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We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean field games (MFGs).
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Algorithms for overcoming the curse of dimensionality for certain hamilton–jacobi equations arising in control theory and elsewhere
Jérôme Darbon and Stanley Osher · 2016
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Mean-field control for improving energy efficiency
Sisi Li, Shengbo Eben Li, and Kun Deng · 2016
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Income and wealth distribution in macroeconomics: A continuous-time approach
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Algorithm for overcoming the curse of dimensionality for time-dependent non-convex hamilton–jacobi equations arising from optimal control and differential games problems
Yat Tin Chow, Jérôme Darbon, Stanley Osher, and Wotao Yin · 2017
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An optimal execution problem in finance targeting the market trading speed: An mfg formulation
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Algorithm for overcoming the curse of dimensionality for certain non-convex hamilton–jacobi equations, projections and differential games
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The variational structure and time-periodic solutions for mean-field games systems
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Solving large-scale optimization problems with a convergence rate independent of grid size
Matt Jacobs, Flavien Léger, Wuchen Li, and Stanley Osher · 2019
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An integral control formulation of mean field game based large scale coordination of loads in smart grids
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Fluid flow mass transport for generative networks
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Computational optimal transport
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Deep neural networks motivated by partial differential equations
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Wasserstein proximal of gans
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