{"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 0x78ae79907a30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78ae79907ac0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78ae79907b50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78ae79907be0>", "_build": "<function ActorCriticPolicy._build at 0x78ae79907c70>", "forward": "<function ActorCriticPolicy.forward at 0x78ae79907d00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78ae79907d90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78ae79907e20>", "_predict": "<function ActorCriticPolicy._predict at 0x78ae79907eb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78ae79907f40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78ae79914040>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78ae799140d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78ae79910640>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690473817546674707, "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}