378 citations · 1.8k across the 48 of their papers we have counts for
5 papers · 1 filter
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Gen Li, Yuejie Chi, Yuting Wei +1
This paper studies multi-agent reinforcement learning in Markov games, with the goal of learning Nash equilibria or coarse correlated equilibria (CCE) sample-optimally. All prior r…
Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games
Yuling Yan, Gen Li, Yuxin Chen +1
This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data. Specifically, consider a -discounted infinite-horizon Markov ga…
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
Gen Li, Laixi Shi, Yuxin Chen +2
This paper is concerned with offline reinforcement learning (RL), which learns using pre-collected data without further exploration. Effective offline RL would be able to accommoda…
The Efficacy of Pessimism in Asynchronous Q-Learning
Yuling Yan, Gen Li, Yuxin Chen +1
This paper is concerned with the asynchronous form of Q-learning, which applies a stochastic approximation scheme to Markovian data samples. Motivated by the recent advances in off…
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Laixi Shi, Gen Li, Yuting Wei +2
Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment. To counter the insufficient coverage…