  In this paper we design and evaluate a Deep-Reinforcement Learning agent that
optimizes routing. Our agent adapts automatically to current traffic conditions
and proposes tailored configurations that attempt to minimize the network
delay. Experiments show very promising performance. Moreover, this approach
provides important operational advantages with respect to traditional
optimization algorithms.
