Mean Field Game GAN

We propose a novel mean field games (MFGs) based GAN(generative adversarial network) framework. To be specific, we utilize the Hopf formula in density space to rewrite MFGs as a primal-dual problem so that we are able to train the model via neural networks and samples. Our model is flexible due to t...

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Bibliographic Details
Published inarXiv.org
Main Authors Ma, Shaojun, Zhou, Haomin, Zha, Hongyuan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.03.2021
Subjects
Neural networks
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Summary:We propose a novel mean field games (MFGs) based GAN(generative adversarial network) framework. To be specific, we utilize the Hopf formula in density space to rewrite MFGs as a primal-dual problem so that we are able to train the model via neural networks and samples. Our model is flexible due to the freedom of choosing various functionals within the Hopf formula. Moreover, our formulation mathematically avoids Lipschitz-1 constraint. The correctness and efficiency of our method are validated through several experiments.
ISSN:2331-8422