{"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 0x7f36b2857320>", "_build": "<function DQNPolicy._build at 0x7f36b28573b0>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7f36b2857440>", "forward": "<function DQNPolicy.forward at 0x7f36b28574d0>", "_predict": "<function DQNPolicy._predict at 0x7f36b2857560>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f36b28575f0>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f36b2857680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f36b284f3f0>"}, "verbose": 1, "policy_kwargs": {"net_arch": [128, 128]}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVYwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLAoWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAACamZm/KVyPvZRoCksChZSMAUOUdJRSlIwEaGlnaJRoEiiWCAAAAAAAAACamRk/KVyPPZRoCksChZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolgIAAAAAAAAAAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLAoWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYCAAAAAAAAAAEBlGghSwKFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "dtype": "float32", "_shape": [2], "low": "[-1.2  -0.07]", "high": "[0.6  0.07]", "bounded_below": "[ True  True]", "bounded_above": "[ True  True]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "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", "n": 3, "_shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 1, "num_timesteps": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": 21121, "action_noise": null, "start_time": 1652297733.5142674, "learning_rate": 0.004, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAEPOmr6Gemo8lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVfQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAABgior6e63E8lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwKGlIwBQ5R0lFKULg=="}, "_episode_num": 611, "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": 74256, "buffer_size": 10000, "batch_size": 128, "learning_starts": 1000, "tau": 1.0, "gamma": 0.98, "gradient_steps": 12, "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:\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    :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 0x7f36b28aae60>", "add": "<function ReplayBuffer.add at 0x7f36b28aaef0>", "sample": "<function ReplayBuffer.sample at 0x7f36b28aaf80>", "_get_samples": "<function ReplayBuffer._get_samples at 0x7f36b282f050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f36b28a72d0>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLEGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "actor": null, "use_sde_at_warmup": false, "exploration_initial_eps": 1.0, "exploration_final_eps": 0.06, "exploration_fraction": 0.2, "target_update_interval": 600, "_n_calls": 100000, "max_grad_norm": 10, "exploration_rate": 0.06, "exploration_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.10.107+-x86_64-with-debian-bullseye-sid #1 SMP Sun Apr 24 15:04:08 UTC 2022", "Python": "3.7.12", "Stable-Baselines3": "1.5.0", "PyTorch": "1.9.1", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}