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Smart city article details

Title	Multi-Task Deep Reinforcement Learning For Iot Service Selection
ID_Doc	38436
Authors	Matsuoka H.; Moustafa A.
Year	2022
Published	International Conference on Agents and Artificial Intelligence, 3
DOI	http://dx.doi.org/10.5220/0010857800003116
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. © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
Author Keywords	Deep Reinforcement Learning; Service Selection

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