Asymmetric co-evolution for imperfect-information zero-sum games

Abstract

We present an asymmetric co-evolutionary learning algorithm for imperfect-information zero-sum games. This algorithm is designed so that the fitness of the individual agents is calculated in a way that is compatible with the goal of game-theoretic optimality. This compatibility has been somewhat lacking in previous co-evolutionary approaches, as these have often depended on unwarranted assumptions about the absolute and relative strength of players. Our algorithm design is tested on a game for which the optimal strategy is known, and is seen to work well.