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Matthew E. Taylor, Nicholas K. Jong, and Peter
Stone. Transferring Instances for Model-Based Reinforcement Learning. In Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 488–505,
September 2008. 19% acceptance rate
ECML-2008
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.
@inproceedings(ECML08-taylor, author="Matthew E.\ Taylor and Nicholas K.\ Jong and Peter Stone", title="Transferring Instances for Model-Based Reinforcement Learning", Booktitle="Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ({ECML PKDD})", pages="488--505", month="September", year= "2008", note = {19% acceptance rate}, wwwnote={<a href="http://www.ecmlpkdd2008.org/">ECML-2008</a>}, abstract={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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