Yatong Chen
Papers
1
Total Citations
42
H-Index
1
About
Yatong Chen is a leading researcher in distributed artificial intelligence and multi-agent systems, with a particular focus on reinforcement learning for cooperative robotics. Their most influential work, "Distributed multi‐agent deep reinforcement learning for cooperative multi‐robot pursuit" (2020, 42 citations), addresses the classic multi-robot pursuit problem—a widely used benchmark for evaluating coordination strategies in multi-robot systems. By applying deep reinforcement learning to this challenge, Chen has advanced the development of scalable, decentralized control algorithms that enable robots to collaborate effectively in dynamic environments. This contribution has significant implications for real-world applications such as search-and-rescue, autonomous surveillance, and swarm robotics. Chen’s research bridges theoretical advances in distributed AI with practical robotic systems, demonstrating how learning-based approaches can outperform traditional rule-based methods in complex, cooperative tasks. Their work continues to inspire new directions in multi-agent reinforcement learning, making Yatong Chen a notable figure in the field of intelligent robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1