{"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 0x0000020019A05CA0>", "_build": "<function DQNPolicy._build at 0x0000020019A05D30>", "make_q_net": "<function DQNPolicy.make_q_net at 0x0000020019A05DC0>", "forward": "<function DQNPolicy.forward at 0x0000020019A05E50>", "_predict": "<function DQNPolicy._predict at 0x0000020019A05EE0>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x0000020019A05F70>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x0000020019A10040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x00000200198C9180>"}, "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": 10000000, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": 42, "action_noise": null, "start_time": 1680701250540457700, "learning_rate": 0.01, "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": 34384, "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": 156094, "buffer_size": 10000, "batch_size": 64, "learning_starts": 10000, "tau": 1.0, "gamma": 0.99, "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 0x00000200199E7430>", "add": "<function ReplayBuffer.add at 0x00000200199E74C0>", "sample": "<function ReplayBuffer.sample at 0x00000200199E7550>", "_get_samples": "<function ReplayBuffer._get_samples at 0x00000200199E75E0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000002001980CF00>"}, "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": 0.01, "exploration_fraction": 0.1, "target_update_interval": 3, "_n_calls": 625000, "max_grad_norm": 10, "exploration_rate": 0.01, "exploration_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Windows-10-10.0.22621-SP0 10.0.22621", "Python": "3.9.13", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cpu", "GPU Enabled": "False", "Numpy": "1.23.5", "Gym": "0.21.0"}}