| {"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 0x7f46f24811b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f46f2481240>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f46f24812d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f46f2481360>", "_build": "<function ActorCriticPolicy._build at 0x7f46f24813f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f46f2481480>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f46f2481510>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f46f24815a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f46f2481630>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f46f24816c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f46f2481750>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f46f24817e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f46f2488540>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683792193815772211, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |