Abstract
The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work.
Keywords
Transfer of learningReinforcement learningComputer scienceTask (project management)Inductive transferMulti-task learningArtificial intelligenceActive learning (machine learning)Error-driven learningMachine learningCore (optical fiber)Robot learningEngineering
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Publication Info
- Year
- 2009
- Type
- article
- Volume
- 10
- Issue
- 56
- Pages
- 1633-1685
- Citations
- 1561
- Access
- Closed
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Cite This
Matthew E. Taylor,
Peter Stone
(2009).
Transfer Learning for Reinforcement Learning Domains: A Survey.
Journal of Machine Learning Research
, 10
(56)
, 1633-1685.
https://doi.org/10.5555/1577069.1755839
Identifiers
- DOI
- 10.5555/1577069.1755839