Shanqi Liu

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About

Shanqi Liu is a researcher advancing the frontiers of multiagent reinforcement learning (RL) and autonomous systems. His work addresses a critical limitation in multiagent RL: the inability of most algorithms to handle dynamic, real-world environments where the number of agents—such as storage robots or drone swarms—changes over time. In his highly cited 2021 paper, "Learning Communication for Cooperation in Dynamic Agent-Number Environment" (13 citations), Liu proposes novel frameworks that enable agents to adapt communication and cooperation strategies without requiring fixed network dimensions or prior knowledge of agent counts. This contribution is foundational for scalable, flexible multiagent systems. Additionally, Liu has demonstrated practical engineering expertise through his 2019 work on the architecture of a driverless robot car based on the EyeBot system (5 citations), developed for the Carolo-Cup competition. This hands-on achievement bridges theoretical RL advances with real-world autonomous navigation. By tackling both algorithmic generality and physical implementation, Liu’s research offers valuable insights for students and researchers working on cooperative AI, swarm robotics, and autonomous driving.