  We explore the feasibility of combining Graph Neural Network-based policy
architectures with Deep Reinforcement Learning as an approach to problems in
systems. This fits particularly well with operations on networks, which
naturally take the form of graphs. As a case study, we take the idea of
data-driven routing in intradomain traffic engineering, whereby the routing of
data in a network can be managed taking into account the data itself. The
particular subproblem which we examine is minimising link congestion in
networks using knowledge of historic traffic flows. We show through experiments
that an approach using Graph Neural Networks (GNNs) performs at least as well
as previous work using Multilayer Perceptron architectures. GNNs have the added
benefit that they allow for the generalisation of trained agents to different
network topologies with no extra work. Furthermore, we believe that this
technique is applicable to a far wider selection of problems in systems
research.
