An adaptive policy gradient in learning Nash equilibria

A novel Nash equilibria (NE) learning algorithm for finite strategic games is presented in this paper. Based on an assumption that each player tries to maximize his own payoff, the algorithm explores the policies of the players in the policy profile space to increase the payoffs in each learning iteration. This paper investigates the effectiveness of the algorithm and show experimentally that the proposed algorithm accelerates the policy learning process. This algorithm learns faster than other proposed intelligent learning approaches, and can learn almost all the existing NE for a finite strategic game.