Learning algorithms for mean field optimal control
Optimization and Control
2025-03-25 v1
Abstract
We analyze an algorithm to numerically solve the mean-field optimal control problems by approximating the optimal feedback controls using neural networks with problem specific architectures. We approximate the model by an -particle system and leverage the exchangeability of the particles to obtain substantial computational efficiency. In addition to several numerical examples, a convergence analysis is provided. We also developed a universal approximation theorem on Wasserstein spaces.
Cite
@article{arxiv.2503.17869,
title = {Learning algorithms for mean field optimal control},
author = {H. Mete Soner and Josef Teichmann and Qinxin Yan},
journal= {arXiv preprint arXiv:2503.17869},
year = {2025}
}