Ziyue Chu

Ph.D. researcher with theoretical foundation and project experience in multi-agent learning. Open to collaborations on scalable multi-agent systems.

I focus on 2 aspects of multi-agent learning: Learning Efficiency and Social Efficiency. The first concerns how much it costs to obtain an answer, and the second concerns how good the answer is in the system view.

As a solution to multi-agent scalability issue, I am interested in online decentralized learning algorithms with theoretical guarantees. I am also keen on translating the theoretical advances into real-world applications.

My research interests include:

• Multi-agent (reinforcement) learning theory with networked info
• Game-theoretic learning for socially efficient cooperation
• Intelligent decision-making for smart-city transportation (e.g., next-generation scalable robotaxi services)