论文标题
预期的虚拟游戏
Anticipatory Fictitious Play
论文作者
论文摘要
虚拟游戏是计算矩阵游戏NASH平衡的算法。最近，虚拟游戏的机器学习变体已成功地应用于复杂的现实游戏。本文对虚拟游戏进行了简单的修改，这与原始作品相比是一个严格的改进：它具有相同的理论最差融合率，同样适用于机器学习环境，并且具有出色的经验表现。我们对算法进行了广泛的比较与虚拟游戏，证明了某些类别的游戏的最佳收敛速率，在各种游戏中以数值为数字上的出色性能，并以将这些算法扩展到深度多种强化学习的设置的实验结论。
Fictitious play is an algorithm for computing Nash equilibria of matrix games. Recently, machine learning variants of fictitious play have been successfully applied to complicated real-world games. This paper presents a simple modification of fictitious play which is a strict improvement over the original: it has the same theoretical worst-case convergence rate, is equally applicable in a machine learning context, and enjoys superior empirical performance. We conduct an extensive comparison of our algorithm with fictitious play, proving an optimal convergence rate for certain classes of games, demonstrating superior performance numerically across a variety of games, and concluding with experiments that extend these algorithms to the setting of deep multiagent reinforcement learning.
