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

Jinyan Guo; Chenchen Mou; Xianjin Yang; Chao Zhou

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arXiv · 2312.06625

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

Jinyan Guo·Chenchen Mou·Xianjin Yang·Chao Zhou

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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.

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BibTeX RIS

Jinyan Guo, Chenchen Mou, Xianjin Yang, Chao Zhou. 2023-12-08. Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes. https://arxiv.org/abs/2312.06625

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