  The digital transformation is pushing the existing network technologies
towards new horizons, enabling new applications (e.g., vehicular networks). As
a result, the networking community has seen a noticeable increase in the
requirements of emerging network applications. One main open challenge is the
need to accommodate control systems to highly dynamic network scenarios.
Nowadays, existing network optimization technologies do not meet the needed
requirements to effectively operate in real time. Some of them are based on
hand-crafted heuristics with limited performance and adaptability, while some
technologies use optimizers which are often too time-consuming. Recent advances
in Deep Reinforcement Learning (DRL) have shown a dramatic improvement in
decision-making and automated control problems. Consequently, DRL represents a
promising technique to efficiently solve a variety of relevant network
optimization problems, such as online routing. In this paper, we explore the
use of state-of-the-art DRL technologies for real-time routing optimization and
outline some relevant open challenges to achieve production-ready DRL-based
solutions.
