让用户通过提问交互式理解强化学习智能体的行为逻辑。
Interactive Explanations for Reinforcement-Learning Agents
- 用户用自然语言提问，系统用时序逻辑解析并回放相关行为视频。
- 用户可精准定位智能体异常行为，识别率显著提升。
- 适合希望深入理解强化学习决策过程的研究者与开发者。
随着强化学习方法取得越来越多成果，理解其决策机制变得愈发重要。现有可解释强化学习（XRL）方法多生成静态解释，依赖开发者的直觉。而社会科学研究表明，有意义的解释应是解释者与被解释者之间的对话。本文提出ASQ-IT——一种交互式解释系统，用户可通过描述行为的时间特性提问，系统基于线性时序逻辑有限轨迹（LTLf）片段生成对应的智能体行为视频片段。该系统基于自动机理论设计查询处理算法。用户研究表明，普通用户能有效理解并提出问题，且使用该系统显著提升了识别智能体故障行为的能力。
原文摘要 · Abstract (English)
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.
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