Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes

TL;DR

This paper introduces a Gaussian Process-based framework to infer agents' strategies and environmental factors in mean field games from partial, noisy observational data, enabling better understanding of complex multi-agent systems.

Contribution

It proposes a novel probabilistic method using Gaussian Processes to solve inverse problems in mean field games, recovering hidden strategies and environment details from limited data.

Findings

Successfully infers agent strategies from noisy data

Handles partial and incomplete observations effectively

Provides a probabilistic approach for inverse mean field game problems

Abstract

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.