| {"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 0x7f4d445072e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4d44507370>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4d44507400>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4d44507490>", "_build": "<function ActorCriticPolicy._build at 0x7f4d44507520>", "forward": "<function ActorCriticPolicy.forward at 0x7f4d445075b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4d44507640>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4d445076d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4d44507760>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4d445077f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4d44507880>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4d44507910>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4d44502040>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1686609117291497336, "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-5.15.107+-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"}} |