Multi-Agent Deep Reinforcement Learning-Based Algorithm for Fast Generalization on Routing Problems

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Journal Title
Journal ISSN
Volume Title
Conference article
Date
2021-11
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
228-238
Series
PROCEDIA COMPUTER SCIENCE, Volume 193
Abstract
We propose a fast generalization method for DQN-Routing, an algorithm based on multi-agent deep reinforcement learning that suffers from generalization problem when introduced to new topologies even if it was trained on a similar topology. The proposed method is based on the wisdom of crowds and allows the distributed routing algorithm, DQN-Routing, to generalize better to new topologies that were not seen before during training. The proposed method also aims to decrease the solution search time as the original DQN-Routing algorithm takes a long time to converge, and to increase the overall performance by minimizing the mean delivery time and total power consumption and the number of collisions. The experimental evaluation of our method proved that is capable to generalize to new topologies and outperform the DQN-Routing algorithm.
Description
Funding Information: Acknowledgements. The work was financially supported by the Russian Science Foundation (Project 20-19-00700). Publisher Copyright: © 2021 Elsevier B.V.. All rights reserved.
Keywords
Distributed routing problems, DQN, DQN-Routing, Fast generalization, Multi-agent deep reinforcement learning, Routing problem
Other note
Citation
Barbahan , I , Baikalov , V , Vyatkin , V & Filchenkov , A 2021 , ' Multi-Agent Deep Reinforcement Learning-Based Algorithm for Fast Generalization on Routing Problems ' , PROCEDIA COMPUTER SCIENCE , vol. 193 , pp. 228-238 . https://doi.org/10.1016/j.procs.2021.10.023