Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games

Understand

Multi-agent reinforcement learning, despite its popularity and empirical success, faces significant scalability challenges in large-population dynamic games.

• Graphon mean field games (GMFGs) offer a principled framework for approximating such games while capturing heterogeneity among players.
• In this paper, we propose and analyze a policy optimization framework for continuous-time, finite-horizon linear-quadratic GMFGs.
• Exploiting the structural properties of GMFGs, we design an efficient policy parameterization in which each player's policy is represented as an affine function of their private state, with a shared slope function and player-specific intercepts.