Deep Reinforcement Learning for Internet of Things : A Comprehensive Survey

Wuhui CHEN, Xiaoyu QIU, Ting CAI, Hong Ning DAI, Zibin ZHENG, Yan ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review


The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in communication, computing, caching and control (4Cs) problems. The recent advances in deep reinforcement learning (DRL) algorithms can potentially address the above problems of IoT systems. In this context, this paper provides a comprehensive survey that overviews DRL algorithms and discusses DRL-enabled IoT applications. In particular, we first briefly review the state-of-the-art DRL algorithms and present a comprehensive analysis on their advantages and challenges. We then discuss on applying DRL algorithms to a wide variety of IoT applications including smart grid, intelligent transportation systems, industrial IoT applications, mobile crowdsensing, and blockchain-empowered IoT. Meanwhile, the discussion of each IoT application domain is accompanied by an in-depth summary and comparison of DRL algorithms. Moreover, we highlight emerging challenges and outline future research directions in driving the further success of DRL in IoT applications.

Original languageEnglish
Article number9403369
Pages (from-to)1659-1692
Number of pages34
JournalIEEE Communications Surveys and Tutorials
Issue number3
Early online date13 Apr 2021
Publication statusPublished - Sep 2021
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received October 7, 2020; revised February 27, 2021; accepted April 5, 2021. Date of publication April 13, 2021; date of current version August 23, 2021. This work was supported in part by the National Key Research and Development Plan under Grant 2018YFB1003800; in part by the Macao Science and Technology Development Fund through Macao Funding Scheme for Key Research and Development Projects under Grant 0025/2019/AKP; in part by the National Natural Science Foundation of China under Grant 61802450; in part by the Natural Science Foundation of Guangdong under Grant 2018A030313005; in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X355; and in part by the Pearl River Talent Recruitment Program under Grant 2019QN01X130. (Corresponding authors: Zibin Zheng; Hong-Ning Dai.) Wuhui Chen, Xiaoyu Qiu, Ting Cai, and Zibin Zheng are with the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China, and also with the National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou 510006, China (e-mail:;;;

Publisher Copyright:
© 1998-2012 IEEE.


  • Cloud computing
  • decision making
  • Deep reinforcement learning
  • Internet of Things
  • Real-time systems
  • Reinforcement learning
  • resource allocation.
  • Security
  • Servers
  • Smart grids


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