The CFR framework has been a powerful tool for solving large-scale
extensive-form games in practice. However, the theoretical rate at which past
CFR-based algorithms converge to the Nash equilibrium is on the order of
$O(T^{-1/2})$, where $T$ is the number of iterations. In contrast, first-order
methods can be used to achieve a $O(T^{-1})$ dependence on iterations, yet
these methods have been less successful in practice. In this work we present
the first CFR variant that breaks the square-root dependence on iterations. By
combining and extending recent advances on predictive and stable regret
minimizers for the matrix-game setting we show that it is possible to leverage
"optimistic" regret minimizers to achieve a $O(T^{-3/4})$ convergence rate
within CFR. This is achieved by introducing a new notion of
stable-predictivity, and by setting the stability of each counterfactual regret
minimizer relative to its location in the decision tree. Experiments show that
this method is faster than the original CFR algorithm, although not as fast as
newer variants, in spite of their worst-case $O(T^{-1/2})$ dependence on
iterations.

本文提出了第一个在 CFIR 基础上打破了迭代次数平方根的收敛速度的 CFR 变体，通过优化后的遗憾最小化器和新的稳定性概念，在 CFR 中引入了稳定可预测性，并将每个遗憾最小化器稳定性设置为所在决策树中的位置，实现了 $O (T^{-3/4})$ 的收敛速率。