Reinforcement Learning versus Swarm Intelligence for Autonomous Multi-HAPS Coordination

Anicho, Ogbonnaya and Charlesworth, Philip B and Baicher, Gurvinder S and Nagar, Atulya K. (2021) Reinforcement Learning versus Swarm Intelligence for Autonomous Multi-HAPS Coordination. Springer Nature Applied Sciences (SNAS), 3. ISSN 2523-3971

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This work analyses the performance of Reinforcement Learning
(RL) versus Swarm Intelligence (SI) for coordinating multiple unmanned High Altitude Platform Stations (HAPS) for communications area coverage. It builds upon previous work which looked at various elements of both algorithms. The
main aim of this paper is to address the continuous state space challenge within this work by using partitioning to manage the high dimensionality problem. This enabled comparing the performance of the classical cases of both RL and SI establishing a baseline for future comparisons of improved versions. From previous work, SI was observed to perform better across various key performance indicators. However, after tuning parameters and empirically choosing
suitable partitioning ratio for the RL state space, it was observed that the SI algorithm still maintained superior coordination capability by achieving higher mean overall user coverage (about 20% better than the RL algorithm), in addition to faster convergence rates. Though the RL technique showed better average peak user coverage, the unpredictable coverage dips was a key weakness, making SI a more suitable algorithm within the context of this work.

Item Type: Article
Additional Information and Comments: Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit The published version is available at:
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Ogbonnaya Anicho
Date Deposited: 02 Jun 2021 13:32
Last Modified: 02 Jun 2021 13:32

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