| {"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 0x787d3518c5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x787d3518c670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x787d3518c700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x787d3518c790>", "_build": "<function ActorCriticPolicy._build at 0x787d3518c820>", "forward": "<function ActorCriticPolicy.forward at 0x787d3518c8b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x787d3518c940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x787d3518c9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x787d3518ca60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x787d3518caf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x787d3518cb80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x787d3518cc10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x787d353233c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1716934085502715812, "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 Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}} |