The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

Deep neural networks (DNNs) have achieved state-of-the-art results in a broad range of
tasks, in particular the ones dealing with the perceptual data. However, full-scale application …

Interpretability, explainability, and transparency are key issues to introducing artificial
intelligence methods in many critical domains. This is important due to ethical concerns and …

With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …

Methods to find counterfactual explanations have predominantly focused on one-step
decision making processes. In this work, we initiate the development of methods to find …

Despite the promising performance of deep reinforcement learning (DRL) agents in many
challenging scenarios, the black-box nature of these agents greatly limits their applications …

In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …

This article is a gentle discussion about the field of reinforcement learning in practice, about
opportunities and challenges, touching a broad range of topics, with perspectives and …

Counterfactual explanations are a common tool to explain artificial intelligence models. For
Reinforcement Learning (RL) agents, they answer" Why not?" or" What if?" questions by …