Theoretical and Practical Advances on Smoothing for Extensive-Form Games
This work addresses the computational efficiency of solving extensive-form games, which is important for AI in game theory and decision-making, by providing a practical speedup over existing methods.
The authors tackled the problem of accelerating first-order methods for solving large-scale two-player zero-sum extensive-form games by designing a new weighting scheme for the dilated entropy function, which improved convergence rates by a factor of Ω(b^d d) and made the excessive gap technique faster than CFR+ in practice.
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 the acceleration of first-order methods 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 no dependence on the branching factor of the player. This result improves the convergence rate of several first-order methods by a factor of $Ω(b^dd)$, 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 first-order methods despite their theoretically inferior convergence rates. Using our new weighting scheme and practical tuning we show that, for the first time, the excessive gap technique can be made faster than the fastest counterfactual regret minimization algorithm, CFR+, in practice.