{"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 0x7e49534827a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e4953482830>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e49534828c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e4953482950>", "_build": "<function ActorCriticPolicy._build at 0x7e49534829e0>", "forward": "<function ActorCriticPolicy.forward at 0x7e4953482a70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e4953482b00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e4953482b90>", "_predict": "<function ActorCriticPolicy._predict at 0x7e4953482c20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e4953482cb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e4953482d40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e4953482dd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e495c7dba80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713550608948612739, "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": 592, "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"}}