Data-Driven Network LQG Mean Field Games with Heterogeneous Populations via Integral Reinforcement Learning
This work develops a data-driven method to solve infinite horizon Linear Quadratic Gaussian Mean Field Games involving many different types of agents whose internal dynamics are unknown. The solution uses Integral Reinforcement Learning combined with Kleinman's iteration to learn network-coupled control strategies directly from observed agent trajectories, bypassing the need for known agent dynamics.