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• W3183269529 abstract "We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean-field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are not approachable with existing solution methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex-concave saddle-point problem. Second, we parameterize the value and density functions by two neural networks, respectively. By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN). We show the potential of our method on up to 100-dimensional MFG problems." @default.
• W3183269529 created "2021-08-02" @default.
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• W3183269529 date "2021-07-30" @default.
• W3183269529 modified "2023-10-17" @default.
• W3183269529 title "Alternating the population and control neural networks to solve high-dimensional stochastic mean-field games" @default.
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• W3183269529 doi "https://doi.org/10.1073/pnas.2024713118" @default.
• W3183269529 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8346838" @default.
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• W3183269529 hasPublicationYear "2021" @default.
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