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R2L: Routing With Reinforcement Learning

Reis, Joao; Phan, TK; Kheirkhah, M; Yang, Fang; Griffin, D; rocha, M; Rio, Miguel; (2021) R2L: Routing With Reinforcement Learning. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE: Shenzhen, China. Green open access

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Abstract

In a packet network, the routes taken by traffic can be determined according to predefined objectives. Assuming that the network conditions remain static and the defined objectives do not change, mathematical tools such as linear programming could be used to solve this routing problem. However, networks can be dynamic or the routing requirements may change. In that context, Reinforcement Learning (RL), which can learn to adapt in dynamic conditions and offers flexibility of behavior through the reward function, presents as a suitable tool to find good routing strategies. In this work, we train an RL agent, which we call R2L, to address the routing problem. The policy function used in R2L is a neural network and we use an evolution strategy algorithm to determine its weights and biases. We tested R2L in two different scenarios: static and dynamic networks conditions. In the first, we used a 16-node network and experimented with different reward functions, observing that R2L was able to adapt its routing behavior accordingly. Finally, in the second experiment, we used a 5-node network topology where a given link's transmission rate changed during the simulation. In this scenario, we observed that R2L was able to deliver a competitive performance, compared to heuristic benchmarks, with changing network conditions.

Type: Proceedings paper
Title: R2L: Routing With Reinforcement Learning
Event: 2021 International Joint Conference on Neural Networks (IJCNN)
Location: ELECTR NETWORK
Dates: 18 Jul 2021 - 22 Jul 2021
ISBN-13: 978-1-6654-3900-8
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/IJCNN52387.2021.9533549
Publisher version: https://doi.org/10.1109/IJCNN52387.2021.9533549
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Adaptation models, Network topology, Heuristic algorithms, Neural networks, Reinforcement learning, Tools, Benchmark testing
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10148317
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