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

Ibrahim Althamary, Chih Wei Huang, Phone Lin

研究成果: 書貢獻/報告類型會議論文篇章同行評審

12 引文 斯高帕斯(Scopus)

摘要

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.

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主出版物標題2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1154-1159
頁數6
ISBN(電子)9781538677476
DOIs
出版狀態已出版 - 6月 2019
事件15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019 - Tangier, Morocco
持續時間: 24 6月 201928 6月 2019

出版系列

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

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???event.eventtypes.event.conference???15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019
國家/地區Morocco
城市Tangier
期間24/06/1928/06/19

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