{"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 0x7c9f7a8dadd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c9f7a8dae60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c9f7a8daef0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c9f7a8daf80>", "_build": "<function ActorCriticPolicy._build at 0x7c9f7a8db010>", "forward": "<function ActorCriticPolicy.forward at 0x7c9f7a8db0a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c9f7a8db130>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c9f7a8db1c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7c9f7a8db250>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c9f7a8db2e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c9f7a8db370>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c9f7a8db400>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c9f7aa7e980>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1710549478137594444, "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": 186, "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": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 6, "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": "False", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}