{"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 0x793734e68af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793734e68b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x793734e68c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x793734e68ca0>", "_build": "<function ActorCriticPolicy._build at 0x793734e68d30>", "forward": "<function ActorCriticPolicy.forward at 0x793734e68dc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x793734e68e50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x793734e68ee0>", "_predict": "<function ActorCriticPolicy._predict at 0x793734e68f70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x793734e69000>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x793734e69090>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x793734e69120>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x793734df92c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1725553597176703880, "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": -0.007616000000000067, "_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": 492, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.4.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}