… in wireless networks to achieve automatic resource allocation [18], [19]. In this paper, deep
reinforcement … allocation policy in vehicular networks with integrated networking, caching, and …

… reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall
network … relay incentive mechanism in cooperative communication networks,” Computers & …

… on deep reinforcement learning for RRAM in wireless networks… ” combinations of them; ”deep
reinforcement learning,” ”DRL,” ”… RRAM problems in wireless communication networks are …

… technique in future mobile networks. In this paper, by virtue of Deep Reinforcement Learning
(… propose a framework on Double Deep Q-Network for cooperative edge caching in mobile …

… are inefficient or inapplicable in wireless network environments due to the inherent dynamic
… employs Deep reinforcement learning (DRL) to address various difficulties in 5G networks …

… Her research interests include network architecture, resource management, and quality
of service provisioning in wired and wireless communication networks. She is/has been an …

… deep reinforcement learning approach to automatically make a decision for optimally allocating
the network … Here the saved backhaul bandwidth is equal to the wireless communication …

In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-Things
(IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy-, …

… a deep Q-network (DQN) that is based on the reinforcement learning but applies a deep neural
network … Q-learning method, we propose a deep reinforcement learning method, ie, DQN. …

… Abstract—This paper studies multi-agent deep reinforcement learning (… cell wireless powered
communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly …