| {"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 0x7de32ac593a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7de32ac59440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7de32ac594e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7de32ac59580>", "_build": "<function ActorCriticPolicy._build at 0x7de32ac59620>", "forward": "<function ActorCriticPolicy.forward at 0x7de32ac596c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7de32ac59760>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7de32ac59800>", "_predict": "<function ActorCriticPolicy._predict at 0x7de32ac598a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7de32ac59940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7de32ac599e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7de32ac59a80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7de32adbc1c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1741797258704806392, "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": 249, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu124", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |