This work studies an algorithm, which we call magnetic mirror descent, that
is inspired by mirror descent and the non-Euclidean proximal gradient
algorithm. Our contribution is demonstrating the virtues of magnetic mirror
descent as both an equilibrium solver and as an approach to reinforcement
learning in two-player zero-sum games. These virtues include: 1) Being the
first quantal response equilibria solver to achieve linear convergence for
extensive-form games with first order feedback; 2) Being the first standard
reinforcement learning algorithm to achieve empirically competitive results
with CFR in tabular settings; 3) Achieving favorable performance in 3x3 Dark
Hex and Phantom Tic-Tac-Toe as a self-play deep reinforcement learning
algorithm.

研究磁镜下降算法作为均衡求解器和两人零和游戏强化学习的方法，并证明其在多种场景中都可以取得优秀的性能表现，包括在可扩展形式下的均衡求解和在表格设置下的竞争性结果与 CFR 相比。