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Matthew E. Taylor and Peter Stone. Speeding up Reinforcement Learning with Behavior Transfer. In Technical Report of the AAAI 2004 Fall Symposium on Real-life Reinforcement Learning, October 2004.
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Reinforcement learning (RL) methods have become popular machine learning techniques in recent years. RL has had some experimental successes and has been shown to exhibit some desirable properties in theory, but it has often been found very slow in practice. In this paper we introduce behavior transfer, a novel approach to speeding up traditional RL. We present experimental results showing a learner is able learn one task and then use behavior transfer to markedly reduce the total training time for a more complex task.
@InProceedings(aaai-fall04-transfer,
author="Matthew E.\ Taylor and Peter Stone",
title="Speeding up Reinforcement Learning with Behavior Transfer",
BookTitle="Technical Report of the AAAI 2004 Fall Symposium on
Real-life Reinforcement Learning",
month="October",year="2004",
abstract={
Reinforcement learning (RL) methods
have become popular machine learning techniques in
recent years. RL has had some experimental successes
and has been shown to exhibit some desirable
properties in theory, but it has often been found
very slow in practice. In this paper we introduce
\emph{behavior transfer}, a novel approach to
speeding up traditional RL. We present experimental
results showing a learner is able learn one task and
then use behavior transfer to markedly reduce the
total training time for a more complex task.
},
)
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