| Title |
Multi-task Deep Reinforcement Learning for IoT Service Selection |
| ID_Doc |
43792 |
| Authors |
Matsuoka, H; Moustafa, A |
| Title |
Multi-task Deep Reinforcement Learning for IoT Service Selection |
| Year |
2022 |
| Published |
|
| DOI |
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. |
| Author Keywords |
Deep Reinforcement Learning; Service Selection |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000774776400066 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://doi.org/10.5220/0010857800003116
|