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Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks.
- Source :
-
IEEE Transactions on Vehicular Technology . Nov2020, Vol. 69 Issue 11, p14055-14060. 6p. - Publication Year :
- 2020
-
Abstract
- This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 69
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 147041792
- Full Text :
- https://doi.org/10.1109/TVT.2020.3029609