{"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 0x7a58d29b4dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a58d29b4e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a58d29b4ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a58d29b4f70>", "_build": "<function ActorCriticPolicy._build at 0x7a58d29b5000>", "forward": "<function ActorCriticPolicy.forward at 0x7a58d29b5090>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a58d29b5120>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a58d29b51b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7a58d29b5240>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a58d29b52d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a58d29b5360>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a58d29b53f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a58d2951840>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1698857125486929413, "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}