| {"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 0x7b1c01038550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b1c010385e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b1c01038670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b1c01038700>", "_build": "<function ActorCriticPolicy._build at 0x7b1c01038790>", "forward": "<function ActorCriticPolicy.forward at 0x7b1c01038820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b1c010388b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b1c01038940>", "_predict": "<function ActorCriticPolicy._predict at 0x7b1c010389d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b1c01038a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b1c01038af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b1c01038b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b1c0b12a580>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700241442187481796, "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"}} |