Xueguang Lyu (吕雪广) is a CS Ph.D. student at Khoury College of Computer Sciences of Northeastern University, advised by Christopher Amato. Xueguang’s research interests include reinforcement learning, artificial intelligence, and multi-agent system.

Xueguang is interested in enabling effective multi-agent exploration and cooperation in game-theoritic simulations as well as in real-world tasks. His recent work focuses on analyzing multi-agent actor-critic methods in centralized training settings, and utilizing centralized training to learn decentralized cooperating policies.