This paper investigates the challenge of learning in black-box games, where the underlying utility function is unknown to any of the agents. While there is an extensive body of literature on the theoretical analysis of algorithms for computing the Nash equilibrium with complete information about the game, studies on Nash equilibrium in black-box games are less common. In this paper, we focus on learning the Nash equilibrium when the only available information about an agent's payoff comes in the form of empirical queries. We provide a no-regret learning algorithm that utilizes Gaussian processes to identify the equilibrium in such games. Our approach not only ensures a theoretical convergence rate but also demonstrates effectiveness across a variety collection of games through experimental validation.

翻译：本文研究了黑盒博弈中的学习挑战，其中任何智能体均未知底层效用函数。尽管已有大量文献对完全信息博弈中计算纳什均衡的算法进行理论分析，但针对黑盒博弈纳什均衡的研究相对较少。本文聚焦于当智能体收益的唯一可用信息来自经验查询时，如何学习纳什均衡。我们提出了一种利用高斯过程识别此类博弈均衡的无遗憾学习算法。该方法不仅保证了理论收敛速率，还通过实验验证在多种博弈集合中展现了有效性。