| {"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 0x7d0ffc1651b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d0ffc165240>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d0ffc1652d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d0ffc165360>", "_build": "<function ActorCriticPolicy._build at 0x7d0ffc1653f0>", "forward": "<function ActorCriticPolicy.forward at 0x7d0ffc165480>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d0ffc165510>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d0ffc1655a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d0ffc165630>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d0ffc1656c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d0ffc165750>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d0ffc1657e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d0f970b6200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1726747442163955007, "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": "2.4.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |