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Matthew E. Taylor and Peter
Stone. Towards Reinforcement Learning Representation Transfer (Poster). In The Sixth International Joint Conference
on Autonomous Agents and Multiagent Systems (AAMAS), pp. 683–685, May 2007. Poster: 22% acceptance rate for
talks, additional 25% for posters.
AAMAS-2007.
Superseded by the symposium
paper Representation Transfer for
Reinforcement Learning.
(unavailable)
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 methods are able to successfully transfer knowledge between agents in different reinforcement learning tasks, reducing the time needed to learn the target. However, the complimentary task of representation transfer, i.e. 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 introduces one such representation transfer algorithm which is implemented in a complex multiagent domain. Experiments demonstrate that transferring the learned knowledge between different representations is both possible and beneficial.
@InProceedings{AAMAS07-taylorRT,
author="Matthew E.\ Taylor and Peter Stone",
title="Towards Reinforcement Learning Representation Transfer (Poster)",
booktitle="The Sixth International Joint Conference on
Autonomous Agents and Multiagent Systems ({AAMAS})",
pages="683--685",
month="May",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 methods are able to successfully transfer knowledge
between agents in different reinforcement learning tasks,
reducing the time needed to learn the target. However, the
complimentary task of representation transfer, i.e.\
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 introduces one such
representation transfer algorithm which is implemented in a
complex multiagent domain. Experiments demonstrate that
transferring the learned knowledge between different
representations is both possible and beneficial.},
note = "Poster: 22% acceptance rate for talks, additional 25% for posters.",
wwwnote={<a
href="http://www.aamas2007.nl/">AAMAS-2007</a>. <br>Superseded by the symposium paper <a href="http://cs.lafayette.edu/~taylorm/Publications/b2hd-AAAI07-Symposium.html">Representation
Transfer for Reinforcement Learning</a>.},
}
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