Solution concepts of traditional game theory assume entirely rational
players; therefore, their ability to exploit subrational opponents is limited.
One type of subrationality that describes human behavior well is the quantal
response. While there exist algorithms for computing solutions against quantal
opponents, they either do not scale or may provide strategies that are even
worse than the entirely-rational Nash strategies. This paper aims to analyze
and propose scalable algorithms for computing effective and robust strategies
against a quantal opponent in normal-form and extensive-form games. Our
contributions are: (1) we define two different solution concepts related to
exploiting quantal opponents and analyze their properties; (2) we prove that
computing these solutions is computationally hard; (3) therefore, we evaluate
several heuristic approximations based on scalable counterfactual regret
minimization (CFR); and (4) we identify a CFR variant that exploits the bounded
opponents better than the previously used variants while being less exploitable
by the worst-case perfectly-rational opponent.

本文旨在分析和提出可扩展的算法，以计算正常形式和广义形式下对量化对手的有效和稳健策略，通过对量化对手的剥削，定义了两种解决方案，分析了它们的特性，并证明了计算这些解决方案是计算上困难的，因此我们评估了几种基于可扩展的对偶后悔最小化的启发式近似方法，并且鉴别了比先前使用的变体更好地利用有界对手的 CFR 变体，同时被最坏情况的完全理性对手所利用。