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Shu-Tong Ding

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#machine learning Preprint Sep 2026

QAMM: Adjoint MeanFlow Matching for Few-Step Offline Reinforcement Learning

Flow policies can model rich action distributions, but their iterative sampling limits decision speed. Adjoint matching uses the critic's action gradient to improve a flow policy without backpropagating through its sampling trajectory, yet its supervision is defined for instantaneous velocities. We propose QAMM, a meth...

Yu Gong, Shu-Tong Ding, Mo-Kai Pan et al. · 0 citations
Preprint Aug 2026

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling

The rapid proliferation of Electric Vehicles (EVs) introduces significant spatio-temporal uncertainties into power grids, while Vehicle-to-Grid (V2G) technology offers critical flexibility through bidirectional power flow. However, integrating large-scale EVs into the Optimal Power Flow framework presents substantial c...

Haoyu Yan, Shutong Ding, Jiebao Zhang et al. · 0 citations

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