{"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 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__": "<function ActorCriticPolicy.__init__ at 0x7f577127dc20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f577127dcb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f577127dd40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f577127ddd0>", "_build": "<function ActorCriticPolicy._build at 0x7f577127de60>", "forward": "<function ActorCriticPolicy.forward at 0x7f577127def0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f577127df80>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5771287050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f57712870e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5771287170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5771287200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f57712cd9c0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":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:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 606208, "_total_timesteps": 600000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652034233.5091095, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_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": -0.010346666666666726, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 185, "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": 32, "n_epochs": 5, "clip_range": {":type:": "<class 'function'>", ":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"}}