{"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 0x78d8d1458c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78d8d1458ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78d8d1458d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78d8d1458dc0>", "_build": "<function ActorCriticPolicy._build at 0x78d8d1458e50>", "forward": "<function ActorCriticPolicy.forward at 0x78d8d1458ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78d8d1458f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78d8d1459000>", "_predict": "<function ActorCriticPolicy._predict at 0x78d8d1459090>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78d8d1459120>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78d8d14591b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78d8d1459240>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78d8d15fbf80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1714577143521669993, "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": 272, "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"}}