  Recent years have witnessed the promise that reinforcement learning, coupled
with Graph Neural Network (GNN) architectures, could learn to solve hard
combinatorial optimization problems: given raw input data and an evaluator to
guide the process, the idea is to automatically learn a policy able to return
feasible and high-quality outputs. Recent work have shown promising results but
the latter were mainly evaluated on the travelling salesman problem (TSP) and
similar abstract variants such as Split Delivery Vehicle Routing Problem
(SDVRP). In this paper, we analyze how and whether recent neural architectures
can be applied to graph problems of practical importance. We thus set out to
systematically "transfer" these architectures to the Power and Channel
Allocation Problem (PCAP), which has practical relevance for, e.g., radio
resource allocation in wireless networks. Our experimental results suggest that
existing architectures (i) are still incapable of capturing graph structural
features and (ii) are not suitable for problems where the actions on the graph
change the graph attributes. On a positive note, we show that augmenting the
structural representation of problems with Distance Encoding is a promising
step towards the still-ambitious goal of learning multi-purpose autonomous
solvers.
