Multi-task Deep Reinforcement Learning for IoT Service Selection

Hiroki Matsuoka, Ahmed Moustafa

2022

Abstract

Reinforcement learning has emerged as a powerful paradigm for sequential decision making. By using reinforcement learning, intelligent agents can learn to adapt to the dynamics of uncertain environments. In recent years, several approaches using the RL decision-making paradigm have been proposed for IoT service selection in smart city environments. However, most of these approaches rely only on one criterion to select among the available services. These approaches fail in environments where services need to be selected based on multiple decision-making criteria. The vision of this research is to apply multi-task deep reinforcement learning, specifically (IMPALA architecture), to facilitate multi-criteria IoT service selection in smart city environments. We will also conduct its experiments to evaluate and discuss its performance.

Paper Citation

in Harvard Style

Matsuoka H. and Moustafa A. (2022). Multi-task Deep Reinforcement Learning for IoT Service Selection. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 548-554. DOI: 10.5220/0010857800003116

in Bibtex Style

@conference{icaart22,
author={Hiroki Matsuoka and Ahmed Moustafa},
title={Multi-task Deep Reinforcement Learning for IoT Service Selection},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={548-554},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010857800003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Multi-task Deep Reinforcement Learning for IoT Service Selection
SN - 978-989-758-547-0
AU - Matsuoka H.
AU - Moustafa A.
PY - 2022
SP - 548
EP - 554
DO - 10.5220/0010857800003116