  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.
