A Reinforcement Learning Framework for Parameter Optimization in Elastic Optical Networks

Autor:	Wolfgang Schairer, Bernd Sommerkorn-Krombholz, Bernhard Spinnler, Sebastian Kuhl, Stephan Pachnicke, Rui Manuel Morais, Rebekka Weixer
Rok vydání:	2020
Předmět:	Prediction algorithms Computer science Generalization Bit rate Genetic algorithm Reinforcement learning Transmission system Algorithm
Zdroj:	ECOC
DOI:	10.1109/ecoc48923.2020.9333298
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7d18a3d37ce0d4d969c3281b3188cb81 https://doi.org/10.1109/ecoc48923.2020.9333298 Zobrazit plný text záznamu