Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

Deep reinforcement learning has shown great potential in improving system performance
autonomously, by learning from iterations with the environment. However, traditional …

Broad-XAI moves away from interpreting individual decisions based on a single datum and
aims to provide integrated explanations from multiple machine learning algorithms into a …

Experience replay is a technique that allows off-policy reinforcement-learning methods to
reuse past experiences. The stability and speed of convergence of reinforcement learning …

Nowadays, broadband applications that use the licensed spectrum of the cellular network
are growing fast. For this reason, Long-Term Evolution-Unlicensed (LTE-U) technology is …

Prolonging the lifetime, and maximizing the throughput are important factors in designing an
efficient communications system, especially for energy harvesting-based systems. In this …

Q-Learning, which is a well-known model-free reinforcement learning algorithm, a learning
agent explores an environment to update a state-action function. In reinforcement learning …

An imposing task for a reinforcement learning agent in an uncertain environment is to
expeditiously learn a policy or a sequence of actions, with which it can achieve the desired …

We consider the stochastic dual dynamic programming (SDDP) algorithm-a widely
employed algorithm applied to multistage stochastic programming-and propose a variant …

Just like so many other topics that have been adopted into the realm of machine learning
and AI—deep learning, digital twins, active learning, and so on—Autonomous …