  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.
