Matthew E. Taylor's Publications

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Transferring Instances for Model-Based Reinforcement Learning

Matthew E. Taylor, Nicholas K. Jong, and Peter Stone. Transferring Instances for Model-Based Reinforcement Learning. In The Adaptive Learning Agents and Multi-Agent Systems (ALAMAS+ALAG) workshop at AAMAS, May 2008.
AAMAS 2008 workshop on Adaptive Learning Agents and Multi-Agent Systems
Superseded by the ECML-08 conference paper Transferring Instances for Model-Based Reinforcement Learning.

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Abstract

Reinforcement learning agents typically require a significant amount of data before performing well on complex tasks. Transfer learning methods have made progress reducing sample complexity, but they have only been applied to model-free learning methods, not more data-efficient model-based learning methods. This paper introduces TIMBREL, a novel method capable of transferring information effectively into a model-based reinforcement learning algorithm. We demonstrate that TIMBREL can significantly improve the sample complexity and asymptotic performance of a model-based algorithm when learning in a continuous state space.

BibTeX Entry

@inproceedings(AAMAS08-ALAMAS-Taylor,
  author="Matthew E.\ Taylor and Nicholas K.\ Jong and Peter Stone",
  title="Transferring Instances for Model-Based Reinforcement Learning",
  Booktitle="The Adaptive Learning Agents and Multi-Agent Systems ({ALAMAS+ALAG}) workshop at {AAMAS}",
  month="May",
  year="2008",
  abstract = "\emph{Reinforcement learning} agents typically require a
    significant amount of data before performing well on complex
    tasks.  \emph{Transfer learning} methods have made progress
    reducing sample complexity, but they have only been applied to
    model-free learning methods, not more data-efficient model-based
    learning methods. This paper introduces TIMBREL, a novel method
    capable of transferring information effectively into a model-based
    reinforcement learning algorithm. We demonstrate that TIMBREL can
    significantly improve the sample complexity and asymptotic
    performance of a model-based algorithm when learning in a
    continuous state space.",
  wwwnote={<a href="http://ki.informatik.uni-wuerzburg.de/~kluegl/ALAMAS.ALAg/">AAMAS 2008 workshop on Adaptive Learning Agents and Multi-Agent Systems</a><br> Superseded by the ECML-08 conference paper <a href="http://teamcore.usc.edu/taylorm/Publications/b2hd-ECML08-Taylor.html">Transferring Instances for Model-Based Reinforcement Learning</a>.},
)

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