Authors

• Yanhua Huang* - Xiaohongshu Technology Co. (iofficium[at]gmail.com)

Abstract

This chapter aims to introduce one of the most important deep reinforcement learning algorithms, called deep Q-networks. We will start with the Q-learning algorithm via temporal difference learning, and introduce the deep Q-networks algorithm and its variants. We will end this chapter with code examples and experimental comparison of deep Q-networks and its variants in practice.

Keywords: temporal difference learning, DQN, double DQN, dueling DQN, prioritized experience replay, distributional reinforcement learning

Content

中文版PDF

Code

Codes for contents in this chapter are available here.

Citation

To cite this book, please use this bibtex entry:

@incollection{deepRL-chapter4-2020,
 title={Deep Q-Networks},
 chapter={4},
 author={Yanhua Huang},
 editor={Hao Dong, Zihan Ding, Shanghang Zhang},
 booktitle={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
 publisher={Springer Nature},
 pages={135-160},
 note={\url{http://www.deepreinforcementlearningbook.org}},
 year={2020}
}

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