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：本文提出一种高斯过程（GP）框架——一种被广泛认可用于回归与分类任务的非参数技术，以解决平均场博弈（MFGs）中的逆问题。通过利用高斯过程，我们旨在从群体中智能体状态及环境设置的部分含噪观测中，恢复智能体的策略性行动与环境配置。该方法作为一种概率工具，可在完整数据集不可获取或受噪声污染的场景下，从数据中推断平均场博弈中智能体的行为。