… deep reinforcement learning, which integrates several layers of neural networks for extracting
and learning … model formulation of deep reinforcement learning as an effective method for …

… In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or
near-optimal solutions for security enhancement through the detection of various malicious …

… application schemes in CR networks such as Dynamic Channel Selection (DCS), topology
management, congestion control, and scheduling. In this paper, Reinforcement Learning (RL) …

… Reinforcement learning has been applied in a wide range of schemes in CR networks for
SU performance enhancements, whilst minimizing interference to PUs. The schemes are listed …

… with the help of a clustering mechanism and reinforcement learning (RL), an artificial intel…
network scalability by reducing the flooding of routing overheads as well as network stability …

… Reinforcement learning, which rapidly analyzes the amount of data in a model-free manner,
… algorithms based on reinforcement learning techniques in cognitive radio networks. The …

… network without using active radio-frequency (RF) transmitter chain. We consider the symbiosis
between the cellular network and the IoT network … ) in the primary network serves multiple …

Cognitive radio (CR) users are expected to be uncoordinated users that opportunistically
seek the spectrum resource from primary users (PUs) in a competitive way. In most existing …

… Reinforcement learning has found great success in variety of applications [4], [25]. In this …
multiagent reinforcement learning to optimize the sensing policy in a cognitive radio network. …

… schemes to enhance the network performance of CR networks, and investigate them using
… radio peripheral and GNU radio units. Two schemes are based on reinforcement learning (RL…