Stochastic nash equilibrium seeking for games with general nonlinear payoffs

Abstract

Extremum seeking is extended from standard multi-input optimization problems to multi-player non-cooperative games. The players do not have knowledge of the payoff functions and only measure their own payoff values. The players are also unaware of the other players’ actions. Extremum seeking is shown to achieve convergence to the Nash equilibrium of the underlying static game.