Context (archaeology)

World Wide Web

Biology

Crowdsensing

Variety (cybernetics)

Computer security

The Internet

Scalability

Computer science

Distributed computing

Paleontology

Reinforcement learning

Database

Internet of Things

Data science

Artificial intelligence

Multimedia

IEEE Communications Surveys & Tutorials

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.

Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey