Linear Quadratic Graphon Field Games
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Abstract (arXiv)
Linear quadratic graphon field games (LQ-GFGs) are defined to be LQ games which involve a large number of agents that are weakly coupled via a weighted undirected graph on which each node represents an agent. The links of the graph correspond to couplings between the agents' dynamics, as well as between the individual cost functions, which each agent attempts to minimize. We formulate limit LQ-GFG problems based on the assumption that these graphs lie in a sequence which converges to a limit graphon. First, under a finite-rank assumption on the limit graphon, the existence and uniqueness of solutions to the formulated limit LQ-GFG problem is established. Second, based upon the solutions to the limit LQ-GFG problem, epsilon-Nash equilibria are constructed for the corresponding game problems with a very large but finite number of players. This result is then generalized to the case with random initial conditions. It is to be noted that LQ-GFG problems are distinct from the class of graphon mean field game (GMFG) problems where a population is hypothesized to be associated with each node of the graph [Caines and Huang CDC 2018, 2019].
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.
5 papers.
- 2022Learning Sparse Graphon Mean Field Gamescited by 14
- 2024Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithmcited by 5
- 2023Learning Regularized Graphon Mean-Field Games with Unknown Graphonscited by 4
- 2023Reinforcement Learning for SBM Graphon Games with Re-Samplingcited by 1
- 2025Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Gamescited by 1
- 2025Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games6 Jun 2025
- 2024Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm8 May 2024
- 2023Learning Regularized Graphon Mean-Field Games with Unknown Graphons26 Oct 2023
- 2023Reinforcement Learning for SBM Graphon Games with Re-Sampling25 Oct 2023
- 2022Learning Sparse Graphon Mean Field Games8 Sep 2022