Learning algorithms for mean field optimal control

H. Mete Soner; Josef Teichmann; Qinxin Yan

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arXiv · 2503.17869

Learning algorithms for mean field optimal control

H. Mete Soner·Josef Teichmann·Qinxin Yan

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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 $N$-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.

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H. Mete Soner, Josef Teichmann, Qinxin Yan. 2025-03-22. Learning algorithms for mean field optimal control. https://arxiv.org/abs/2503.17869

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