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@InProceedings(AAAI07-Symposium-Taylor,
        author="Matthew E.\ Taylor and Peter Stone",
        title="Representation Transfer for Reinforcement Learning",
        BookTitle="AAAI 2007 Fall Symposium on Computational
        Approaches to Representation Change during Learning and
        Development",
        month="November",year="2007",
        abstract={Transfer learning problems are typically framed as
        leveraging knowledge learned on a source task to improve
        learning on a related, but different, target task. Current
        transfer learning methods are able to successfully transfer
        knowledge from a source reinforcement learning task into a
        target task, reducing learning time. However, the
        complimentary task of transferring knowledge between agents
        with different internal representations has not been well
        explored The goal in both types of transfer problems is the
        same: reduce the time needed to learn the target with
        transfer, relative to learning the target without
        transfer. This work defines representation transfer, contrasts
        it with task transfer, and introduces two novel
        algorithms. Additionally, we show representation transfer
        algorithms can also be successfully used for task transfer,
        providing an empirical connection between the two
        problems. These algorithms are fully implemented in a complex
        multiagent domain and experiments demonstrate that
        transferring the learned knowledge between different
        representations is both possible and beneficial.  },
        wwwnote={<a
        href="http://yertle.isi.edu/~clayton/aaai-fss07/index.php/Welcome">2007
        AAAI Fall Symposium: Computational Approaches to
        Representation Change during Learning and Development</a>},
)