@COMMENT This file was generated by bib2html.pl version 0.90
@COMMENT written by Patrick Riley
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@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={Official version from journal website.},
}