## Qinghua Liu
## Recent Papers
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games
**Qinghua Liu**, Csaba Szepesvári, Chi Jin arXiv preprint; preliminary version appeared in 2022 European Workshop on Reinforcement Learning (Oral presentation)
Policy Optimization for Markov Games: Unified Framework and Faster Convergence Runyu Zhang,**Qinghua Liu**, Huan Wang, Caiming Xiong, Na Li, Yu Bai arXiv preprint
When Is Partially Observable Reinforcement Learning Not Scary?
**Qinghua Liu**, Alan Chung, Csaba Szepesvári, Chi Jin COLT, 2022
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
**Qinghua Liu**, Yuanhao Wang, Chi Jin ICML, 2022 (Long presentation)
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games Zihan Ding, Dijia Su,**Qinghua Liu**, Chi Jin arXiv preprint
V-Learning – A Simple, Efficient, Decentralized Algorithm for Multiagent RL (α-β order) Chi Jin,**Qinghua Liu**, Yuanhao Wang, Tiancheng Yu arXiv preprint; Best Paper in ICLR 2022 Workshop on Gamification and Multiagent Solutions
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces (α-β order) Chi Jin,**Qinghua Liu**, Tiancheng Yu ICML, 2022; preliminary version appeared in ICML 2021 Workshop on Reinforcement Learning Theory
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms [Slides] [RL Theory Seminar] (α-β order) Chi Jin,**Qinghua Liu**, Sobhan Miryoosefi NeurIPS, 2021 (Spotlight)
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
**Qinghua Liu**, Tiancheng Yu, Yu Bai, Chi Jin ICML, 2021
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States Dipendra Misra,**Qinghua Liu**, Chi Jin, John Langford ICLR, 2021
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs [Slides] [RL Theory Seminar] (α-β order) Chi Jin, Sham M. Kakade, Akshay Krishnamurthy,**Qinghua Liu** NeurIPS, 2020 (Spotlight)
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang,**Qinghua Liu**, Hao Liang, Gauri Joshi, H. Vincent Poor NeurIPS, 2020; full version in IEEE Transactions on Signal Processing
## Technical NotesA Tight Lower Bound for Uniformly Stable Algorithms (α-β order)**Qinghua Liu**, Zhou Lu arXiv preprint
## ServicesReviewers for COLT, NeurIPS, ICML, JMLR, ICLR, ALT, IEEE-TSP, IEEE-TAC, etc. |