Graphon Mean Field

Graphon mean field theory combines graphon models of network structure with mean field game theory to analyze large-scale multi-agent systems with heterogeneous interactions. Current research focuses on developing efficient algorithms, such as online learning methods and policy gradient reinforcement learning, to compute approximate Nash equilibria in these complex systems, often employing discrete-time formulations and representative player approaches to manage computational complexity. This framework offers a powerful tool for approximating solutions to otherwise intractable problems in areas like cooperative multi-agent reinforcement learning, improving scalability and providing insights into the emergent behavior of large networks.