cs.AI updates on arXiv.org 10月01日 14:00
多玩家非完全信息博弈纳什均衡精确计算
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本文提出一种在多玩家非完全信息博弈中精确计算纳什均衡的方法,该方法基于非线性互补问题公式的二次约束规划,并利用了非线性二次规划的最新进展。算法在解决三玩家Kuhn扑克问题中表现优异,且优于Gambit软件套件中的logit量反应方法。

arXiv:2509.25618v1 Announce Type: cross Abstract: There has been significant recent progress in algorithms for approximation of Nash equilibrium in large two-player zero-sum imperfect-information games and exact computation of Nash equilibrium in multiplayer strategic-form games. While counterfactual regret minimization and fictitious play are scalable to large games and have convergence guarantees in two-player zero-sum games, they do not guarantee convergence to Nash equilibrium in multiplayer games. We present an approach for exact computation of Nash equilibrium in multiplayer imperfect-information games that solves a quadratically-constrained program based on a nonlinear complementarity problem formulation from the sequence-form game representation. This approach capitalizes on recent advances for solving nonconvex quadratic programs. Our algorithm is able to quickly solve three-player Kuhn poker after removal of dominated actions. Of the available algorithms in the Gambit software suite, only the logit quantal response approach is successfully able to solve the game; however, the approach takes longer than our algorithm and also involves a degree of approximation. Our formulation also leads to a new approach for computing Nash equilibrium in multiplayer strategic-form games which we demonstrate to outperform a previous quadratically-constrained program formulation.

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纳什均衡 非完全信息博弈 二次约束规划 非线性互补问题 Gambit软件
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