17 citations · 63 across the 17 of their papers we have counts for
8 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…
A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models
Gen Li, Yuting Wei
Approximate message passing (AMP) emerges as an effective iterative paradigm for solving high-dimensional statistical problems. However, prior AMP theory -- which focused mostly on…
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…
Pessimism for Offline Linear Contextual Bandits using Confidence Sets
Gene Li, Cong Ma, Nathan Srebro
We present a family of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell…
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…
Specializing Versatile Skill Libraries using Local Mixture of Experts
Onur Celik, Dongzhuoran Zhou, Ge Li +2
A long-cherished vision in robotics is to equip robots with skills that match the versatility and precision of humans. For example, when playing table tennis, a robot should be cap…