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.

这篇论文研究了学习在黑盒游戏中的挑战，其中底层效用函数对任何代理都是未知的。通过实证查询的形式，我们提供了一种利用高斯过程来识别这类游戏中平衡点的无悔学习算法。我们的方法不仅确保了理论上的收敛速率，还通过实验验证在各种游戏中的有效性。