{"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 0x7c3afc1d3d00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c3afc1d3d90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c3afc1d3e20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c3afc1d3eb0>", "_build": "<function ActorCriticPolicy._build at 0x7c3afc1d3f40>", "forward": "<function ActorCriticPolicy.forward at 0x7c3afc1c8040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c3afc1c80d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c3afc1c8160>", "_predict": "<function ActorCriticPolicy._predict at 0x7c3afc1c81f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c3afc1c8280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c3afc1c8310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c3afc1c83a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c3afc1676c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 14384, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1710949842832126831, "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": 392, "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"}}