This paper presents a Gaussian Process (GP) framework, a non-parametric technique widely acknowledged for regression and classification tasks, to address inverse problems in mean field games (MFGs). By leveraging GPs, we aim to recover agents' strategic actions and the environment's configurations from partial and noisy observations of the population of agents and the setup of the environment. Our method is a probabilistic tool to infer the behaviors of agents in MFGs from data in scenarios where the comprehensive dataset is either inaccessible or contaminated by noises.

翻译：本文提出了一种高斯过程框架——一种被广泛认可用于回归和分类任务的非参数技术——来解决平均场博弈中的逆问题。通过利用高斯过程，我们旨在从群体中部分且含噪声的观测数据以及环境设置中恢复智能体的策略行为与环境配置。该方法是一种概率工具，能够在综合数据集不可获取或受到噪声污染的场景下，从数据中推断平均场博弈中智能体的行为。