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Transfer Learning for Reinforcement Learning Domains: A Survey.
Matthew
E. Taylor and Peter Stone.
Journal of Machine Learning Research,
10(1):1633–1685, 2009.
Official version
from journal website.
[PDF]399.8kB [postscript]579.4kB
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.
@Article{JMLR09-taylor,
Author="Matthew E.\ Taylor and Peter Stone",
title="Transfer Learning for Reinforcement Learning Domains: A Survey",
journal="Journal of Machine Learning Research",
volume="10",number="1",
pages="1633--1685",
year="2009",
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.",
wwwnote={<a href="http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf">Official version</a> from journal website.},
}
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