A survey on multi-agent reinforcement learning methods for vehicular networks

Ibrahim Althamary, Chih Wei Huang, Phone Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as mobile smart agents that communicating, cooperating, and competing for resources and information. The task between vehicles is to learn and make decisions depending on the policy to improve the effectiveness of the multi-agent system (MAS) that deals with the continually changing environment. The multi-agent reinforcement learning (MARL) is considered as one of the learning frameworks for finding reliable solutions in a highly dynamic vehicular MAS. In this paper, we provide a survey on research issues related to vehicular networks such as resource allocation, data offloading, cache placement, ultra-reliable low latency communication (URLLC), and high mobility. Furthermore, we show the potential applications of MARL that enables decentralized and scalable decision making in vehicle-to-everything (V2X) scenarios.

Original languageEnglish
Title of host publication2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1154-1159
Number of pages6
ISBN (Electronic)9781538677476
DOIs
StatePublished - Jun 2019
Event15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019 - Tangier, Morocco
Duration: 24 Jun 201928 Jun 2019

Publication series

Name2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019

Conference

Conference15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019
Country/TerritoryMorocco
CityTangier
Period24/06/1928/06/19

Keywords

  • 5G
  • Caching
  • Data Offloading
  • Multi-agent
  • Reinforcement Learning
  • URLLC
  • Vehicular Network

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