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 …

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices …
intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for …

… investigations have allowed the integration of the Internet of Things (IoT) in current and …
papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL…

… edge computing (MEC) networks for intelligent internet of things (IoT), where multiple users
… intelligently through the deep reinforcement learning algorithm. In this algorithm, Deep Q-…

… In this paper, we advocate the use of deep reinforcement learning (DRL) to solve the
problem of caching IoT data at the edge without knowing future IoT data popularity, user request …

… medical Internet of Things. In this paper, we focus on the potential of the deep reinforcement
… In this work, we present several representative deep reinforcement learning models that are …

… in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications.
… issues, we propose a federated deep-reinforcement-learning-based cooperative edge …

… new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of
Things,” IEEE Internet Things … Xie, “An actor-critic deep reinforcement learning approach for …

… for the Industrial Internet of Things intrusion detection system based on a deep reinforcement
… This method combines deep learning’s observation capability with reinforcement learning’s …

… with QoE is solved by deep reinforcement learning (DRL) with the … deep reinforcement
learning approach for collaborative mobile edge computing IoT networks,” IEEE Internet Things …