| {"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 0x7eca0358a320>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eca0358a3b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eca0358a440>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eca0358a4d0>", "_build": "<function ActorCriticPolicy._build at 0x7eca0358a560>", "forward": "<function ActorCriticPolicy.forward at 0x7eca0358a5f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7eca0358a680>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eca0358a710>", "_predict": "<function ActorCriticPolicy._predict at 0x7eca0358a7a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eca0358a830>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eca0358a8c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eca0358a950>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ec9bd4d8bc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1736476057225152498, "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": 310, "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": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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 Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |