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New York University Shanghai
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Neural networks can now learn the hidden parameters of mean field games from population dynamics, unlocking new avenues for modeling complex strategic interactions.
Robust $Q$-learning can outperform idealized Bellman iterations in mean-field control scenarios plagued by common-noise uncertainties.
