Yiyun Zhang

National University of Defense Technology

Papers

Total Citations

277

H-Index

About

Dr. Yiyun Zhang has made transformative contributions to artificial intelligence, with a primary focus on deep reinforcement learning (RL) and its real-world applications. Their landmark survey, "Deep Reinforcement Learning: A Survey" (2020), has garnered 277 citations, establishing a foundational reference for researchers and practitioners alike. This comprehensive work systematically catalogs the evolution of deep RL, bridging theoretical advances with practical implementations across end-to-end control, robotic manipulation, recommendation systems, and natural language dialogue systems. Dr. Zhang’s research is distinguished by its emphasis on bridging algorithmic innovation with scalable deployment, enabling autonomous agents to learn complex decision-making policies in dynamic environments. Their work has directly influenced the development of more efficient exploration strategies and sample-efficient learning paradigms. Beyond the survey, Dr. Zhang has contributed to advancing multi-agent RL frameworks and transfer learning techniques, further expanding the frontier of intelligent systems. With a citation impact that underscores their role as a key synthesizer and innovator in the field, Dr. Zhang continues to shape how deep RL is understood, taught, and applied across industries. Their scholarship serves as an essential resource for students and researchers seeking to navigate the rapidly evolving landscape of reinforcement learning.