Jeng-Yih Chiou

National Chung Cheng University, Kun Shan University

Papers

Total Citations

H-Index

About

Jeng-Yih Chiou is a researcher whose work sits at the intersection of game theory, reinforcement learning, and multi-agent robotics. His primary research focus is on developing intelligent strategy systems for cooperative robot teams, particularly in dynamic environments like robot soccer. Chiou’s major contribution lies in his pioneering application of zero-sum game theory to reinforcement learning, creating frameworks where robotic agents can learn optimal cooperative and competitive strategies through self-learning. His most cited work, “Reinforcement learning in zero-sum Markov games for robot soccer systems” (2004, 7 citations), established a foundational method for enabling robots to choose appropriate actions in adversarial settings. He extended this concept in “Cooperative reinforcement learning based on zero-sum games” (2008, 6 citations), refining the learning process for team-based coordination. Additionally, his work on “Robot Control System Based on Distributed Embedded Systems” (2007, 3 citations) demonstrates his expertise in practical system architecture, proposing a modular, distributed control structure for robotic platforms. Though his citation counts are modest, Chiou’s research represents an important early step in applying formal game-theoretic models to real-world multi-robot learning, bridging theoretical algorithms with embedded system implementation.