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
Citation
PATERIA, Shubham; SUBAGDJA, Budhitama; TAN, Ah-hwee; and QUEK, Chai.
Hierarchical reinforcement learning: A comprehensive survey. (2021). ACM Computing Surveys. 54, (5), 1-35.
Available at: https://ink.library.smu.edu.sg/sis_research/6047
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.