Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry
<|reference_start|>Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry: This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.<|reference_end|>
@article{navarro2024reinforcement-learning,
    title={Reinforcement-Learning based routing for packet-optical networks with
  hybrid telemetry},
    author={A. L. Garc'ia Navarro, Nataliia Koneva, Alfonso S'anchez-Maci'an,
  Jos'e Alberto Hern'andez, 'Oscar Gonz'alez de Dios, J. M. Rivas-Moscoso},
    journal={The 28th International Conference on Optical Network Design and
  Modelling (ONDM 2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12602},
    primaryClass={cs.NI cs.LG}
}