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"__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 ",
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"make_q_net": "<function DQNPolicy.make_q_net at 0x7fe229c735b0>",
"forward": "<function DQNPolicy.forward at 0x7fe229c73640>",
"_predict": "<function DQNPolicy._predict at 0x7fe229c736d0>",
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"__abstractmethods__": "frozenset()",
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},
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"bounded_above": "[ True True True True True]",
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"add": "<function ReplayBuffer.add at 0x7fe229c6f130>",
"sample": "<function ReplayBuffer.sample at 0x7fe229c6f1c0>",
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"exploration_initial_eps": 1.0,
"exploration_final_eps": 0.05,
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