mlsanityData to 14 Oct 2026

Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry

arXiv 2406.12602 ↗First posted 18 Jun 2024cs.NI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 11 references that are in the map. First posted on arXiv 18 Jun 2024.

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1 of 11 references.

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  1. 2022From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANscited by 3

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