Yuxi Li

Papers

Total Citations

587

H-Index

About

Yuxi Li is a prominent researcher specializing in deep reinforcement learning (RL), whose work has significantly shaped how the field is understood, taught, and applied. Best known for synthesizing complex advances in artificial intelligence into accessible, comprehensive frameworks, Li has established himself as a leading voice in bridging theoretical foundations and practical implementation of RL systems. His most influential contribution, "Deep Reinforcement Learning: An Overview" (2017), has garnered over 545 citations and remains a foundational reference for researchers and students entering the field. The paper's structured examination of six core elements, six important mechanisms, and twelve real-world applications provides an invaluable roadmap through a rapidly evolving discipline. He expanded upon this work in a 2018 book-length treatment, offering even richer historical and contemporary context for deep RL developments. More recently, Li has turned his attention to the practical realities of deploying RL systems, addressing opportunities and challenges that arise beyond the laboratory setting. His 2022 work reflects a maturing perspective on the field's limitations and potential. Across his career, Li's ability to synthesize vast bodies of research into coherent, accessible narratives has made him an essential guide for anyone navigating the landscape of modern reinforcement learning.