An efficient approach to model-based hierarchical reinforcement learning

• Main Authors: LI, Zhuoru, NARAYAN, Akshay, LEONG, Tze-Yun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Reinforcement learning; hierarchical reinforcement learning; MAXQ; R-MAX; model-based reinforcement learning; Bench-mark problems; Feature combination; Hierarchical reinforcement learning; Levels of abstraction; Model based approach; Problem characterization; Robotic environments; State-of-the-art algorithms; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/4398; https://ink.library.smu.edu.sg/context/sis_research/article/5401/viewcontent/14771_66644_1_PB.pdf
• Institution: Singapore Management University

We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the comm...