| {"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 0x7848064ed1c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7848064ed260>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7848064ed300>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7848064ed3a0>", "_build": "<function ActorCriticPolicy._build at 0x7848064ed440>", "forward": "<function ActorCriticPolicy.forward at 0x7848064ed4e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7848064ed580>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7848064ed620>", "_predict": "<function ActorCriticPolicy._predict at 0x7848064ed6c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7848064ed760>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7848064ed800>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7848064ed8a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x784806441a40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1740362937075226141, "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": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu124", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |