32 citations · 93 across the 30 of their papers we have counts for
5 papers · 1 filter
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
Chenlu Ye, Wei Xiong, Quanquan Gu +1
Despite the significant interest and progress in reinforcement learning (RL) problems with adversarial corruption, current works are either confined to the linear setting or lead t…
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond
Han Zhong, Wei Xiong, Sirui Zheng +4
We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable…
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Wei Xiong, Han Zhong, Chengshuai Shi +2
Existing studies on provably efficient algorithms for Markov games (MGs) almost exclusively build on the "optimism in the face of uncertainty" (OFU) principle. This work focuses on…
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
Wei Xiong, Han Zhong, Chengshuai Shi +3
Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms h…
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
Han Zhong, Wei Xiong, Jiyuan Tan +4
We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset colle…