… Inspired by the success of machine learning in solving complicated control and decision-…
deep reinforcement-learning (DRL)–based approaches that allow network entities to learn and …

… In this work, we demonstrate how to use coordination graphs and reinforcement learning in
a … show how mobile networks can be modeled using coordination graphs and how network …

… In this work, deep Q-network (DQN) as a deep reinforcement learning technique is used to
… mobile communication scheme based on the deep Qnetwork, a deep reinforcement learning …

… on mobile edge caching and DRL. We first examine the key issues in mobile edge caching
and review the existing learn… in mobile edge caching, and illustrate an example of DRL-based …

… A large number of mobility-based services have brought heavy workloads to mobile …
Reinforcement Learning (RL) and propose a novel approach, named Smart Reinforcement Learning…

… hoc networks as a multi-agent learning domain and discuss some motivations for this study.
We apply reinforcement learning … We apply Q-learning to the case of mobile networks, where …

… Towards this end, we propose a deep reinforcement learning-based online offloading (DROO)
framework to maximize the weighted sum of the computation rates of all the WDs, ie, the …

… mobile offloading and the caching procedures. In this article, we propose security solutions
that apply reinforcement learn… -based security solution for mobile edge caching and discuss …

… REINFORCEMENT LEARNING FRAMEWORK In this section, we first introduce the reinforcement
learning … Then, we present the classical Q-learning and the emerging deep Q network. …

… adopt a model-free reinforcement learning scheme called Qlearning [10], which allows us
to learn the optimal control policy without any information of dynamic network statistics. The Q-…