论文标题
可解释的强化学习的调查
A Survey of Explainable Reinforcement Learning
论文作者
论文摘要
可解释的强化学习（XRL）是可解释的机器学习的新兴子场，近年来引起了相当大的关注。 XRL的目的是阐明在顺序决策设置中学习代理的决策过程。在这项调查中，我们提出了一种新的分类法，以组织优先级RL设置的XRL文献。我们根据该分类法概述技术。我们指出了文献中的空白，我们用来激励和概述未来工作的路线图。
Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of learning agents in sequential decision-making settings. In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting. We overview techniques according to this taxonomy. We point out gaps in the literature, which we use to motivate and outline a roadmap for future work.
