# 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Superseded by the ECML-08 conference paper Transferring Instances for Model-Based Reinforcement Learning.

(unavailable)

### 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