AI RESEARCH
ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization
arXiv CS.LG
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ArXi:2501.17377v4 Announce Type: replace 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.