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Transfer Learning with Reinforcement Learning

Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments.

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Sources

  1. Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗
  2. Lazaric, A. (2012). Transfer in Reinforcement Learning: A Framework and a Survey. In M. Wiering & M. van Otterlo (Eds.), Reinforcement Learning: State-of-the-Art (pp. 143–173). Springer. link ↗

Related methods

Domain-adaptive reinforcement learningFine-Tuned Reinforcement LearningReinforcement LearningTransfer Learning with Convolutional Neural Network

Referenced by

Fine-Tuned Reinforcement LearningMultilingual Reinforcement LearningMultimodal Reinforcement LearningSelf-supervised Reinforcement LearningSemi-supervised Reinforcement Learning

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ScholarGate — Transfer Learning with Reinforcement Learning (Transfer Learning Applied to Reinforcement Learning). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/transfer-learning-reinforcement-learning