The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since
their first introduction in 2015. Though uses in many different applications are being found …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Reinforcement Learning (RL) systems achieved outstanding performance in different
domains such as Atari games, finance, healthcare, and self-driving cars. However, their …

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human
performance across various domains. This presents us with an opportunity to further human …

Reinforcement Learning (RL) algorithms are showing promising results in simulated
environments, but their replication in real physical applications, even more so in safety …

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

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 …

Counterfactual examples (CFs) are one of the most popular methods for attaching post hoc
explanations to machine learning models. However, existing CF generation methods either …

Current work in explainable reinforcement learning generally produces policies in the form
of a decision tree over the state space. Such policies can be used for formal safety …

The remarkable success of the use of machine learning-based solutions for network security
problems has been impeded by the developed ML models' inability to maintain efficacy …