@COMMENT This file was generated by bib2html.pl <http://www.cs.cmu.edu/~pfr/misc_software/index.html#bib2html> version 0.90
@COMMENT written by Patrick Riley <http://www.cs.cmu.edu/~pfr>
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings{AAMAS08-taylor,
  author="Matthew E.\ Taylor and Gregory Kuhlmann and Peter Stone",
  title="Autonomous Transfer for Reinforcement Learning",
  booktitle="The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems",
  month="May",
  year="2008",
  abstract={Recent work in transfer learning has succeeded in
            making reinforcement learning algorithms more
            efficient by incorporating knowledge from previous
            tasks. However, such methods typically must be
            provided either a full model of the tasks or an
            explicit relation mapping one task into the
            other. An autonomous agent may not have access to
            such high-level information, but would be able to
            analyze its experience to find similarities between
            tasks. In this paper we introduce Modeling
            Approximate State Transitions by Exploiting
            Regression (MASTER), a method for automatically
            learning a mapping from one task to another through
            an agent's experience. We empirically demonstrate
            that such learned relationships can significantly
            improve the speed of a reinforcement learning
            algorithm in a series of Mountain Car
            tasks. Additionally, we demonstrate that our method
            may also assist with the difficult problem of task
            selection for transfer.},
 wwwnote={<a href="http://gaips.inesc-id.pt/aamas2008/">AAMAS-2008</a>},
}