Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163303
Title: Applications of multi-agent reinforcement learning in future internet: a comprehensive survey
Authors: Li, Tianxu
Zhu, Kun
Nguyen Cong Luong
Niyato, Dusit
Wu, Qihui
Zhang, Yang
Chen, Bing
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Li, T., Zhu, K., Nguyen Cong Luong, Niyato, D., Wu, Q., Zhang, Y. & Chen, B. (2022). Applications of multi-agent reinforcement learning in future internet: a comprehensive survey. IEEE Communications Surveys and Tutorials, 24(2), 1240-1279. https://dx.doi.org/10.1109/COMST.2022.3160697
Journal: IEEE Communications Surveys and Tutorials
Abstract: Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, the future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments but also other entities' policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next-generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in the future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues. Finally, we discuss the challenges, open issues, and future directions related to the applications of MARL in the future Internet.
URI: https://hdl.handle.net/10356/163303
ISSN: 1553-877X
DOI: 10.1109/COMST.2022.3160697
Schools: School of Computer Science and Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

SCOPUSTM   
Citations 10

47
Updated on May 15, 2024

Web of ScienceTM
Citations 20

15
Updated on Oct 31, 2023

Page view(s)

106
Updated on May 16, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.