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Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making


Sep 29, 2022
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvari, Chi Jin


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Dive into Big Model Training


Jul 25, 2022
Qinghua Liu, Yuxiang Jiang

* Report 

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A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games


Jul 18, 2022
Zihan Ding, Dijia Su, Qinghua Liu, Chi Jin


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Policy Optimization for Markov Games: Unified Framework and Faster Convergence


Jun 06, 2022
Runyu Zhang, Qinghua Liu, Huan Wang, Caiming Xiong, Na Li, Yu Bai


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Sample-Efficient Reinforcement Learning of Partially Observable Markov Games


Jun 02, 2022
Qinghua Liu, Csaba Szepesvári, Chi Jin


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When Is Partially Observable Reinforcement Learning Not Scary?


Apr 19, 2022
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin


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Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits


Mar 14, 2022
Qinghua Liu, Yuanhao Wang, Chi Jin


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LiMuSE: Lightweight Multi-modal Speaker Extraction


Nov 07, 2021
Qinghua Liu, Yating Huang, Yunzhe Hao, Jiaming Xu, Bo Xu


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V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL


Oct 27, 2021
Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu

* This is the journal version of arXiv:2006.12007, with new results on (1) finding CE and CCE in the multiplayer general-sum setting, (2) monotonic techniques that allow V-learning to output Markov policies in a subset of settings, and (3) decoupling V-learning with the adversarial bandit subroutine 

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The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces


Jun 07, 2021
Chi Jin, Qinghua Liu, Tiancheng Yu


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