{"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 0x7fd776e6e200>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd776e6e290>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd776e6e320>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd776e6e3b0>", "_build": "<function ActorCriticPolicy._build at 0x7fd776e6e440>", "forward": "<function ActorCriticPolicy.forward at 0x7fd776e6e4d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd776e6e560>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd776e6e5f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd776e6e680>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd776e6e710>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd776e6e7a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd776e6e830>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fd776e69180>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1684767029111851014, "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": 248, "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"}}