Han Zhong (钟涵)

Han Zhong Ph.D. Student Peking University Email: hanzhong@stu.pku.edu.cn Google Scholar / Twitter / WeChat

About Me

I am a third-year Ph.D. student at Peking University, where I am fortunate to be advised by Professor Liwei Wang. Before that, I obtained a bachelor's degree in Mathematics from University of Science and Technology of China (USTC). Additionally, I had the privilege of conducting research at both the Hong Kong University of Science and Technology (HKUST), where I collaborated with Professor Tong Zhang, and at Microsoft Research Asia (MSRA), where I had the opportunity to work with Doctor Wei Chen.

I work on machine learning. The primary goal of my research is to design provably efficient and practical machine learning algorithms, particularly in the context of interactive decision-making problems. To achieve this goal, my recent researches focus on reinforcement learning theory. Currently, I am also interested in RLHF (for LLMs) and foundation models (for decision-making problems). If you share common interests and would like to explore collaboration or simply have a discussion, feel free to contact me.

Selected Publications

Theoretical Foundation of Interactive Decision Making: We propose a unified framework, GEC, to study the statistical complexity of interactive decision making. We also reveal a potential representation complexity hierarchy among different reinforcement learning paradigms, including model-based RL, policy-based RL, and value-based RL.

• Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity
  Guhao Feng, Han Zhong (alphabetical order)
  

• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
  Zhihan Liu*, Miao Lu*, Wei Xiong*, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang
  Conference on Neural Information Processing Systems (NeurIPS) 2023

• GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond
  Han Zhong*, Wei Xiong*, Sirui Zheng, Liwei Wang, Zhaoran Wang, Zhuoran Yang, Tong Zhang
  

Multi-Agent Reinforcement Learning: We develope the first line of efficient equilibrium-finding algorithms for offline Markov games and Stackelberg Markov games.

• Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers?
  Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
  Journal of Machine Learning Research (JMLR) 2023
  

• Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
  Han Zhong*, Wei Xiong*, Jiyuan Tan*, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang
  International Conference on Machine Learning (ICML) 2022
  

Robust Machine Learning: We focus on understanding distributionally robust reinforcement learning and robust generalization in deep learning.

• Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
  Jose Blanchet, Miao Lu, Tong Zhang, Han Zhong (alphabetical order)
  Conference on Neural Information Processing Systems (NeurIPS) 2023
  

• Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
  Jiachen Hu*, Han Zhong*, Chi Jin, Liwei Wang
  International Conference on Learning Representations (ICLR) 2023
  

• Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
  Binghui Li*, Jikai Jin*, Han Zhong, John E. Hopcroft, Liwei Wang
  Conference on Neural Information Processing Systems (NeurIPS) 2022
  

Policy Optimization: We provide theoretical guarantees for policy optimization algorithms, especially optimistic proximal policy optimization (PPO).

• A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
  Han Zhong, Tong Zhang
  Conference on Neural Information Processing Systems (NeurIPS) 2023
  

• Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
  Tianhao Wu*, Yunchang Yang*, Han Zhong*, Liwei Wang, Simon S. Du, Jiantao Jiao
  International Conference on Machine Learning (ICML) 2022