{"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 0x7d21bafd55a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d21bafd5630>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d21bafd56c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d21bafd5750>", "_build": "<function ActorCriticPolicy._build at 0x7d21bafd57e0>", "forward": "<function ActorCriticPolicy.forward at 0x7d21bafd5870>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d21bafd5900>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d21bafd5990>", "_predict": "<function ActorCriticPolicy._predict at 0x7d21bafd5a20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d21bafd5ab0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d21bafd5b40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d21bafd5bd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d21baf8bc80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1709041532017047980, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}