Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes
This work addresses the challenge of inferring agent behaviors in mean field games from limited data, which is incremental as it applies an existing non-parametric technique to a specific domain problem.
The paper tackles the inverse problem in mean field games by using a Gaussian Process framework to recover agents' strategic actions and environment configurations from partial, noisy observations of population and environment data, providing a probabilistic inference tool for scenarios with incomplete or contaminated datasets.
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.