Data-Driven Network LQG Mean Field Games with Heterogeneous Populations via Integral Reinforcement Learning
Abstract
This paper establishes a data-driven solution for infinite-horizon linear quadratic Gaussian Mean Field Games with network-coupled heterogeneous agent populations where the dynamics of the agents are unknown. The solution technique relies on Integral Reinforcement Learning and Kleinman’s iteration for solving algebraic Riccati equations (ARE). The resulting algorithm uses trajectory data to generate network-coupled MFG strategies for agents and does not require parameters of agents’ dynamics. Under technical conditions on the persistency of excitation and on the existence of unique stabilizing solution to the corresponding AREs, the learned network-coupled MFG strategies are shown to converge to their true values.
Fields of Study & Subjects
Adaptive Dynamic Programming Control
Stochastic processes and financial applications
Reinforcement Learning in Robotics
Publication details
- Journal / source
- IFAC-PapersOnLine
- Volume
- 60
- Issue
- 1
- Pages
- 193–198
- Publication date
- 1 January 2026
- Language
- EN