  Counterfactual explanations, which deal with "why not?" scenarios, can
provide insightful explanations to an AI agent's behavior. In this work, we
focus on generating counterfactual explanations for deep reinforcement learning
(RL) agents which operate in visual input environments like Atari. We introduce
counterfactual state explanations, a novel example-based approach to
counterfactual explanations based on generative deep learning. Specifically, a
counterfactual state illustrates what minimal change is needed to an Atari game
image such that the agent chooses a different action. We also evaluate the
effectiveness of counterfactual states on human participants who are not
machine learning experts. Our first user study investigates if humans can
discern if the counterfactual state explanations are produced by the actual
game or produced by a generative deep learning approach. Our second user study
investigates if counterfactual state explanations can help non-expert
participants identify a flawed agent; we compare against a baseline approach
based on a nearest neighbor explanation which uses images from the actual game.
Our results indicate that counterfactual state explanations have sufficient
fidelity to the actual game images to enable non-experts to more effectively
identify a flawed RL agent compared to the nearest neighbor baseline and to
having no explanation at all.
