Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

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 …

Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for
tasks where complex policies are learned within reactive systems. Unfortunately, these …

In this paper, we consider the problem of learning safe policies for probabilistic-constrained
reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high …

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …

Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide
variety of robotic applications. A natural consequence is the adoption of this paradigm for …

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in
machine learning due to its successful applications to real-world and complex systems …

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 …

Deep Neural Networks are increasingly adopted in critical tasks that require a high level of
safety, eg, autonomous driving. While state-of-the-art verifiers can be employed to check …