Smart services are an important element of the smart cities and the Internet of Things (IoT)
ecosystems where the intelligence behind the services is obtained and improved through …

Modern communication networks have become very complicated and highly dynamic, which
makes them hard to model, predict and control. In this paper, we develop a novel experience …

Reinforcement learning is one of the core components in designing an artificial intelligent
system emphasizing real-time response. Reinforcement learning influences the system to …

In order to mitigate some of the inefficiencies of Reinforcement Learning (RL), modular
approaches composing different decision-making policies to derive agents capable of …

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 rise of chronic disease patients and the pandemic pose immediate threats to healthcare
expenditure and mortality rates. This calls for transforming healthcare systems away from …

Training machine learning models, such as reinforcement learning models, require a
significant investment of time, and a trained model can only work on a specific system in a …

Deep reinforcement learning (DRL) combines deep learning (DL) with a reinforcement
learning (RL) architecture. It has been able to perform a wide range of complex decision …

Non terrestrial networks (NTN) involving 'in the sky'objects such as low-earth orbit satellites,
high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected …

Deep Reinforcement Learning (DRL) is considered a potential framework to improve many
real-world autonomous systems; it has attracted the attention of multiple and diverse fields …