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Model Predictive Mean Field Games for Controlling Multi-Agent Systems
- Publication Year :
- 2020
-
Abstract
- When controlling multi-agent systems, the trade-off between performance and scalability is a major challenge. Here, we address this difficulty by using mean field games (MFGs), which is a framework that deduces the macroscopic dynamics describing the density profile of agents from their microscopic dynamics. To effectively use the MFG, we propose a model predictive MFG (MP-MFG), which estimates the agent population density profile with using kernel density estimation and manages the input generation with model predictive control. The proposed MP-MFG generates control inputs by monitoring the agent population at each time step, and thus achieves higher robustness than the conventional MFG. Numerical results show that the MP-MFG outperforms the MFG when the agent model has modeling errors or the number of agents in the system is small.<br />Comment: This paper has been accepted for 2021 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC2021)
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2004.07994
- Document Type :
- Working Paper