We develop a Deep Reinforcement Learning (DRL) agent for the RMSA problem and improve the Shapley Value for Explaining Reinforcement Learning (SVERL) explainability framework by integrating policy sensitivity and feature interdependence for the RMSA problem. We then explain the proactive rejection of lightpath requests.
Beyond Performance: Explaining Non-Intuitive Deep Reinforcement Learning Actions in Elastic Optical Networks
Asdikian, Jean Pierre;Maier, Guido;Troia, Sebastian;Ayoub, Omran
2025-01-01
Abstract
We develop a Deep Reinforcement Learning (DRL) agent for the RMSA problem and improve the Shapley Value for Explaining Reinforcement Learning (SVERL) explainability framework by integrating policy sensitivity and feature interdependence for the RMSA problem. We then explain the proactive rejection of lightpath requests.File in questo prodotto:
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