Transfer Learning in Deep Reinforcement Learning: A Survey

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…

• NS
  National Science Foundation

  Awards: IIS-1749940, 2212174, 1749940
• OO
  Office of Naval Research

  Awards: N00014-20-1-2382, N00014