In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …

To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …

Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped
reward function. Intrinsically motivated exploration methods address this limitation by …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

We introduce a value-based RL agent, which we call BBF, that achieves super-human
performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used …

Offline reinforcement learning (offline RL), which aims to find an optimal policy from a
previously collected static dataset, bears algorithmic difficulties due to function …

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …

Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …