As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL) methods generate a static explanation depicting their developers' intuition of what should be explained and how. In contrast, literature from the social sciences proposes that meaningful explanations are structured as a dialog between the explainer and the explainee, suggesting a more active role for the user and her communication with the agent. In this paper, we present ASQ-IT – an interactive explanation system that presents video clips of the agent acting in its environment based on queries given by the user that describe temporal properties of behaviors of interest. Our approach is based on formal methods: queries in ASQ-IT's user interface map to a fragment of Linear Temporal Logic over finite traces (LTLf), which we developed, and our algorithm for query processing is based on automata theory. User studies show that end-users can understand and formulate queries in ASQ-IT and that using ASQ-IT assists users in identifying faulty agent behaviors.

中文翻译：

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ASQ-IT：强化学习代理的交互式解释


随着强化学习方法日益取得成就，理解其解决方案的需求变得更加重要。大多数可解释的强化学习 (XRL) 方法都会生成静态解释，描述开发人员对应该解释什么以及如何解释的直觉。相比之下，社会科学文献提出，有意义的解释被构造为解释者和被解释者之间的对话，这表明用户及其与代理的沟通扮演着更积极的角色。在本文中，我们提出了 ASQ-IT——一种交互式解释系统，该系统根据用户给出的描述感兴趣行为的时间属性的查询来呈现在其环境中行动的代理的视频剪辑。我们的方法基于形式化方法：ASQ-IT 用户界面中的查询映射到我们开发的有限迹线线性时序逻辑 (LTLf) 的片段，并且我们的查询处理算法基于自动机理论。用户研究表明，最终用户可以理解和制定 ASQ-IT 中的查询，并且使用 ASQ-IT 可以帮助用户识别错误的代理行为。