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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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        "_build": "<function DQNPolicy._build at 0x7f16a6309b90>",
        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f16a6309c20>",
        "forward": "<function DQNPolicy.forward at 0x7f16a6309cb0>",
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        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f16a6309dd0>",
        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f16a6309e60>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc_data object at 0x7f16a63004e0>"
    },
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        "dtype": "float32",
        "low": "[-1.2  -0.07]",
        "high": "[0.6  0.07]",
        "bounded_below": "[ True  True]",
        "bounded_above": "[ True  True]",
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