Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong, Nguyen Cong; Hoang, Dinh Thai; Gong, Shimin; Niyato, Dusit; Wang, Ping; Liang, Ying‐Chang; Kim, Dong In

doi:10.1109/comst.2019.2916583

articleIEEE Communications Surveys & TutorialsJan 1, 2019Closed access

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

NCNguyen Cong Luong DTDinh Thai Hoang SGShimin Gong DNDusit Niyato PWPing Wang

Nanyang Technological University · University of Technology Sydney · +4 more institutions

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Abstract

This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually…

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Topics

Keywords

Reinforcement learning
Computer science
Wireless network
Telecommunications network
State (computer science)
Distributed computing
Computer network
Wireless

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