  Although deep reinforcement learning agents have produced impressive results
in many domains, their decision making is difficult to explain to humans. To
address this problem, past work has mainly focused on explaining why an action
was chosen in a given state. A different type of explanation that is useful is
a counterfactual, which deals with "what if?" scenarios. In this work, we
introduce the concept of a counterfactual state to help humans gain a better
understanding of what would need to change (minimally) in an Atari game image
for the agent to choose a different action. We introduce a novel method to
create counterfactual states from a generative deep learning architecture. In
addition, we evaluate the effectiveness of counterfactual states on human
participants who are not machine learning experts. Our user study results
suggest that our generated counterfactual states are useful in helping
non-expert participants gain a better understanding of an agent's decision
making process.
