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@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.},
}