Hiển thị ở chế độ nhân viên: Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

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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Tác giả chính:	Tianxu, Li, Kun, Zhu, Nguyen, Cong Luong, Dusit, Niyato
Định dạng:	Bài trích
Ngôn ngữ:	English
Nhà xuất bản:	IEEE Transactions on Magnetics 2022
Chủ đề:	Multi-agent reinforcement learning Future Internet
Truy cập trực tuyến:	https://ieeexplore.ieee.org/document/9738819/keywords#keywords https://dlib.phenikaa-uni.edu.vn/handle/PNK/5902 https://doi.org/10.1109/COMST.2022.3160697
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spelling	oai:localhost:PNK-59022022-08-17T05:54:55Z Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey Tianxu, Li Kun, Zhu Nguyen, Cong Luong Dusit, Niyato 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. 2022-07-13T01:59:52Z 2022-07-13T01:59:52Z 2022 Bài trích https://ieeexplore.ieee.org/document/9738819/keywords#keywords https://dlib.phenikaa-uni.edu.vn/handle/PNK/5902 https://doi.org/10.1109/COMST.2022.3160697 en IEEE Transactions on Magnetics
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 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
author_facet	Tianxu, Li Kun, Zhu Nguyen, Cong Luong Dusit, Niyato
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 Transactions on Magnetics
publishDate	2022
url	https://ieeexplore.ieee.org/document/9738819/keywords#keywords https://dlib.phenikaa-uni.edu.vn/handle/PNK/5902 https://doi.org/10.1109/COMST.2022.3160697
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score	8.887836

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

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