Towards Self-Adaptive Resilient Swarms Using Multi-Agent Reinforcement Learning

Rafael Pina, Varuna De Silva, Corentin Artaud

2024

Abstract

Cooperative swarms of intelligent agents have been used recently in several different fields of application. The ability to have several units working together to accomplish a task can drastically extend the range of challenges that can be solved. However, these swarms are composed of machines that are susceptible to suffering external attacks or even internal failures. In cases where some of the elements of the swarm fail, the others must be capable of adjusting to the malfunctions of the teammates and still achieve the objectives. In this paper, we investigate the impact of possible malfunctions in swarms of cooperative agents through the use of Multi-Agent Reinforcement Learning (MARL). More specifically, we investigate how MARL agents react when one or more teammates start acting abnormally during their training and how that transfers to testing. Our results show that, while common MARL methods might be able to adjust to simple flaws, they do not adapt well when these become more complex. In this sense, we show how independent learners can be used as a potential direction of future research to adapt to malfunctions in swarms using MARL. With this work, we hope to motivate further research to create more robust intelligent swarms using MARL.

Paper Citation

in Harvard Style

Pina R., De Silva V. and Artaud C. (2024). Towards Self-Adaptive Resilient Swarms Using Multi-Agent Reinforcement Learning. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 410-417. DOI: 10.5220/0012462800003654

in Bibtex Style

@conference{icpram24,
author={Rafael Pina and Varuna De Silva and Corentin Artaud},
title={Towards Self-Adaptive Resilient Swarms Using Multi-Agent Reinforcement Learning},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={410-417},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012462800003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Towards Self-Adaptive Resilient Swarms Using Multi-Agent Reinforcement Learning
SN - 978-989-758-684-2
AU - Pina R.
AU - De Silva V.
AU - Artaud C.
PY - 2024
SP - 410
EP - 417
DO - 10.5220/0012462800003654
PB - SciTePress