ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization 事件

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization arXiv:2501.17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts. To address this issue, we unco