{"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 0x7dbac9070af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dbac9070b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dbac9070c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dbac9070ca0>", "_build": "<function ActorCriticPolicy._build at 0x7dbac9070d30>", "forward": "<function ActorCriticPolicy.forward at 0x7dbac9070dc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dbac9070e50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dbac9070ee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7dbac9070f70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dbac9071000>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dbac9071090>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dbac9071120>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dbac9216c80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717690023496120459, "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": -0.007616000000000067, "_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": 492, "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": 0.9995, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}