论文标题
用最大化初始化的虚拟播放
Fictitious Play with Maximin Initialization
论文作者
论文摘要
虚拟游戏最近成为近似多人游戏中NASH平衡策略的最准确的可扩展算法。我们表明，通过仔细选择初始策略，可以显着降低虚拟游戏的平衡近似误差程度。我们提出了几种策略初始化的新程序，并将其与经典方法进行比较，该方法初始化了所有具有同等概率的纯策略。表现最佳的方法（称为Maximin）求解了一个非convex二次程序，以计算初始策略，并在使用5个初始化时，近似误差的近似误差近75％。
Fictitious play has recently emerged as the most accurate scalable algorithm for approximating Nash equilibrium strategies in multiplayer games. We show that the degree of equilibrium approximation error of fictitious play can be significantly reduced by carefully selecting the initial strategies. We present several new procedures for strategy initialization and compare them to the classic approach, which initializes all pure strategies to have equal probability. The best-performing approach, called maximin, solves a nonconvex quadratic program to compute initial strategies and results in a nearly 75% reduction in approximation error compared to the classic approach when 5 initializations are used.
