… to share a common wireless spectrum and each network is unaware of the … reinforcement
learning (DRL) based MAC protocol for a particular network, and the objective of this network is …

… Two main branches of machine learning are supervised learning and reinforcement learning
(RL). When training input and output pairs are available, the supervised learning method is …

… In this network, we study our resource allocation policies to manage both power and time
for throughput maximization. We use deep reinforcement learning (DRL) to develop our …

… We select commercially available WiFi routers with Gigabit ethernet backhaul as the wireless
… Model-Free Reinforcement Learning: We develop a modelfree reinforcement learning (RL) …

… However, considering the dynamics and uncertainty that inherently exist in wireless network
environments, conventional approaches for service and resource management that require …

… Thus, the constrained reinforcement learning (cRL) method … We propose a constraint deep
Q-network (cDQN) algorithm to … reinforcement learning (DRL) algorithm is leveraged to learn …

… Inspired by the reinforcement learning (RL) in solving … strong feature representation ability
of deep neural network (DNN) [8] … strategy in the wireless caching network to improve cache hit …

… and sub-6 GHz band) wireless network where both orthogonal and non-… reinforcement
learning technique are proposed. The latter are based on multiple parallel deep neural networks …

… critical in wireless networks. Motivated by the recent advances in deep reinforcement learning
(… channel access and power control in a wireless interference network. We first propose a …

… service requirements, and communicate via industrial wireless networks (IWNs). However, …
To address this problem, a deep reinforcement learningbased dynamic priority multichannel …