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Matthew E. Taylor and Peter
Stone. Speeding up Reinforcement Learning with Behavior Transfer. In AAAI 2004 Fall Symposium on Real-life
Reinforcement Learning, October 2004.
Superseded by the journal article Transfer
Learning via Inter-Task Mappings for Temporal Difference Learning.
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{AAAI04-Symposium, author="Matthew E.\ Taylor and Peter Stone", title="Speeding up Reinforcement Learning with Behavior Transfer", BookTitle="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. }, wwwnote={Superseded by the journal article <a href="http://cs.lafayette.edu/~taylorm/Publications/b2hd-JMLR07-taylor.html">Transfer Learning via Inter-Task Mappings for Temporal Difference Learning</a>.} }
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