{"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 0x7fca6741e200>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fca6741e290>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fca6741e320>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fca6741e3b0>", "_build": "<function ActorCriticPolicy._build at 0x7fca6741e440>", "forward": "<function ActorCriticPolicy.forward at 0x7fca6741e4d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fca6741e560>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fca6741e5f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fca6741e680>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fca6741e710>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fca6741e7a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fca6741e830>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fca6741af40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1212416, "_total_timesteps": 1200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688978349811705929, "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": -0.010346666666666726, "_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": 296, "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": "3.10.12", "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"}}