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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
Format: Bài trích
Language:English
Published: IEEE Transactions on Magnetics 2022
Subjects:
Multi-agent reinforcement learning
Future Internet
Online Access: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
_version_ 1751856287201624064
score 8.891053

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