Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2021

Abstract

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Data Science and Engineering

Publication

ACM Computing Surveys

Volume

54

Issue

5

First Page

1

Last Page

35

ISSN

0360-0300

Identifier

10.1145/3453160

Publisher

Association for Computing Machinery (ACM)

Embargo Period

8-3-2021

Copyright Owner and License

Author

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