Transfer Learning in Deep Reinforcement Learning: A Survey

🤖 Plain-English Summary

Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Specifically, we provide a framework for categorizing the advanced transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications.

🔑 Key Findings

• 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.

🔗 Related Resources