Learning Near-Pareto-Optimal Conventions in Polynomial Time

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

Bibtex Metadata Paper

Authors

Xiaofeng Wang, Tuomas Sandholm

Abstract

We study how to learn to play a Pareto-optimal strict Nash equilibrium when there exist multiple equilibria and agents may have different pref- erences among the equilibria. We focus on repeated coordination games of non-identical interest where agents do not know the game structure up front and receive noisy payoffs. We design ef´Čücient near-optimal al- gorithms for both the perfect monitoring and the imperfect monitoring setting(where the agents only observe their own payoffs and the joint actions).