Matthew E. Taylor's Publications

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Cross-Domain Transfer for Reinforcement Learning

Matthew E. Taylor and Peter Stone. Cross-Domain Transfer for Reinforcement Learning. In Proceedings of the Twenty-Fourth International Conference on Machine Learning, June 2007.
ICML-2007

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Abstract

A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings have shown considerable promise, but they typically transfer between pairs of very similar tasks. This work introduces Rule Transfer, a transfer algorithm that first learns rules to summarize a source task policy and then leverages those rules to learn faster in a target task. This paper demonstrates that Rule Transfer can effectively speed up learning in Keepaway, a benchmark RL problem in the robot soccer domain, based on experience from source tasks in the gridworld domain. We empirically show, through the use of three distinct transfer metrics, that Rule Transfer is effective across these domains.

BibTeX Entry

@InProceedings(ICML07-taylor,
        author="Matthew E.\ Taylor and Peter Stone",
        title="Cross-Domain Transfer for Reinforcement Learning",
        booktitle="Proceedings of the Twenty-Fourth International
         Conference on Machine Learning",
        month="June",year="2007", 
        abstract="A typical goal for transfer learning algorithms is
          to utilize knowledge gained in a source task to learn a
          target task faster. Recently introduced transfer methods in
          reinforcement learning settings have shown considerable
          promise, but they typically transfer between pairs of very
          similar tasks. This work introduces Rule Transfer, a
          transfer algorithm that first learns rules to summarize a
          source task policy and then leverages those rules to learn
          faster in a target task. This paper demonstrates that Rule
          Transfer can effectively speed up learning in Keepaway, a
          benchmark RL problem in the robot soccer domain, based on
          experience from source tasks in the gridworld domain. We
          empirically show, through the use of three distinct transfer
          metrics, that Rule Transfer is effective across these
          domains.",
        wwwnote={<a href="http://oregonstate.edu/conferences/icml2007">ICML-2007</a>}, 
)

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