Authors

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

Abstract

In this chapter, we introduce hierarchical reinforcement learning, which is a type of methods to improve the learning performance by constructing and leveraging the underlying structures of cognition and decision making process. Specifically, we first introduce the backgrounds and two primary categories of hierarchical reinforcement learning: options framework and feudal reinforcement learning. Then we have a detailed introduction of some typical algorithms in these categories, including strategic attentive writer, option-critic and feudal networks, etc. Finally, we provide a summary of recent works on hierarchical reinforcement learning at the end of this chapter.

Keywords: hierarchical reinforcement learning, options framework, option-critic, feudal reinforcement learning, feudal networks

Content

中文版PDF

Citation

To cite this book, please use this bibtex entry:

@incollection{deepRL-chapter10-2020,
 title={Hierarchical Reinforcement Learning},
 chapter={10},
 author={Yanhua Huang},
 editor={Hao Dong, Zihan Ding, Shanghang Zhang},
 booktitle={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
 publisher={Springer Nature},
 pages={317-334},
 note={\url{http://www.deepreinforcementlearningbook.org}},
 year={2020}
}

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