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Transfer Learning for Reinforcement Learning Domains: A Survey (2009)
Matthew E. Taylor
and
Peter Stone
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.
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Citation:
Journal of Machine Learning Research
, 10(1):1633-1685, 2009.
Bibtex:
@Article{JMLR09-taylor, title={Transfer Learning for Reinforcement Learning Domains: A Survey}, author={Matthew E. Taylor and Peter Stone}, volume={10}, journal={Journal of Machine Learning Research}, number={1}, pages={1633-1685}, url="http://nn.cs.utexas.edu/?taylor:jmlr09", year={2009} }
People
Peter Stone
pstone [at] cs utexas edu
Matthew Taylor
taylorm [at] eecs wsu edu
Areas of Interest
Transfer Learning
Reinforcement Learning