… that can learn to well control a communicationnetwork from its … to leverage emerging Deep ReinforcementLearning (DRL) for … communicationnetwork with K end-to-end communication …
… We give a review of the applications of reinforcementlearning for intelligent caching, mmWave communicationnetwork and UAV aided communication system in sections III, IV and V. …
… Selected aspects of communications and networking … , we analyze research papers applying reinforcementlearning to different aspects of communication and networking. We carefully …
Y Qian, J Wu, R Wang, F Zhu… - … of Communications and …, 2019 - ieeexplore.ieee.org
… communicationnetworks are increasingly under intensive study. Artificial intelligence enhances the network … Prospect of designing intelligent networks using reinforcementlearning is …
… Inverse reinforcementlearning (future work): The scalar reward function does not provide … Hence, we will study and formulate the inverse reinforcementlearning problem to optimize the …
Y Zhi, J Tian, X Deng, J Qiao, D Lu - Digital Communications and Networks, 2022 - Elsevier
… In particular, ReinforcementLearning (RL) [21] has become an effective tool for addressing resource management problems in wireless communicationnetworks. The agent can directly …
… We study reinforcementlearning at the sensors to find a transmission scheduling strategy, … sensor networks. Numerical results demonstrate that the proposed reinforcementlearning …
… covers specific topics such as: Deep reinforcementlearning models, covering deep learning, deep reinforcementlearning, and models of deep reinforcementlearning Physical layer …
Z Mammeri - Ieee Access, 2019 - ieeexplore.ieee.org
… since the earlier communicationnetworks. From wired and manually configured networks, we moved to very dynamic and autonomic networks. The management of most of current …