" }, "ep_success_buffer": { ":type:": "", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg==" }, "_n_updates": 977, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.0, "max_grad_norm": 0.0, "normalize_advantage": true, "batch_size": 128, "cg_max_steps": 25, "cg_damping": 0.1, "line_search_shrinking_factor": 0.8, "line_search_max_iter": 10, "target_kl": 0.01, "n_critic_updates": 20, "sub_sampling_factor": 1 }