{"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 0x78fb47110670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78fb47110700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78fb47110790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78fb47110820>", "_build": "<function ActorCriticPolicy._build at 0x78fb471108b0>", "forward": "<function ActorCriticPolicy.forward at 0x78fb47110940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78fb471109d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78fb47110a60>", "_predict": "<function ActorCriticPolicy._predict at 0x78fb47110af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78fb47110b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78fb47110c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78fb47110ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78fb470a6580>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701105193672601046, "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"}}