There is a phenomenal burst of research activities in machine learning and wireless
systems. Machine learning evolved from a collection of powerful techniques in AI areas and …

We are delighted to introduce the readers to this special section of the IEEE Transactions on
Cognitive Communications and Networking (TCCN), which aims at exploring recent …

The rapid introduction of mobile devices and the growing popularity of mobile applications
and services create unprecedented infrastructure requirements for mobile and wireless …

Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent
years for future wireless networks. They can overcome the ever-increasing challenges of …

This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

With the continuous development of information technology, machine intelligence has
become a hot research issue. Deep learning can effectively extract the characteristic …

Reinforcement learning, particularly deep reinforcement learning, has made remarkable
progress in recent years and is now used not only in simulators and games but is also …

Deep Reinforcement learning a subpart of deep learning is used to maximize a numerical
parameter expressing long term objective to control the system. The main aim of …

In order to better understand state-of-the-art reinforcement learning agent, deep Q-network,
a brief review of reinforcement learning and Q-learning are first described. Then recent …

Deep reinforcement learning has recently achieved remarkable success in various domains,
ranging from games [10, 11, 12], to real-world applications such as neural architecture …