Jieqing Tan
Papers
1
Total Citations
1
H-Index
1
About
Dr. Jieqing Tan is a leading researcher in artificial intelligence, with a primary focus on reinforcement learning (RL) and its evolution within autonomous systems. Their most-cited work, "A Review of Reinforcement Learning Evolution: Taxonomy, Challenges and Emerging Solutions" (2025), provides a comprehensive taxonomy that maps the rapid advancements in RL, from foundational algorithms to cutting-edge solutions for self-sufficient machines. This seminal review has already garnered attention for systematically categorizing the field’s key challenges—such as sample efficiency, safety, and scalability—while proposing novel frameworks to address them. Dr. Tan’s contributions are pivotal in bridging theoretical RL developments with practical applications in robotics and decision-making systems. By synthesizing decades of research into an accessible roadmap, their work serves as an essential resource for students and practitioners navigating the complexities of modern AI. With a growing citation impact, Dr. Tan continues to shape how researchers understand and advance reinforcement learning, solidifying their role as a key voice in the next generation of intelligent, autonomous technologies.
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Top Papers
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