Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

Wuhui Chen, Xiaoyu Qiu, Ting Cai, Hong Ning Dai*, Zibin Zheng*, Yan Zhang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

128 Citations (Scopus)


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
Pages (from-to)1659-1692
Number of pages34
JournalIEEE Communications Surveys and Tutorials
Issue number3
Publication statusPublished - Jul 2021

Scopus Subject Areas

  • Electrical and Electronic Engineering

User-Defined Keywords

  • decision making
  • Deep reinforcement learning
  • Internet of Things
  • resource allocation


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