mlsanityData to 14 Oct 2026

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

arXiv 2602.14339 ↗First posted 15 Feb 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

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.

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.

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11 of 28 references.

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  1. 2024Mean Field LQG Social Optimization: A Reinforcement Learning Approach19 Oct

Other references · 10

  1. 2020Unified Reinforcement Q-Learning for Mean Field Game and Control Problemscited by 49
  2. 2019Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methodscited by 28
  3. 2019Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Gamescited by 19
  4. 2020Stochastic Linear Quadratic Optimal Control Problem: A Reinforcement Learning Methodcited by 11
  5. 2020LQG Graphon Mean Field Games: Analysis via Graphon Invariant Subspacescited by 4
  6. 2019Linear quadratic mean field games: Asymptotic solvability and relation to the fixed point approachcited by 3
  7. 2020Approximate Equilibrium Computation for Discrete-Time Linear-Quadratic Mean-Field Gamescited by 2
  8. 2024Two system transformation data-driven algorithms for linear quadratic mean-field gamescited by 1
  9. 2024Linear Quadratic Mean Field Games with Quantile-Dependent Cost Coefficientscited by 1
  10. 2025Data-Driven Mean Field Equilibrium Computation in Large-Population LQG Gamescited by 1

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  1. 2025Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games
  2. 2025Reinforcement Learning for Discrete-time LQG Mean Field Social Control Problems with Unknown Dynamics
  3. 2025Robust Mean Field Social Control: A Unified Reinforcement Learning Framework
  4. 2026Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
  5. 2024Efficient and Scalable Deep Reinforcement Learning for Mean Field Control Games
  6. 2024Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective
  7. 2025Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
  8. 2024Unified continuous-time q-learning for mean-field game and mean-field control problems