Latency-Aware Routing and Spectrum Assignment with Deep Reinforcement Learning

This paper evaluates different aspects of the performance of a solution for routing and spectrum allocation in elastic optical networks. The evaluation includes the experimentation under different traffic loads and the use of a previously trained deep reinforcement learning (DRL) agent. The obtained results show that the DRL agent further outperforms a traditional algorithm as network resources become scarce. Moreover, the results show the proper operation of the pre-trained agent when provisioning lightpaths.