{"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 0x7cc5e0f06dd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7cc5e0f06e60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7cc5e0f06ef0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7cc5e0f06f80>", "_build": "<function ActorCriticPolicy._build at 0x7cc5e0f07010>", "forward": "<function ActorCriticPolicy.forward at 0x7cc5e0f070a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7cc5e0f07130>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7cc5e0f071c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7cc5e0f07250>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7cc5e0f072e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7cc5e0f07370>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7cc5e0f07400>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7cc5e0f09d00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690401732857109307, "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": -0.004885333333333408, "_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": 740, "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": "3.10.6", "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"}}