{"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 0x79dfe0f24ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79dfe0f24f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79dfe0f25000>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79dfe0f25090>", "_build": "<function ActorCriticPolicy._build at 0x79dfe0f25120>", "forward": "<function ActorCriticPolicy.forward at 0x79dfe0f251b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79dfe0f25240>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79dfe0f252d0>", "_predict": "<function ActorCriticPolicy._predict at 0x79dfe0f25360>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79dfe0f253f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79dfe0f25480>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79dfe0f25510>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79df834ba880>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 204432, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1733308711427509138, "learning_rate": 0.00025, "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": 0.803392, "_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": 378, "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": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 10, "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": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}