{"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 0x7fcf4feba3b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcf4feba440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcf4feba4d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcf4feba560>", "_build": "<function ActorCriticPolicy._build at 0x7fcf4feba5f0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcf4feba680>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcf4feba710>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcf4feba7a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcf4feba830>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcf4feba8c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcf4feba950>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcf4feba9e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fcf4feacf00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688542061835377811, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWV9QAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaAAAAAAAAAAM6XCb/VQsO7TJoJv7zv9Lu52ji/W7IMvF+/Fb/tELS7zisfv6h7+DrTYwK/fcgOvEe5Db/LuQi7rYTpvpb/MrwKJBq/ho+CvIAKH7+4ek28zRG8vloj3LiWaea+BnkDvPuICb/tGCU8GKEHvxR4GrsjDvm+mCoevOgbEr/eE6g4lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwKGlIwBQ5R0lFKULg=="}, "_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": -0.1468799999999999, "_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": 36, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True]", "bounded_above": "[ True  True]", "_shape": [2], "low": "[-1.2  -0.07]", "high": "[0.6  0.07]", "low_repr": "[-1.2  -0.07]", "high_repr": "[0.6  0.07]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIAwAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "3", "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}