Research Area:  Machine Learning
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.
Author(s) Name:  Shubham Pateria , Budhitama Subagdja , Ah-hwee Tan , Chai Quek
Journal name:  ACM Computing Surveys
Publisher name:  ACM
Volume Information:  Volume 54,Issue 5,June 2022,Article No: 109,pp 1–35
Paper Link:   https://dl.acm.org/doi/abs/10.1145/3453160