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Transferring Instances for Model-Based Reinforcement Learning (2008)
Matthew E. Taylor
,
Nicholas K. Jong
, and
Peter Stone
Recent work in transfer learning has succeeded in 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 primarily 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 efficiency and asymptotic performance of a model-based algorithm when learning in a continuous state space. Additionally, we conduct experiments to test the limits of TIMBREL's effectiveness.
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Citation:
In
Machine Learning and Knowledge Discovery in Databases
, Vol. 5212, pp. 488-505, September 2008.
Bibtex:
@inproceedings{ECML08-taylor, title={Transferring Instances for Model-Based Reinforcement Learning}, author={Matthew E. Taylor and Nicholas K. Jong and Peter Stone}, booktitle={Machine Learning and Knowledge Discovery in Databases}, volume={5212}, month={September}, series={Lecture Notes in Artificial Intelligence}, pages={488-505}, url="http://www.cs.utexas.edu/users/ai-lab?ECML08-taylor", year={2008} }
People
Nicholas Jong
Ph.D. Alumni
nickjong [at] me com
Peter Stone
Faculty
pstone [at] cs utexas edu
Matthew Taylor
Ph.D. Alumni
taylorm [at] eecs wsu edu
Areas of Interest
Other Areas
Planning
Reinforcement Learning
Transfer Learning
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Learning Agents