Xiangyu Liu

xiangyu.jpg

2104 Brendan Iribe Center

College Park, Maryland 20740

I am a fourth-year PhD student in Computer Science at the University of Maryland, College Park (UMD) since 2021, working under the guidance of Prof. Kaiqing Zhang. I have also had the privilege of working with Prof. Furong Huang. Prior to this, I completed my undergraduate studies in Computer Science at Shanghai Jiao Tong University (SJTU) from 2017 to 2021, where I conducted my bachelor thesis research with Prof. Ying Wen.

My research focuses on the foundational aspects of (multi-agent) reinforcement learning (RL), particularly on game-theoretical/strategic (NeurIPS 2021, ICML 2023) and partially observable settings (NeurIPS 2024), as well as their applications on adversarially robust RL (ICLR 2024).

More recently, I have expanded my research to explore (multi-)large language model (LLM) agents interactions (Arxiv), leveraging techniques from game theory and online learning.

News

Sep 25, 2024 One paper accepted by NeurIPS 2024 on partially observable RL with privileged information.
Mar 24, 2024 Gave a talk at 2024 INFORMS Optimization Society Conference (IOS 2024) on partially obseravble multi-agent RL at Houston, Texas.
Jan 16, 2024 Three papers accepted at ICLR 2024 including one spotlight.

Selected publications

* denotes equal contribution, † denotes alphabetical order.

  1. NeurIPS 2021
    Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
    Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu, and Yaodong Yang
    Advances in Neural Information Processing Systems, 2021
    Code Website
  2. ICML 2023
    Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing
    Xiangyu Liu, and Kaiqing Zhang
    In International Conference on Machine Learning, 2023
    Extended version under review at SIAM Journal on Control and Optimization (SICON)
    arXiv
  3. NeurIPS 2024
    Provable Partially Observable Reinforcement Learning with Privileged Information
    Yang Cai†, Xiangyu Liu†, Argyris Oikonomou†, and Kaiqing Zhang†
    Advances in Neural Information Processing Systems, 2024
    A shorter version also presented at ICML 2024 ARLET workshop. Full version coming soon.
  4. ICLR 2024 (spotlight)
    Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
    Xiangyu Liu*, Chenghao Deng*, Yanchao Sun, Yongyuan Liang, and Furong Huang
    In The Twelfth International Conference on Learning Representations, 2024
    arXiv Code Website

Talks

Awards

Service