Transfer Learning in Deep Reinforcement Learning: A Survey

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.

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• 1 Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks.
• 2 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.
• 3 In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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