| {"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 0x7f007bdd6ef0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f007bdd6f80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f007bdd7010>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f007bdd70a0>", "_build": "<function ActorCriticPolicy._build at 0x7f007bdd7130>", "forward": "<function ActorCriticPolicy.forward at 0x7f007bdd71c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f007bdd7250>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f007bdd72e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f007bdd7370>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f007bdd7400>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f007bdd7490>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f007bdd7520>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f007bf777c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697785987733700108, "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": 310, "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": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |