File size: 13,634 Bytes
0cf071f |
1 |
{"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 0x7b099e6eaf80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b099e6eb010>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b099e6eb0a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b099e6eb130>", "_build": "<function ActorCriticPolicy._build at 0x7b099e6eb1c0>", "forward": "<function ActorCriticPolicy.forward at 0x7b099e6eb250>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b099e6eb2e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b099e6eb370>", "_predict": "<function ActorCriticPolicy._predict at 0x7b099e6eb400>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b099e6eb490>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b099e6eb520>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b099e6eb5b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b099e6e7000>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 3014656, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1692027321314267406, "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": -0.004885333333333408, "_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": 736, "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"}} |