{"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 0x79fc393963b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79fc39396440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79fc393964d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79fc39396560>", "_build": "<function ActorCriticPolicy._build at 0x79fc393965f0>", "forward": "<function ActorCriticPolicy.forward at 0x79fc39396680>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79fc39396710>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79fc393967a0>", "_predict": "<function ActorCriticPolicy._predict at 0x79fc39396830>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79fc393968c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79fc39396950>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79fc393969e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79fc3939c2c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1711305200047619227, "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": 252, "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"}}