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

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …

In the IoT and WSN era, large number of connected objects and sensing devices are
dedicated to collect, transfer, and generate a huge amount of data for a wide variety of fields …

Abstract The Deep Q-Network (DQN) is one of the key methods in the deep reinforcement
learning algorithm, which has a deep neural network structure used to estimate Q-values in …

Abstract The Deep Q-Network (DQN) is a method of deep reinforcement learning algorithm.
DQN is a deep neural network structure used for the estimation of Q value of the Q-learning …

Abstract The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms,
which uses deep neural network structure to estimate the Q-value in Q-learning. In the …

Energy saving has become a major concern for the developed society of our days. This
paper presents a Wireless Sensor and Actuator Network (WSAN) designed to provide …

Abstract Ambient Intelligence (AmI) deals with a new world of ubiquitous computing devices,
where physical environments interact intelligently and unobtrusively with people. AmI …