We present a user study to investigate the impact of explanations on non-experts'
understanding of reinforcement learning (RL) agents. We investigate both a common RL …

How should reinforcement learning (RL) agents explain themselves to humans not trained in
AI? To gain insights into this question, we conducted a 124-participant, four-treatment …

Causal explanations present an intuitive way to understand the course of events through
causal chains, and are widely accepted in cognitive science as the prominent model …

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

Reinforcement learning (RL) studies how an agent comes to achieve reward in an
environment through interactions over time. Recent advances in machine RL have …

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 …

Explainable reinforcement learning methods can roughly be divided into local explanations
that analyze specific decisions of the agents and global explanations that convey the …

We argue that cognitive models can provide a common ground between human users and
deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial …

Machine Learning models become increasingly proficient in complex tasks. However, even
for experts in the field, it can be difficult to understand what the model learned. This hampers …

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 …