{"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 0x7fea0b4af2e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fea0b4af370>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fea0b4af400>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fea0b4af490>", "_build": "<function ActorCriticPolicy._build at 0x7fea0b4af520>", "forward": "<function ActorCriticPolicy.forward at 0x7fea0b4af5b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fea0b4af640>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fea0b4af6d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fea0b4af760>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fea0b4af7f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fea0b4af880>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fea0b4af910>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fea0b4aacc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 4014080, "_total_timesteps": 4000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688935827709216077, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAAAaZDxIFZO6blcHNfF8NzAkqy47MiBqtAAAgD8AAIA/mnWPvLh+67mNfQk5kz8LuebYb7ubOsm3AACAPwAAgD+mtnw+YoWgP8Ztjz4CZ/q+itASPyG3ST0AAAAAAAAAAKqNa77KNzw/yhxcPRd8IL9NRre+vTldPgAAAAAAAAAAmn9KPXspjLxPesu9NTGUvI/riz01Sl28AACAPwAAgD+wPIi+uTmlPx4D0L408Au/qL4Ov1j0Dr4AAAAAAAAAAE05Az32BFy6mJAnNGaEy69xeHE5FqawswAAgD8AAIA/ANVkPVQelD+nF5g+pk5Tv+Al8j068oI+AAAAAAAAAACNlN69+bUuPtaXCT7/GQ2/t97pvbKUAD4AAAAAAAAAANqQ0r1vcHU+QE2RPpkiAr8IsCm9IojjPQAAAAAAAAAAmq82vm6gUD+Vo269ULotv6khtL6O6O07AAAAAAAAAABmdoA8H6Xru4keCL0z2Z88KF9OPTWohb0AAIA/AACAP3Okgz2o9CE/dtJMPerBOL+m7Cs+PXbxPAAAAAAAAAAAzU2oPPXHtT+BFQA/CoF/PWKZNLwBKiI8AAAAAAAAAAAz5N28w5kjun9mn7qvE7u1ljq9uvsMvTkAAAAAAACAPyZQ5b3zCMQ+pcySPfG7Hb/Hbii+U5+4PQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": -0.0035199999999999676, "_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": 7360, "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": 1024, "n_epochs": 20, "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"}}