DQN with model-based exploration: efficient learning on environments with sparse rewards

22 Mar 2019 Stephen Zhen Gou Yuyang Liu

We propose Deep Q-Networks (DQN) with model-based exploration, an algorithm combining both model-free and model-based approaches that explores better and learns environments with sparse rewards more efficiently. DQN is a general-purpose, model-free algorithm and has been proven to perform well in a variety of tasks including Atari 2600 games since it's first proposed by Minh et el... (read more)

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