Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks

Sangwon Hwang, Hanjin Kim, Hoon Lee, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number9217951
Pages (from-to)14055-14060
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Volume69
Issue number11
DOIs
Publication statusPublished - 2020 Nov

Keywords

  • actor-critic method
  • multi-agent deep reinforcement learning
  • Wireless powered communication networks

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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