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Representation Transfer for Reinforcement Learning.
Matthew E. Taylor
and Peter Stone.
In AAAI 2007 Fall Symposium on Computational
Approaches to Representation Change during Learning and Development, November 2007.
2007
AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development
[PDF]144.9kB [postscript]375.1kB
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.
@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>},
)
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