Bridging behavior cloning and policy gradient methods to simplify training processes in neural combinatorial optimization.
The proposed Instance-Conditioned Adaptation Model (ICAM) effectively integrates instance-conditioned information, such as problem scale and node-to-node distances, into both the encoding and decoding processes of a neural combinatorial optimization model, enabling it to achieve state-of-the-art performance on large-scale Traveling Salesman Problem and Capacitated Vehicle Routing Problem instances.