In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Modern machine learning requires system designers to specify aspects of the learning
pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn …

Tremendous progress has been made in reinforcement learning (RL) over the past decade.
Most of these advancements came through the continual development of new algorithms …

In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …

Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …

Systematic development of accurate density functionals has been a decades-long challenge
for scientists. Despite emerging applications of machine learning (ML) in approximating …

The past decade has seen vast progress in deep reinforcement learning (RL) on the back of
algorithms manually designed by human researchers. Recently, it has been shown that it is …

Intelligent agents should have the ability to leverage knowledge from previously learned
tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have …