Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial and error, to solve complex decision-making problems. Learning with little supervision causes RL methods to require large amounts of data, which renders them too expensive for many applications (e.g. robotics). By reusing knowledge from a different task, knowledge transfer methods present an alternative to reduce the training time in RL. Given how severe data scarcity can be, there has been a growing interest for methods capable of transferring knowledge across different domains (i.e. problems with different representation) due to the flexibility they offer. This review presents a unifying analysis of methods focused on transferring knowledge across different domains. Through a taxonomy based on a transfer-approach categorization, and a characterization of works based on their data-assumption requirements, the objectives of this article are to 1) provide a comprehensive and systematic revision of knowledge transfer methods for the cross-domain RL setting, 2) categorize and characterize these methods to provide an analysis based on relevant features such as their transfer approach and data requirements, and 3) discuss the main challenges regarding cross-domain knowledge transfer, as well as ideas of future directions worth exploring to address these problems.

翻译：强化学习（RL）提供了一种框架，使智能体能够通过试错训练来解决复杂的决策问题。由于学习过程中监督信息极少，强化学习方法通常需要大量数据，这使得它们在许多应用场景（如机器人技术）中成本过高。通过复用不同任务中的知识，知识迁移方法为减少RL训练时间提供了替代方案。鉴于数据稀缺问题的严重性，因其灵活性，能够跨不同领域（即具有不同表征的问题）迁移知识的方法日益引起关注。本综述对专注于跨领域知识迁移的方法进行了统一分析。基于迁移方法分类的体系以及对各研究数据假设要求的特征描述，本文旨在：1）系统全面地梳理跨领域RL设置下的知识迁移方法；2）分类并描述这些方法，基于其迁移方案和数据需求等关键特征进行分析；3）探讨跨领域知识迁移面临的主要挑战，以及值得探索的未来研究方向以应对这些问题。