Machine Learning Architectures for Price Formation Models

Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate training process that relies on min–max characterization of the optimal control and variables. Our main theoretical contribution is development posteriori estimates as tool evaluate convergence process. illustrate our results with numerical experiments for linear dynamics both quadratic non-quadratic