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Routing in Elastic Optical Networks Based on Deep Reinforcement Learning for Multi-Agent Systems

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Reinforcement Learning (RL) has become a valuable strategy in artificial intelligence and it has showed some success in real-world scenarios. Nonetheless, most of the progress achieved in research is often hard to harness in real-world systems given the theoretical assumptions, which are rarely aligned with practical settings. This work focuses on the assignment of resources within elastic optical networks, as a solution to their ever-increasing traffic. This document performs an assessment of two multi-agent reinforcement learning algorithms to solve Routing, Modulation, Spectrum, and Core Assignment (RMSCA), seeking to optimize availability for resource assignment over a network topology and increase the overall capacity, while considering the variability of traffic-related demands. Simulations are carried out in a 14-node topology. The results evidence a 50% increase in spectral efficiency and a blocking probability below 10%. After the system training process, low latency, high speed, and high availability are ensured, thus improving quality for the end user.
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Blocking Probability; Deep Q-Network; Elastic Optical Network; Frequency Slot Unit; Multi-Agent Reinforcement Learning; Reinforcement Learning

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