{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCURRTlBvbGljeZSTlC4=", "__module__": "stable_baselines3.dqn.policies", "__doc__": "\n    Policy class with Q-Value Net and target net for DQN\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 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 DQNPolicy.__init__ at 0x7fa5cd3054c0>", "_build": "<function DQNPolicy._build at 0x7fa5cd305550>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7fa5cd3055e0>", "forward": "<function DQNPolicy.forward at 0x7fa5cd305670>", "_predict": "<function DQNPolicy._predict at 0x7fa5cd305700>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7fa5cd305790>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7fa5cd305820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa5cd3064e0>"}, "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:": "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", "n": 4, "_shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 16, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670291384042692505, "learning_rate": 0.0001, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEBAQEBAQEBAQEBAQEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 2041, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 14844, "buffer_size": 1000000, "batch_size": 64, "learning_starts": 50000, "tau": 1.0, "gamma": 0.999, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==", "__module__": "stable_baselines3.common.buffers", "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x7fa5cd2df1f0>", "add": "<function ReplayBuffer.add at 0x7fa5cd2df280>", "sample": "<function ReplayBuffer.sample at 0x7fa5cd2df310>", "_get_samples": "<function ReplayBuffer._get_samples at 0x7fa5cd2df3a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa5cd356c00>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLBGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "actor": null, "use_sde_at_warmup": false, "exploration_initial_eps": 1.0, "exploration_final_eps": 0.05, "exploration_fraction": 0.1, "target_update_interval": 625, "_n_calls": 62500, "max_grad_norm": 10, "exploration_rate": 0.05, "exploration_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}