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Jan, 2024
通过采样不变参数化的平均场博弈无监督解算子学习
Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations
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Han Huang, Rongjie Lai
TL;DR
基于深度学习的新框架通过学习MFG解算符,解决高维度均场博弈问题并具有采样不变性,从而避免了离散化,并减少了创建训练数据集的计算开销。
Abstract
Recent advances in
deep learning
has witnessed many innovative frameworks that solve high dimensional
mean-field games
(MFG) accurately and efficiently. These methods, however, are restricted to solving single-in
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