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A Sample-Wise Adjoint Regression Framework for Mean-Field Control with Connections to Adjoint Matching
This work proposes a novel numerical approach for solving mean-field control (MFC) problems using an adjoint-based optimization framework motivated by the stochastic maximum principle (SMP). Rather than solving the adjoint processes $(Y_t,Z_t)$ explicitly, we construct sample-wise unbiased estimator
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