We compare the performance of two popular algorithms, fictitious play and counterfactual regret minimization, in approximating Nash equilibrium in multiplayer games. Despite recent success of counterfactual regret minimization in multiplayer poker and conjectures of its superiority, we show that fictitious play leads to improved Nash equilibrium approximation over a variety of game classes and sizes.

翻译：我们比较了两种流行算法——虚篇博弈与反事实遗憾最小化——在多玩家游戏中近似纳什均衡的性能。尽管反事实遗憾最小化近期在多玩家扑克中取得成功且被认为具有优越性，我们表明虚篇博弈在多种游戏类别与规模中能实现更优的纳什均衡近似。