| {"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 0x79ac0b2156c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79ac0b215750>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79ac0b2157e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79ac0b215870>", "_build": "<function ActorCriticPolicy._build at 0x79ac0b215900>", "forward": "<function ActorCriticPolicy.forward at 0x79ac0b215990>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79ac0b215a20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79ac0b215ab0>", "_predict": "<function ActorCriticPolicy._predict at 0x79ac0b215b40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79ac0b215bd0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79ac0b215c60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79ac0b215cf0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79ac0b20d900>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1708605316303033383, "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:": "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-5.15.133+-x86_64-with-glibc2.31 # 1 SMP Tue Dec 19 13:14:11 UTC 2023", "Python": "3.10.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.2", "GPU Enabled": "True", "Numpy": "1.26.3", "Cloudpickle": "3.0.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.26.2"}} |