Learning and Planning with Hierarchical Reinforcement Learning Models

Abstract

Hierarchical Reinforcement Learning (HRL) is a subfield of reinforcement learning that addresses the challenge of solving complex tasks by decomposing them into simpler subtasks. This approach leverages the principles of hierarchy and abstraction, enabling agents to learn and perform tasks more efficiently. This paper provides an in-depth review of HRL, exploring its theoretical foundations, key algorithms, and applications. We also discuss current challenges and future directions in this rapidly evolving field.