| {"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 0x78c9228432e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78c922843370>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78c922843400>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78c922843490>", "_build": "<function ActorCriticPolicy._build at 0x78c922843520>", "forward": "<function ActorCriticPolicy.forward at 0x78c9228435b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78c922843640>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78c9228436d0>", "_predict": "<function ActorCriticPolicy._predict at 0x78c922843760>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78c9228437f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78c922843880>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78c922843910>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78c922848480>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699005061271406272, "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": 620, "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": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "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"}} |