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。

强化学习中可解释性的抽象轨迹可视化