{"policy_class": {":type:": "", ":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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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__": "", "_get_constructor_parameters": "", "reset_noise": "", "_build_mlp_extractor": "", "_build": "", "forward": "", "_get_action_dist_from_latent": "", "_predict": "", "evaluate_actions": "", "get_distribution": "", "predict_values": "", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f705cda6f60>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 2031616, "_total_timesteps": 2000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670807029955396540, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": {":type:": "", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "", ":serialized:": "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"}, "ep_success_buffer": {":type:": "", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 940, "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:": "", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}