  Deep Reinforcement Learning (DRL) has shown a dramatic improvement in
decision-making and automated control problems. Consequently, DRL represents a
promising technique to efficiently solve many relevant optimization problems
(e.g., routing) in self-driving networks. However, existing DRL-based solutions
applied to networking fail to generalize, which means that they are not able to
operate properly when applied to network topologies not observed during
training. This lack of generalization capability significantly hinders the
deployment of DRL technologies in production networks. This is because
state-of-the-art DRL-based networking solutions use standard neural networks
(e.g., fully connected, convolutional), which are not suited to learn from
information structured as graphs.
  In this paper, we integrate Graph Neural Networks (GNN) into DRL agents and
we design a problem specific action space to enable generalization. GNNs are
Deep Learning models inherently designed to generalize over graphs of different
sizes and structures. This allows the proposed GNN-based DRL agent to learn and
generalize over arbitrary network topologies. We test our DRL+GNN agent in a
routing optimization use case in optical networks and evaluate it on 180 and
232 unseen synthetic and real-world network topologies respectively. The
results show that the DRL+GNN agent is able to outperform state-of-the-art
solutions in topologies never seen during training.
