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@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings(AAAI05-actions,
author="Alexander A.\ Sherstov and Peter Stone",
title="Improving Action Selection in {MDP}'s via Knowledge Transfer",
booktitle="Proceedings of the Twentieth National Conference on Artificial Intelligence",
month="July",year="2005",
abstract={
Temporal-difference reinforcement learning (RL) has
been successfully applied in several domains with
large \emph{state} sets. Large \emph{action} sets,
however, have received considerably less attention.
This paper demonstrates the use of knowledge
transfer between related tasks to accelerate
learning with large action sets. We introduce
\emph{action transfer}, a technique that extracts
the actions from the \mbox{(near-)optimal} solution
to the first task and uses them in place of the full
action set when learning any subsequent tasks. When
optimal actions make up a small fraction of the
domain's action set, action transfer can
substantially reduce the number of actions and thus
the complexity of the problem. However, action
transfer between \emph{dissimilar} tasks can be
detrimental. To address this difficulty, we
contribute \emph{randomized task perturbation}
(RTP), an enhancement to action transfer that makes
it robust to unrepresentative source tasks. We
motivate RTP action transfer with a detailed
theoretical analysis featuring a formalism of
related tasks and a bound on the suboptimality of
action transfer. The empirical results in this
paper show the potential of RTP action transfer to
substantially expand the applicability of RL to
problems with large action sets.
},
wwwnote={AAAI 2005},
)