Recent advances in deep learning have introduced numerous innovative frameworks for solving high-dimensional mean-field games (MFGs). However, these methods are often limited to solving single-instance MFGs and require extensive computational time for each instance, presenting challenges for practical applications.
In this talk, I will present our recent work on a novel framework for learning the MFG solution operator using in-context learning. Our model takes MFG instances as input and directly outputs their solutions in a single forward pass, significantly improving computational efficiency. Our method offers two key advantages: (1) it is discretization-free, making it particularly effective for high-dimensional MFGs, and (2) it can be trained without requiring supervised labels, thereby reducing the computational burden of preparing training datasets common in existing operator learning methods. If time permits, I will also discuss a generalization-error analysis on this transformer-based model, which bridges the proposed framework to emerging theory on in-context learning, and highlights its broader implications and avenues for further work.
Copyright. All Rights Reserved.