research-article

Hierarchical Reinforcement Learning: A Comprehensive Survey

Authors:

Shubham Pateria,

Budhitama Subagdja,

Chai QuekAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 5

Article No.: 109, Pages 1 - 35

https://doi.org/10.1145/3453160

Published: 05 June 2021 Publication History

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.

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Index Terms

Hierarchical Reinforcement Learning: A Comprehensive Survey
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Reinforcement learning

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ACM Computing Surveys Volume 54, Issue 5

June 2022

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ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3467690

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Published: 05 June 2021

Accepted: 01 February 2021

Revised: 01 February 2021

Received: 01 July 2020

Published in CSUR Volume 54, Issue 5

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