Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations

arXiv:2401.15482v2 Announce Type: replace
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-instance MFG and demands extensive computational time per instance, limiting practicality. To overcome this, we develop a novel framework to learn the MFG solution operator. Our model takes a MFG instances as input and output their solutions with one forward pass. To ensure the proposed parametrization is well-suited …

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