| {"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 0x7d61956d3880>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d61956d3910>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d61956d39a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d61956d3a30>", "_build": "<function ActorCriticPolicy._build at 0x7d61956d3ac0>", "forward": "<function ActorCriticPolicy.forward at 0x7d61956d3b50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d61956d3be0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d61956d3c70>", "_predict": "<function ActorCriticPolicy._predict at 0x7d61956d3d00>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d61956d3d90>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d61956d3e20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d61956d3eb0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d619587f240>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1722502841707576520, "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-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": "2.3.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |