Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.

翻译：强化学习是一种解决序贯决策问题的学习范式。近年来，随着深度神经网络的快速发展，强化学习取得了显著进展。在机器人、游戏博弈等多个领域展现出广阔前景的同时，迁移学习通过从外部专业知识中转移知识，旨在提升学习过程的效率和有效性，从而应对强化学习所面临的各种挑战。本综述系统性地研究了深度强化学习背景下迁移学习方法的最新进展。具体而言，我们构建了一个对现有先进迁移学习方法进行分类的框架，在此框架下分析了它们的目标、方法论、兼容的强化学习主干网络及实际应用。我们还从强化学习视角出发，探讨了迁移学习与其他相关主题之间的关联，并指出了未来研究有待解决的关键挑战。