Spectrum Continuity and Contiguity Aware State Representation for Deep Reinforcement Learning-Based Routing of EONs

We study the deep reinforcement learning-based routing scheme for elastic optical networks. We claim the importance of proper state representation and propose a state representation with awareness of the spectrum continuity and contiguity constraints. Simulation results on NSFNet show our method outperforms previous approaches by 11.6%.