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":type:": "<class 'abc.ABCMeta'>",
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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 ",
"__init__": "<function DQNPolicy.__init__ at 0x7f4789073490>",
"_build": "<function DQNPolicy._build at 0x7f4789073520>",
"make_q_net": "<function DQNPolicy.make_q_net at 0x7f47890735b0>",
"forward": "<function DQNPolicy.forward at 0x7f4789073640>",
"_predict": "<function DQNPolicy._predict at 0x7f47890736d0>",
"_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f4789073760>",
"set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f47890737f0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7f4789082280>"
},
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"policy_kwargs": {},
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"bounded_below": "[ True True True True True]",
"bounded_above": "[ True True True True True]",
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"add": "<function ReplayBuffer.add at 0x7f478906b130>",
"sample": "<function ReplayBuffer.sample at 0x7f478906b1c0>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7f478906b250>",
"__abstractmethods__": "frozenset()",
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"exploration_initial_eps": 1.0,
"exploration_final_eps": 0.05,
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