Hierarchical Reinforcement Learning: A Comprehensive Survey

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.

Automated summary

Hierarchical Reinforcement Learning (HRL) allows for autonomous decomposition of challenging decision-making tasks, with a growing landscape of research approaches. Emphasis is placed on learning strategies, subtask discovery, transfer learning, and multi-agent learning challenges, with proposed open problems and practical application examples highlighted.