A Reinforcement Learning Framework for Parameter Optimization in Elastic Optical Networks

摘要

We present a reinforcement learning (RL) framework for maximizing the total capacity of a 51-channel transmission system, which runs magnitudes faster than a genetic algorithm (GA) based optimization. The generalization capabilities and performance of the RL framework are compared to results obtained with a GA.