… application of the traditional, as well as the enhanced, RL models, to routing in wireless
networks… The routing challenges associated with different types of distributed wireless networks, …

… network and application performance enhancements. This article provides an extensive review
on the application … , and open issues associated with the application of RL in WSNs. This …

… communication technology, this study applies DNNs and DRL algorithms to wireless networks,
providing experimental basis for the development of the wireless communication industry. …

… In this article, we comprehensively review the applications of RL in wireless networks from
a layering perspective. First, we present an overview of the principle, fundamentals, and sev…

… reinforcement learning for energy optimization and resource allocation in wireless networks,
… ”AND/OR” combinations of them; ”deep reinforcement learning,” ”DRL,” ”energy optimization…

… For example, we know that the expected transmission time of a packet in a wireless
network is 20 minutes. However, this information may not be so meaningful because it may …

… and dynamic channel selection in wireless networks. Examples … wireless networks are
mobile ad hoc networks, wireless sensor networks, cellular networks and cognitive radio networks…

… in single-hop networks. A similar approach has … application of reinforcement learning to the
medium access control problem. A new novel protocol is introduced for single hop networks (…

… (CRN) has been regarded as the next-generation wireless network centered on the application
of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and …

… An optimization problem is formulated to maximize the network operator’s … , wireless
channel qualities, and the cache status of all the available nodes. We apply a deep reinforcement …