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.

通过展示强化学习代理在更广泛的轨迹分布中的行为，我们的方法可以传达代理在分布转移下的表现，从而有助于代理的有效验证。在用户研究中，我们展示了我们的方法可以使用户在代理验证任务中的得分比基准方法高。