My research interests fall at the intersection between machine learning and distributed systems, with the aim of developing algorithms that can endow machines with the ability to autonomously acquire the skills to cooperate, communicate, and compete, and building next generation of intelligent systems that can better serve for our daily life.
In particular, I currently focus on
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 2022, and also postdoc researchers. If you are interested, drop me an email.
Our two papers “The Emergence of Individuality” and “FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning” were accepted at ICML’21.
Our paper “Learning Individually Inferred Communication for Multi-Agent Cooperation” was accepted at NIPS’20 for oral presentation. Congratulations to Ziluo Ding.
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…]
ICLR 2022 2021, NeurIPS 2021 2020, ICML 2021, CoRL 2020, IJCAI 2020 2021, AAMAS 2020, INFOCOM 2016 2019 2020, MM 2017 2018
Nature Machine Intelligence
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