Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance
This is an incremental review that synthesizes existing methods for applying deep learning to mean field games in financial contexts.
The paper reviews the application of deep learning to solve mean field games and mean field control problems, particularly in finance, addressing large-scale, high-dimensional games with complex structures.
Financial markets and more generally macro-economic models involve a large number of individuals interacting through variables such as prices resulting from the aggregate behavior of all the agents. Mean field games have been introduced to study Nash equilibria for such problems in the limit when the number of players is infinite. The theory has been extensively developed in the past decade, using both analytical and probabilistic tools, and a wide range of applications have been discovered, from economics to crowd motion. More recently the interaction with machine learning has attracted a growing interest. This aspect is particularly relevant to solve very large games with complex structures, in high dimension or with common sources of randomness. In this chapter, we review the literature on the interplay between mean field games and deep learning, with a focus on three families of methods. A special emphasis is given to financial applications.