| {"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 0x7f3662472c20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3662472cb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3662472d40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3662472dd0>", "_build": "<function ActorCriticPolicy._build at 0x7f3662472e60>", "forward": "<function ActorCriticPolicy.forward at 0x7f3662472ef0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3662472f80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3662473010>", "_predict": "<function ActorCriticPolicy._predict at 0x7f36624730a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3662473130>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f36624731c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3662473250>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f366260b840>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1719470598904852546, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAABAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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:": "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 Fri May 24 14:06:39 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |