{"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 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__": "", "_get_constructor_parameters": "", "reset_noise": "", "_build_mlp_extractor": "", "_build": "", "forward": "", "extract_features": "", "_get_action_dist_from_latent": "", "_predict": "", "evaluate_actions": "", "get_distribution": "", "predict_values": "", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f049b9bb5d0>"}, "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": 1, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677860807131519833, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "", ":serialized:": "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"}, "_last_obs": {":type:": "", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAABbR6j4xggg/Dh+ZPiLzI795qaE+Yu6ZOwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "ep_info_buffer": {":type:": "", ":serialized:": "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"}, "ep_success_buffer": {":type:": "", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 9770, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.98, "ent_coef": 0.01, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}