… , 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 …

… that can learn to well control a communication network from its … to leverage emerging Deep
Reinforcement Learning (DRL) for … communication network with K end-to-end communication …

… We give a review of the applications of reinforcement learning for intelligent caching,
mmWave communication network and UAV aided communication system in sections III, IV and V. …

… Selected aspects of communications and networking … , we analyze research papers applying
reinforcement learning to different aspects of communication and networking. We carefully …

… communication networks are increasingly under intensive study. Artificial intelligence enhances
the network … Prospect of designing intelligent networks using reinforcement learning is …

… Inverse reinforcement learning (future work): The scalar reward function does not provide …
Hence, we will study and formulate the inverse reinforcement learning problem to optimize the …

… In particular, Reinforcement Learning (RL) [21] has become an effective tool for addressing
resource management problems in wireless communication networks. The agent can directly …

… We study reinforcement learning at the sensors to find a transmission scheduling strategy, …
sensor networks. Numerical results demonstrate that the proposed reinforcement learning …

… covers specific topics such as: Deep reinforcement learning models, covering deep learning,
deep reinforcement learning, and models of deep reinforcement learning Physical layer …

… since the earlier communication networks. From wired and manually configured networks,
we moved to very dynamic and autonomic networks. The management of most of current …