{"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 0x7e39af7b0e00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e39af7b0ea0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e39af7b0f40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e39af7b0fe0>", "_build": "<function ActorCriticPolicy._build at 0x7e39af7b1080>", "forward": "<function ActorCriticPolicy.forward at 0x7e39af7b1120>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e39af7b11c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e39af7b1260>", "_predict": "<function ActorCriticPolicy._predict at 0x7e39af7b1300>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e39af7b13a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e39af7b1440>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e39af7b14e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e39af711b00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 5046272, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1738183446201238710, "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": -0.009254400000000107, "_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": 308, "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": 4096, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 256, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu124", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}