… deep reinforcement learning for proactive caching[34-36] and coded caching[41]. We observe
that deep reinforcement … also be integrated into these algorithms to accelerate deep neural …

… The goal of this study is to show the effectiveness of the proposed deep reinforcement
learning approach by simulations with a realistic multi-RAT (5G/4G/Satellite) network scenario. …

… 8, we show the integrated reward performance of the proposed scheme with different
learning rates α. There are four curves in Fig. 8, which can be divided into two groups. …

… maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) …
for nonorthogonal multiple access in the air–space–ground-integrated networks. This …

… a distributed deep reinforcement learning (DRL) framework for multi-user access control in
… To tackle the issues above, we make use of the deep reinforcement learning (DRL) technique…

… deep reinforcement learning mechanism that can dynamically adapt two parameters of the
system in order to enhance the probability of successful access … allows its integration into …

… It is in demand but challenging to conduct the integrated … to capacitate the automated
integrated design for the 6G-IIoT. In … deep reinforcement learning (DRL) to conduct the integrated …

… This study aims to evaluate deep reinforcement learning algorithms in integration traffic
control. To test control performance, we select two scenarios with ramp weaving sections. The …

… integrated access backhaul (IAB)-based networks that exhibit physical limitations. We design
a deep reinforcement-… a decentralized routing policy using deep policy gradients. To make …

… the deep reinforcement learning (DRL) in the dynamic network environment, we propose a
DRL framework to help the user make access … Yin, “Integrated networking, caching, and com…