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Reinforcement Learning for Generalized Parameter Optimization in Elastic Optical Networks
Elastic Optical Networking enables efficient use of spectral resources at the cost of a large parameter space which needs to be optimized to maximize transmission bandwidth. By formulating this optimization problem as a Markov Decision Process, we show that by using a state of the art Reinforcement Learning algorithm an agent can be trained, which is able to select near optimal parameters for different link conditions within seconds. Furthermore, the trained agent is able to generalize to unseen conditions, removing the need to optimize and train for every possible link scenario.
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