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Recent years have witnessed the promise that reinforcement learning, coupledwith Graph Neural Network (GNN) architectures, could learn to solve hardcombinatorial optimization problems: given raw input data and an evaluator toguide the process, the idea is to automatically learn a policy able to returnfeasible and high-quality outputs. Recent work have shown promising results butthe latter were mainly evaluated on the travelling salesman problem (TSP) andsimilar abstract variants such as Split Delivery Vehicle Routing Problem(SDVRP). In this paper, we analyze how and whether recent neural architecturescan be applied to graph problems of practical importance. We thus set out tosystematically "transfer" these architectures to the Power and ChannelAllocation Problem (PCAP), which has practical relevance for, e.g., radioresource allocation in wireless networks. Our experimental results suggest thatexisting architectures (i) are still incapable of capturing graph structuralfeatures and (ii) are not suitable for problems where the actions on the graphchange the graph attributes. On a positive note, we show that augmenting thestructural representation of problems with Distance Encoding is a promisingstep towards the still-ambitious goal of learning multi-purpose autonomoussolvers.

Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure