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 …

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 …

Deep Reinforcement Learning (DRL) has recently achieved significant advances in various
domains. However, explaining the policy of RL agents still remains an open problem due to …

The black-box nature of deep reinforcement learning (RL) hinders them from real-world
applications. Therefore, interpreting and explaining RL agents have been active research …

Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of
complex control tasks. However, the agent's decision-making process is generally not …

Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful
explanations to an AI agent's behavior [Miller [38]]. In this work, we focus on generating …

Despite notable results in various fields over the recent years, deep reinforcement learning
(DRL) algorithms lack transparency, affecting user trust and hindering deployment to high …

Recently deep reinforcement learning has achieved tremendous success in wide ranges of
applications. However, it notoriously lacks data-efficiency and interpretability. Data-efficiency …

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

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 …