{"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 0x7f040f79d000>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f040f79d090>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f040f79d120>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f040f79d1b0>", "_build": "<function ActorCriticPolicy._build at 0x7f040f79d240>", "forward": "<function ActorCriticPolicy.forward at 0x7f040f79d2d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f040f79d360>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f040f79d3f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f040f79d480>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f040f79d510>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f040f79d5a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f040f79d630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f040f7a0280>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1684558111496589155, "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": 292, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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"}}