Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …

Abstract We consider Reinforcement Learning (RL) problems where an agent attempts to
maximize a reward signal while minimizing a cost function that models unsafe behaviors …

In the last years, neural networks achieved groundbreaking successes in a wide variety of
applications. However, for safety critical tasks, such as robotics and healthcare, it is …

Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical
subtasks due to its ability to learn complex behaviours in a dynamic environment. This task …

The development of artificial microscopic robots, like synthetic microswimmers, is one of the
state-of-the-art research topics due to their promising biomedical applications. The …

We propose a novel benchmark environment for Safe Reinforcement Learning focusing on
aquatic navigation. Aquatic navigation is an extremely challenging task due to the non …

Recent Multi-Agent Deep Reinforcement Learning approaches factorize a global action-
value to address non-stationarity and favor cooperation. These methods, however, hinder …

The combination of Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL)
has been recently proposed to merge the benefits of both solutions. Existing mixed …

This article presents a literature review of the past five years of studies using Deep
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …

This work investigates the effects of Curriculum Learning (CL)-based approaches on the
agent's performance. In particular, we focus on the safety aspect of robotic mapless …