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

Ibrahim Althamary; Chih Wei Huang; Phone Lin

doi:10.1109/IWCMC.2019.8766739

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

Ibrahim Althamary, Chih Wei Huang, Phone Lin

Department of Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

48 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 language	English
Title of host publication	2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1154-1159
Number of pages	6
ISBN (Electronic)	9781538677476
DOIs	https://doi.org/10.1109/IWCMC.2019.8766739
State	Published - Jun 2019
Event	15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019 - Tangier, Morocco Duration: 24 Jun 2019 → 28 Jun 2019

Publication series

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

Conference

Conference	15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019
Country/Territory	Morocco
City	Tangier
Period	24/06/19 → 28/06/19

Keywords

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

Access to Document

10.1109/IWCMC.2019.8766739

Deep Reinforcement Learning for Mobile Traffic Offloading
Huang, C.-W. (PI)
1/08/18 → 31/07/19
Project: Research

Cite this

Althamary, I., Huang, C. W., & Lin, P. (2019). A survey on multi-agent reinforcement learning methods for vehicular networks. In 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019 (pp. 1154-1159). Article 8766739 (2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWCMC.2019.8766739

Althamary, Ibrahim ; Huang, Chih Wei ; Lin, Phone. / A survey on multi-agent reinforcement learning methods for vehicular networks. 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1154-1159 (2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019).

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keywords = "5G, Caching, Data Offloading, Multi-agent, Reinforcement Learning, URLLC, Vehicular Network",

author = "Ibrahim Althamary and Huang, {Chih Wei} and Phone Lin",

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Althamary, I, Huang, CW & Lin, P 2019, A survey on multi-agent reinforcement learning methods for vehicular networks. in 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019., 8766739, 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1154-1159, 15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019, Tangier, Morocco, 24/06/19. https://doi.org/10.1109/IWCMC.2019.8766739

A survey on multi-agent reinforcement learning methods for vehicular networks. / Althamary, Ibrahim; Huang, Chih Wei; Lin, Phone.
2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1154-1159 8766739 (2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Huang, Chih Wei

AU - Lin, Phone

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AB - 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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KW - Caching

KW - Data Offloading

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Althamary I, Huang CW, Lin P. A survey on multi-agent reinforcement learning methods for vehicular networks. In 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1154-1159. 8766739. (2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019). doi: 10.1109/IWCMC.2019.8766739

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

Abstract

Publication series

Conference

Keywords

Access to Document

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Projects

Deep Reinforcement Learning for Mobile Traffic Offloading

Cite this