Publications

* denotes equal contribution

Conference Publications

• Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
  Rui Yang*, Han Zhong*, Jiawei Xu*, Amy Zhang, Chongjie Zhang, Lei Han, Tong Zhang
  International Conference on Learning Representations (ICLR) 2024
  

• Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
  Jianliang He, Han Zhong, Zhuoran Yang
  International Conference on Learning Representations (ICLR) 2024
  

• Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation
  Jiayi Huang, Han Zhong, Liwei Wang, Lin F. Yang
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2024
  

• 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
  

• 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 (short version)
  

• 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
  

• Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
  Jiayi Huang, Han Zhong, Liwei Wang, Lin F. Yang
  Conference on Neural Information Processing Systems (NeurIPS) 2023
  

• A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
  Yunchang Yang*, Han Zhong*, Tianhao Wu*, Bin Liu, Liwei Wang, Simon S. Du
  Conference on Neural Information Processing Systems (NeurIPS) 2023
  

• Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
  Shuang Qiu*, Ziyu Dai*, Han Zhong, Zhaoran Wang, Zhuoran Yang, Tong Zhang
  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
  

• Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
  Wei Xiong*, Han Zhong*, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang
  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
  

• 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
  ICLR 2022 Workshop on Gamification and Multiagent Solutions
  

• A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
  Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang
  International Conference on Machine Learning (ICML) 2022
  ICLR 2022 Workshop on Gamification and Multiagent Solutions
  

• 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
  

• Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
  Xiaoyu Chen*, Han Zhong*, Zhuoran Yang, Zhaoran Wang, Liwei Wang
  International Conference on Machine Learning (ICML) 2022
  

• A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
  Yunchang Yang*, Tianhao Wu*, Han Zhong*, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du
  International Conference on Learning Representations (ICLR) 2022
  

• Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
  Han Zhong, Jiayi Huang, Lin F. Yang, Liwei Wang
  Conference on Neural Information Processing Systems (NeurIPS) 2021

Journal Publications

• 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
  ICLR 2022 Workshop on Gamification and Multiagent Solutions

Preprints

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

• Gibbs Sampling from Human Feedback: A Provable KL-constrained Framework for RLHF
  Wei Xiong*, Hanze Dong*, Chenlu Ye*, Han Zhong, Nan Jiang, Tong Zhang
  

• Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
  Han Zhong*, Jiachen Hu*, Yecheng Xue, Tongyang Li, Liwei Wang
  

• 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
  

• Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
  Han Zhong, Zhuoran Yang, Zhaoran Wang, Csaba Szepesvári
  

• Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy
  Han Zhong, Xun Deng, Ethan X. Fang, Zhuoran Yang, Zhaoran Wang, Runze Li