| {"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 0x7fa535846a70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa535846b00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa535846b90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa535846c20>", "_build": "<function ActorCriticPolicy._build at 0x7fa535846cb0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa535846d40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa535846dd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa535846e60>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa535846ef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa535846f80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa535847010>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa5358470a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa53583b380>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685534615152755589, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAOZ0ML2PJhq8+ArqOo/2lzvmPIk9q2KPvAAAgD8AAIA/M9RTPU3Zjz5MfTO+qqvpvk5E+rz++SO9AAAAAAAAAACmDzm+rz1RP3BZDryH7gK/EW+OvgPu6z0AAAAAAAAAAADUdr3GKYM/908rvtoDBr9zNgK+t9bJvAAAAAAAAAAAgPhWvY8mO7qOsfQ5APP4tVwAhTopVgu5AACAPwAAgD9N3RA+vTSsPhJCHr+U3NK+DG12vQ97yL4AAAAAAAAAAHqhK76TbHc/Jjq/vedNAL8Yw5i+pW6NPAAAAAAAAAAAzVa4vY8GTLqKa9y6I7QNtjRbb7p0DwE6AAAAAAAAgD/NIag8KWw1ujoJ/beihEuyZoO3u5T2FDcAAIA/AACAPzOBsrw2WgC81ZgUPXKZnjwC1U29p0WEPQAAgD8AAIA/ZtQYPEhPj7q294E6OjxrNcnQPruWzZa5AACAPwAAgD9NTuS9e96CunmLRTOVuquywQiHOhB80bMAAIA/AACAP5oJWbtsBJC78T8uPP7sgzwxKrq8s1JiPQAAgD8AAIA/Zk+SPVKitDwZ2hW+DJTOvkncIL3wtPa8AAAAAAAAAACaGfE5xsOaPqbo7b3Tuu++R8dHvMKFoL0AAAAAAAAAAGYpBr0cQQW87XEFPv7x7zzFK2K9wsrEPQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.007616000000000067, "_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": 1232, "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"}} |