Partially Centralized Model-Predictive Mean Field Games for controlling multi-agent systems

Abstract

Fast, high-performance control of large Multi-Agent Systems (MASs) is a major challenge. We address this problem using Mean Field Games (MFGs), which deduce the macroscopic dynamics of the density distribution of the agent population from the microscopic dynamics of each agent. To control MASs using the MFG, two main problems need to be solved: preventing control performance degradation caused by distribution estimation errors and ensuring the scalability of communication. To overcome these issues, we develop a new control method called the Partially Centralized Model-Predictive MFG (PCMP-MFG). The proposed method solves the first issue by repeating distribution estimation and model-predictive control at each time; it solves the second by introducing broadcast control. Our numerical results show that the proposed PCMP-MFG method outperforms the conventional MFG-based method in a wide parameter range.