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@InProceedings{AAMAS08-taylor,
author="Matthew E.\ Taylor and Gregory Kuhlmann and Peter Stone",
title="Autonomous Transfer for Reinforcement Learning",
booktitle="The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems",
month="May",
year="2008",
abstract={Recent work in transfer learning has succeeded in
making reinforcement learning algorithms more
efficient by incorporating knowledge from previous
tasks. However, such methods typically must be
provided either a full model of the tasks or an
explicit relation mapping one task into the
other. An autonomous agent may not have access to
such high-level information, but would be able to
analyze its experience to find similarities between
tasks. In this paper we introduce Modeling
Approximate State Transitions by Exploiting
Regression (MASTER), a method for automatically
learning a mapping from one task to another through
an agent's experience. We empirically demonstrate
that such learned relationships can significantly
improve the speed of a reinforcement learning
algorithm in a series of Mountain Car
tasks. Additionally, we demonstrate that our method
may also assist with the difficult problem of task
selection for transfer.},
wwwnote={AAMAS-2008},
}