This paper surveys the field of transfer learning in the problem setting of Reinforcement Learning (RL). RL has been a key solution to sequential decision-making problems. Along with the fast advances of RL in various domains, such as robotics and game-playing, transfer learning arises as an important technique to assist RL by leveraging and transferring external expertise to boost the learning process of RL. In this survey, we review the central issues of transfer learning in the RL domain, providing a systematic categorization of its state-of-the-art techniques. We analyze their goals, methodologies, applications, and the RL frameworks under which the transfer learning techniques are approachable. We discuss the relationship between transfer learning and other relevant topics from the RL perspective and also explore the potential challenges as well as future development directions for transfer learning in RL.

翻译：本文调查了在加强学习问题设置中的转让学习领域。RL是连续决策问题的关键解决办法。除了在机器人和游戏游戏等各个领域的快速发展外,转让学习还作为一种重要技术,通过利用和转让外部专门知识协助学习领域,促进学习领域的学习进程。在本次调查中,我们审查了RL领域转让学习的中心问题,对其最新技术进行了系统分类。我们分析了它们的目标、方法、应用和可接近的转让学习技术的RL框架。我们从RL的角度讨论了转让学习和其他相关专题之间的关系,还探讨了转让学习的潜在挑战以及未来发展方向。