… reinforcement learning (RL) [5] for wireless networking. Specifically, we demonstrate that the
use of deep neural networks (DNN) … us with two essential properties to wireless MAC: (i) fast …

… This work is the first to apply deep reinforcement learning to … learning techniques on various
dynamic wireless network … wireless network can be applied to a larger wireless network. Also…

… In this paper, we consider the application of deep RL techniques to the problem … wireless
networks, and we propose a mechanism for scheduling transmissions using multi-agent deep …

… deep reinforcement learning approach to wireless networks … We use Google TensorFlow to
implement deep reinforcement … interference alignment wireless networks. Simulation results …

… the use of Deep Reinforcement Learning, in particular, Deep Q learning, … deep learning
with Q learning, Deep Q learning or Deep Q Network (DQN) [5] can use a deep neural network …

… as enabling techniques for AI-based wireless networks and we focus on delivering a more
applied perspective of MARL to solve wireless communication problems. Table II summarizes …

… neural network training. The results demonstrate that this ap… of deep reinforcement learning
for proactive caching[34-36] and coded caching[41]. We observe that deep reinforcement …

… communication technology, this study applies DNNs and DRL algorithms to wireless networks,
providing experimental basis for the development of the wireless communication industry. …

… at the wireless network edge using a deep reinforcement learning framework with
Wolpertinger architecture. In particular, we propose deep actorcritic reinforcement learning based …

… [14] 2015 The authors in this paper provided an overview of the application of RL based
routing schemes in distributed wireless networks. The challenges, the advantages brought, and …