  In order for humans to confidently decide where to employ RL agents for
real-world tasks, a human developer must validate that the agent will perform
well at test-time. Some policy interpretability methods facilitate this by
capturing the policy's decision making in a set of agent rollouts. However,
even the most informative trajectories of training time behavior may give
little insight into the agent's behavior out of distribution. In contrast, our
method conveys how the agent performs under distribution shifts by showing the
agent's behavior across a wider trajectory distribution. We generate these
trajectories by guiding the agent to more diverse unseen states and showing the
agent's behavior there. In a user study, we demonstrate that our method enables
users to score better than baseline methods on one of two agent validation
tasks.
