| {"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 0x7fcb4ec97250>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcb4ec972e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcb4ec97370>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcb4ec97400>", "_build": "<function ActorCriticPolicy._build at 0x7fcb4ec97490>", "forward": "<function ActorCriticPolicy.forward at 0x7fcb4ec97520>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcb4ec975b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcb4ec97640>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcb4ec976d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcb4ec97760>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcb4ec977f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcb4ec97880>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fcb4ec8eec0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1686508026383558934, "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-5.15.107+-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"}} |