| {"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 0x7e2698cbd3f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e2698cbd480>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e2698cbd510>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e2698cbd5a0>", "_build": "<function ActorCriticPolicy._build at 0x7e2698cbd630>", "forward": "<function ActorCriticPolicy.forward at 0x7e2698cbd6c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e2698cbd750>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e2698cbd7e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7e2698cbd870>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e2698cbd900>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e2698cbd990>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e2698cbda20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e2698cb1240>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1692604210984820436, "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": 248, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |