{"policy_class": {":type:": "", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f3bca9087b0>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653220700.6098459, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": {":type:": "", ":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.04857599999999995, "ep_info_buffer": {":type:": "", ":serialized:": "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"}, "ep_success_buffer": {":type:": "", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 160, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}