Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction 事件

Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction arXiv:2606.00949v1 Announce Type: cross Abstract: We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbulent flows. Taking as a baseline the results of training agents directly targeting wall-shear stress and opposition control, three SHAP-guided approaches are compared. In the first, the r