Explainable AI (XAI) has demonstrated the potential to help reinforcement
learning (RL) practitioners to understand how RL models work. However, XAI for
users who do not have RL expertise (non-RL experts), has not been studied
sufficiently. This results in a difficulty for the non-RL experts to
participate in the fundamental discussion of how RL models should be designed
for an incoming society where humans and AI coexist. Solving such a problem
would enable RL experts to communicate with the non-RL experts in producing
machine learning solutions that better fit our society. We argue that
abstracted trajectories, that depicts transitions between the major states of
the RL model, will be useful for non-RL experts to build a mental model of the
agents. Our early results suggest that by leveraging a visualization of the
abstracted trajectories, users without RL expertise are able to infer the
behavior patterns of RL.

解释性人工智能（XAI）可以帮助研究强化学习（RL）模型如何工作的 RL 从业者，但对于没有 RL 专业知识的用户（非 RL 专家）的 XAI 研究不够充分。我们认为，描述 RL 模型主要状态之间转换的抽象轨迹对于非 RL 专家构建对代理模型的心理模型很有用。我们的早期结果表明，通过利用抽象轨迹的可视化，没有 RL 专业知识的用户能够推断 RL 的行为模式。