Faster algorithms for extensive-form game solving via improved smoothing functions

Abstract

Sparse iterative methods, in particular first-order methods, are known to be among the most effective in solving large-scale two-player zero-sum extensive-form games. The convergence rates of these methods depend heavily on the properties of the distance-generating function that they are based on. We investigate both the theoretical and practical performance improvement of first-order methods (FOMs) for solving extensive-form games through better design of the dilated entropy function—a class of distance-generating functions related to the domains associated with the extensive-form games. By introducing a new weighting scheme for the dilated entropy function, we develop the first distance-generating function for the strategy spaces of sequential games that has only a logarithmic dependence on the branching factor of the player. This result improves the overall convergence rate of several FOMs working with dilated entropy function by a factor of $$\Omega (b^dd)$$, where b is the branching factor of the player, and d is the depth of the game tree. Thus far, counterfactual regret minimization methods have been faster in practice, and more popular, than FOMs despite their theoretically inferior convergence rates. Using our new weighting scheme and a practical parameter tuning procedure we show that, for the first time, the excessive gap technique, a classical FOM, can be made faster than the counterfactual regret minimization algorithm in practice for large games, and that the aggressive stepsize scheme of CFR+ is the only reason that the algorithm is faster in practice.