# Data-Driven Network LQG Mean Field Games with Heterogeneous Populations via Integral Reinforcement Learning

arXiv: https://arxiv.org/abs/2602.14339 · HTML: https://mlsanity.com/paper/2602.14339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 15 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 11 of 11)

- [Unified Reinforcement Q-Learning for Mean Field Game and Control Problems](https://mlsanity.com/paper/2006.13912/) (2020)
- [Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods](https://mlsanity.com/paper/1910.04295/) (2019)
- [Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games](https://mlsanity.com/paper/1910.07498/) (2019)
- [Stochastic Linear Quadratic Optimal Control Problem: A Reinforcement Learning Method](https://mlsanity.com/paper/2011.14516/) (2020)
- [LQG Graphon Mean Field Games: Analysis via Graphon Invariant Subspaces](https://mlsanity.com/paper/2004.00679/) (2020)
- [Mean Field LQG Social Optimization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2410.15119/) (2024)
- [Linear quadratic mean field games: Asymptotic solvability and relation to the fixed point approach](https://mlsanity.com/paper/1903.08776/) (2019)
- [Approximate Equilibrium Computation for Discrete-Time Linear-Quadratic Mean-Field Games](https://mlsanity.com/paper/2003.13195/) (2020)
- [Two system transformation data-driven algorithms for linear quadratic mean-field games](https://mlsanity.com/paper/2404.10285/) (2024)
- [Linear Quadratic Mean Field Games with Quantile-Dependent Cost Coefficients](https://mlsanity.com/paper/2411.01668/) (2024)
- [Data-Driven Mean Field Equilibrium Computation in Large-Population LQG Games](https://mlsanity.com/paper/2502.19993/) (2025)

## Abstract (arXiv)

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.