{"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 0x7b85108c9ea0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b85108c9f30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b85108c9fc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b85108ca050>", "_build": "<function ActorCriticPolicy._build at 0x7b85108ca0e0>", "forward": "<function ActorCriticPolicy.forward at 0x7b85108ca170>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b85108ca200>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b85108ca290>", "_predict": "<function ActorCriticPolicy._predict at 0x7b85108ca320>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b85108ca3b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b85108ca440>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b85108ca4d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b85106d8ac0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1695777004789653439, "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": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}