  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.
