{"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 0x78791f5751b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78791f575240>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78791f5752d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78791f575360>", "_build": "<function ActorCriticPolicy._build at 0x78791f5753f0>", "forward": "<function ActorCriticPolicy.forward at 0x78791f575480>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78791f575510>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78791f5755a0>", "_predict": "<function ActorCriticPolicy._predict at 0x78791f575630>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78791f5756c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78791f575750>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78791f5757e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78791f715900>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713014644196643113, "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:": "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}