  Routing is one of the key functions for stable operation of network
infrastructure. Nowadays, the rapid growth of network traffic volume and
changing of service requirements call for more intelligent routing methods than
before. Towards this end, we propose a definition of cognitive routing and an
implementation approach based on Deep Reinforcement Learning (DRL). To
facilitate the research of DRL-based cognitive routing, we introduce a
simulator named RL4Net for DRL-based routing algorithm development and
simulation. Then, we design and implement a DDPG-based routing algorithm. The
simulation results on an example network topology show that the DDPG-based
routing algorithm achieves better performance than OSPF and random weight
algorithms. It demonstrate the preliminary feasibility and potential advantage
of cognitive routing for future network.
