5 papers
Fast Rates in -Potential Games via Regularized Mirror Descent
Claire Chen, Yuheng Zhang
An -potential game is a multi-player non-cooperative interaction in which a global potential function approximates individual player rewards up to a structural bias . While…
Pessimism-Free Offline Learning in General-Sum Games via KL Regularization
Claire Chen, Yuheng Zhang
Offline multi-agent reinforcement learning in general-sum settings is challenged by the distribution shift between logged datasets and target equilibrium policies. While standard m…
Offline Two-Player Zero-Sum Markov Games with KL Regularization
Claire Chen, Yuheng Zhang, Xinyu Liu +3
We study the problem of learning Nash equilibria in offline two-player zero-sum Markov games. While existing approaches often rely on explicit pessimism to address distribution shi…
Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards
Shuze Daniel Liu, Claire Chen, Jiabao Sean Xiao +4
The recent advancement of Large Language Models (LLMs) has established their potential as autonomous interactive agents. However, they often struggle in strategic games of incomple…
Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
In this paper, we study Interaction-Grounded Learning (IGL) [Xie et al., 2021], a paradigm designed for realistic scenarios where the learner receives indirect feedback generated b…