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Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

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....

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Main Authors:	Tianxu, Li, Kun, Zhu, Nguyen, Cong Luong, Dusit, Niyato, Qihui, Wu, Yang, Zhang, Bing, Chen
Format:	Bài trích
Language:	English
Published:	IEEE 2022
Subjects:	Multi-Agent Reinforcement Learning Future Internet
Online Access:	https://ieeexplore.ieee.org/document/9738819 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5759 https://doi.org/10.1109/COMST.2022.3160697
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spelling	oai:localhost:PNK-57592022-08-17T05:54:53Z Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey Tianxu, Li Kun, Zhu Nguyen, Cong Luong Dusit, Niyato Qihui, Wu Yang, Zhang Bing, Chen Multi-Agent Reinforcement Learning Future Internet 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. 2022-05-05T07:26:20Z 2022-05-05T07:26:20Z 2022 Bài trích https://ieeexplore.ieee.org/document/9738819 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5759 https://doi.org/10.1109/COMST.2022.3160697 en IEEE
institution	Digital Phenikaa
collection	Digital Phenikaa
language	English
topic	Multi-Agent Reinforcement Learning Future Internet
spellingShingle	Multi-Agent Reinforcement Learning Future Internet Tianxu, Li Kun, Zhu Nguyen, Cong Luong Dusit, Niyato Qihui, Wu Yang, Zhang Bing, Chen Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
description	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.
format	Bài trích
author	Tianxu, Li Kun, Zhu Nguyen, Cong Luong Dusit, Niyato Qihui, Wu Yang, Zhang Bing, Chen
author_facet	Tianxu, Li Kun, Zhu Nguyen, Cong Luong Dusit, Niyato Qihui, Wu Yang, Zhang Bing, Chen
author_sort	Tianxu, Li
title	Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
title_short	Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
title_full	Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
title_fullStr	Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
title_full_unstemmed	Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
title_sort	applications of multi-agent reinforcement learning in future internet: a comprehensive survey
publisher	IEEE
publishDate	2022
url	https://ieeexplore.ieee.org/document/9738819 https://dlib.phenikaa-uni.edu.vn/handle/PNK/5759 https://doi.org/10.1109/COMST.2022.3160697
_version_	1751856314683752448
score	8.891053

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

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