… communications in wireless sensor networks (WSNs) demand for new approaches to meet
stringent energy and spectrum requirements. We turn to reinforcement learning, … the network …

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

… more convenient, which means that wireless communication … devices are connected to the
communication network, the wireless … DRL to wireless communication networks. An intelligent …

… takes full advantage of reinforcement learning and a fast … across the network’s links to suit
the prevailing network conditions. … up the learning process to avoid wasting network resources, …

… deep reinforcement learning based resource management for unicast V2V communications
… key parts in the reinforcement learning are shown and the deep Q-network based proposed …

… caching and device-to-device (D2D) communications. When considering the trust-…
reinforcement learning approach to automatically make a decision for optimally allocating the …

… to surveying the application of reinforcement learning methods in different wireless IoT …
scheduling optimization in a maritime communications network based on Software Defined …

… , we propose Dueling Deep Q-network (DDQN) algorithm which introduces neural
networks and dueling structure into Q-learning. Simulation results demonstrate the proposed …

… network slicing architecture. We focus on a non-cellular network scenario where vehicles
communicate … direct device-to-device interface (ie, sidelink communication). In such a vehicular …

… network edge using a deep reinforcement learning framework with Wolpertinger architecture.
In particular, we propose deep actorcritic reinforcement learning … , communication networks…