As machine learning (ML) models becomemore widely deployed in high-stakes
applications, counterfactual explanations have emerged as key tools for providing …

In this paper we introduce and evaluate a distal explanation model for model-free
reinforcement learning agents that can generate explanations forwhy'andwhy not'questions …

To increase the adoption of counterfactual explanations in practice, several criteria that
these should adhere to have been put forward in the literature. We propose counterfactual …

Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and
decision-making processes. The majority of XRL approaches focus on local explanations …

Though reinforcement learning has greatly benefited from the incorporation of neural
networks, the inability to verify the correctness of such systems limits their use. Current work …

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

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 …

Explanation techniques that synthesize small, interpretable changes to a given image while
producing desired changes in the model prediction have become popular for introspecting …

As reinforcement learning (RL) continues to improve and be applied in situations alongside
humans, the need to explain the learned behaviors of RL agents to end-users becomes …

Counterfactual Explanation (CE) is a post-hoc explanation method that provides a
perturbation for altering the prediction result of a classifier. An individual can interpret the …