Tonghan Wang (Tsinghua University & Harvard University)
Tonghan Wang is currently working with Prof. Chongjie Zhang at Institute for Interdisciplinary Information Sciences, Tsinghua University, headed by Prof. Andrew Yao. He will join the EconCS group at Harvard University and work with Prof. David Parkes. His primary research goal is to develop innovative models and methods to enable effective multi-agent cooperation, allowing a group of individuals to explore, communicate, and accomplish tasks of higher complexity. His research interests include multi-agent learning, reinforcement learning, and reasoning under uncertainty.
Short Abstract: Multi-agent reinforcement learning holds the promise to imitating the large-scale cooperation behavior of humans. A major challenge faced by this field of machine learning is scalability — the problem search space grows exponentially with the number of agents.The role concept provides a useful tool to design and understand such complex multi-agent systems, which allows agents with a similar role to share similar behaviors. However, existing role-based methods use prior domain knowledge and predefine role structures and behaviors. In contrast, multi-agent reinforcement learning provides flexibility and adaptability, but less efficiency in complex tasks. To merge the best of these two worlds, we propose to synergize these two paradigms and propose the role-oriented MARL learning framework. In this framework, roles are emergent, and agents with similar roles tend to share their learning and to be specialized on certain sub-tasks. In this talk, we will introduce two specific role-based learning algorithms and study how they achieve state-of-the-art performance on complex benchmarks like StarCraft II micromanagement.