I am a BOYA Assistant Professor in the School of Computer Science, also affiliated with the Institute for Artificial Intelligence, Peking University.
My current research focuses on
with the aim of developing learning algorithms that can endow agents with the ability to autonomously acquire the skills to cooperate, communicate, and compete, and building next generation of AI systems that can better serve for our daily life.
Short Bio
Before joining Peking University in Fall 2017, I was a postdoc in the Department of Computer Science and Engineering, Pennsylvania State University. I received the PhD degree from the School of Computer Science and Engineering, Nanyang Technological University in 2014, master and bachelor degrees from Southeast University.
I am looking for self-motivated undergraduate students for research internships. I am recruiting PhD students, starting Fall 2025, and also RAs and postdocs. If you are interested, drop me an email.
Our two papers about language learning and language grounding via reinforcement learning were respectively accepted at ACL’23 and ICML’23.
Our paper, MA2ML, was accepted at CVPR’23. MA2ML is a MARL algorithm for full-pipeline automated machine learning.
Multi-Agent Reinforcement Learning (MARL) has recently attracted much attention from the communities of machine learning, artificial intelligence, and multi-agent systems. As an interdisciplinary research field, there are so many unsolved problems, from cooperation to competition, from agent communication to agent modeling, from centralized learning to decentralized learning. MARL has been the main research focus of our lab. We are investigating the field from many perspectives. In the following, we introduce some of our studies. [Read More…]
Reinforcement learning (RL) has the potential be applied to many real-world applications. In our research, we also investigate the applications of RL and Multi-agent RL. Currently, we have been investigating two applications: one is traffic signal control; another is EDA. Traffic signals coordinating traffic movements are the key for transportation efficiency. However, conventional traffic signal control that heavily relies on pre-defined rules and assumptions on traffic conditions is far from intelligence. [Read More…]
IEEE Conference on Games 2022, Keynote Co-Chair
ICML 2021 Workshop on Reinforcement Learning for Real Life, General Co-Chair
INFOCOM 2020 Worksop on Network Intelligence, General Co-Chair
ACM TURC 2018, Award Co-Chair
Associate Editor, ACM Journal on Autonomous Transportation Systems
Guest Editor, Machine Learning Journal Special Issue on Reinforcement Learning for Real Life