The Impact of Dataset on Offline Reinforcement Learning of Multiple UAVs for Flying Ad-hoc Network Formation 


Vol. 49,  No. 7, pp. 1002-1011, Jul.  2024
10.7840/kics.2024.49.7.1002


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  Abstract

A Flying Ad-hoc Network (FANET) is a network of airborne mobile nodes that can communicate independently, and can be utilized as a fallback network during a crisis, such as a disaster or war, when existing infrastructures are damaged. Several Unmanned Aerial Vehicles (UAVs) with communication and routing capabilities can travel to areas where communication is not possible and establish a FANET. In this study, we consider a scenario where multiple UAVs trained through offline reinforcement learning establish a FANET without a centralized control. We conducted experiments to compare the performance of multi-agent’s network construction by datasets and offline reinforcement learning algorithms, and analyzed the learning aspects that depend on the features of datasets and algorithms.

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  Cite this article

[IEEE Style]

D. Lee and M. Kwon, "The Impact of Dataset on Offline Reinforcement Learning of Multiple UAVs for Flying Ad-hoc Network Formation," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 7, pp. 1002-1011, 2024. DOI: 10.7840/kics.2024.49.7.1002.

[ACM Style]

Dongsu Lee and Minhae Kwon. 2024. The Impact of Dataset on Offline Reinforcement Learning of Multiple UAVs for Flying Ad-hoc Network Formation. The Journal of Korean Institute of Communications and Information Sciences, 49, 7, (2024), 1002-1011. DOI: 10.7840/kics.2024.49.7.1002.

[KICS Style]

Dongsu Lee and Minhae Kwon, "The Impact of Dataset on Offline Reinforcement Learning of Multiple UAVs for Flying Ad-hoc Network Formation," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 7, pp. 1002-1011, 7. 2024. (https://doi.org/10.7840/kics.2024.49.7.1002)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880