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- W3156032009 endingPage "1692" @default.
- W3156032009 startingPage "1659" @default.
- W3156032009 abstract "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." @default.
- W3156032009 created "2021-04-26" @default.
- W3156032009 creator A5000582109 @default.
- W3156032009 creator A5052666042 @default.
- W3156032009 creator A5062162392 @default.
- W3156032009 creator A5066482792 @default.
- W3156032009 creator A5072659343 @default.
- W3156032009 creator A5086664284 @default.
- W3156032009 date "2021-01-01" @default.
- W3156032009 modified "2023-10-12" @default.
- W3156032009 title "Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey" @default.
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