… deep reinforcement learning for proactive caching[34-36] and coded caching[41]. We observe
that deep reinforcement learning … The sequence-to-sequence learning model can also be …

… wireless secure communication system, where an IRS is deployed to adjust its reflecting
elements to secure the communication of … problem, a novel deep reinforcement learning (DRL)-…

… solve it online using reinforcement learning. The advantages of … than when using conventional
reinforcement learning algorithms; and … of conventional reinforcement learning algorithms. …

… on DRL in the field of wireless communication. Therefore, in the future, … wireless
communication, based on the characteristics of DRL, this study applies DRL to wireless …

… In this paper, we present a novel improved model-free reinforcement learning algorithm in
wireless communication networks that combines Expected Sarsa and eligibility traces. To be …

… network standards do not support the ensuing needs of machine learning (ML)-aware … a
reinforcement-learning-based, one of the ML techniques, a framework for a wireless channel …

… , reinforcement learning, and deep learning techniques which are important branches of
machine learning … of the deep learning to improve efficiency and performance in terms of the …

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have …
, particularly in wireless communications. The next generation of wireless networks is …

… Deep reinforcement learning (DRL) is an emerging methodology that can yield successful
control behavior for time-variant dynamic systems. This article proposes an efficient DRL-…

… (E2E) performance of the communication system. The existing … methods based on
reinforcement learning (RL) for point-to-… and improve the considered communication link’s success …