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@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 \emph{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>},
)