{"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 0x7f271b0ef9a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f271b0efa30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f271b0efac0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f271b0efb50>", "_build": "<function ActorCriticPolicy._build at 0x7f271b0efbe0>", "forward": "<function ActorCriticPolicy.forward at 0x7f271b0efc70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f271b0efd00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f271b0efd90>", "_predict": "<function ActorCriticPolicy._predict at 0x7f271b0efe20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f271b0efeb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f271b0eff40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f271b0fc040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f271b0ea080>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1687264446606792594, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "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"}}