High-Generalizability Reinforcement Learning Based Capacity Optimization in WDM Long-Haul Networks

We propose a fast machine learning assisted capacity optimization in a point-to-point link of an elastic optical network using a reinforcement learning (RL) algorithm. We demonstrate a RL agent trained on idealized parameters in simulation, and compare the results with classical optimization algorithms such as a genetic algorithm and a simple power sweep. Furthermore, we present the high generalization ability of the RL agent under varying link conditions. Experimental results show that for 8 DWDM channels over nine 50km spans of True Wave Classic NZDSF with a span loss of 12 dB the RL agent is able to cope with these unseen conditions, removing the need to optimize for every possible link scenario during training.