Peter Stone's Selected Publications

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Autonomous Transfer for Reinforcement Learning

Matthew E. Taylor, Gregory Kuhlmann, and Peter Stone. Autonomous Transfer for Reinforcement Learning. In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.
AAMAS-2008

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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.

BibTeX Entry

@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>},
}

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