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

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

Abstract Explainable Artificial Intelligence (XAI), ie, the development of more transparent and
interpretable AI models, has gained increased traction over the last few years. This is due to …

Deep reinforcement learning (DRL) has proven successful for many difficult control
problems by learning policies represented by neural networks. However, the complexity of …

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 …

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 …

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

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