… As a subgroup of artificial intelligence (AI), DRL can be considered as an integration of
reinforcement learning (RL) and deep learning (DL). On the one hand, RL involves self-learning …

… To the best of our knowledge, there does not exist an article in the literature that is dedicated
to surveying the application of reinforcement learning methods in different wireless IoT …

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 …

… a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework.
FADE enables base stations (BSs) to cooperatively learn a shared predictive model by …

… reinforcement learning algorithm. In this algorithm, Deep Q-Network is used to automatically
learn … show the effectiveness of the proposed reinforcement learning offloading strategy. In …

Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for …
Motivated by these observations, we proposed a reinforcement learning-based mechanism in …

… 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 …

… key paradigm to handle the increasing Internet-of-Things (IoT) … policy with the help of the
reinforcement learning (RL) method. … edge computing,” IEEE Internet Things J., vol. 6, no. 3, pp. …

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

In this article, we investigate an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT)
system in a sophisticated 3-D environment, where the UAV’s trajectory is optimized to …