{"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 0x7a4122a3b880>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a4122a3b910>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a4122a3b9a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a4122a3ba30>", "_build": "<function ActorCriticPolicy._build at 0x7a4122a3bac0>", "forward": "<function ActorCriticPolicy.forward at 0x7a4122a3bb50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a4122a3bbe0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a4122a3bc70>", "_predict": "<function ActorCriticPolicy._predict at 0x7a4122a3bd00>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a4122a3bd90>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a4122a3be20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a4122a3beb0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a4122a359c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1693230833315585671, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 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"}}