Abstract We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans to correctly perceive the aptitude of agents with different characteristics, including their capabilities and limitations, given visual summaries automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent's strengths and limitations in performing a task, and determine when it might need adjustments to improve its performance.

中文翻译：

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可解释强化学习的趣味性元素：了解代理的能力和局限性

摘要 我们提出了一个可解释的强化学习 (XRL) 框架，该框架分析代理与环境交互的历史，以提取有助于解释其行为的有趣元素。该框架依赖于从标准 RL 算法中随时可用的数据，并增加了代理在学习时可以轻松收集的数据。我们描述了如何基于提议的元素以突出关键交互时刻的短视频剪辑的形式创建代理行为的视觉摘要。我们还报告了一项用户研究，根据我们的框架自动生成的视觉摘要，我们评估了人类正确感知具有不同特征的代理的能力，包括他们的能力和局限性。