Learning Near-Pareto-Optimal Conventions in Polynomial Time

We study how to learn to play a Pareto-optimal strict Nash equilibrium when there exist multiple equilibria and agents may have different preferences 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 efficient near-optimal algorithms for both the perfect monitoring and the imperfect monitoring setting(where the agents only observe their own payoffs and the joint actions).

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Xiao Feng Wang, Tuomas Sandholm
• Keywords: Multiple Equilibria, NIPS 2003, NIPS 2007, Noisy Payoffs, Strict Nash Equilibrium