Transferring Instances for Model-Based Reinforcement Learning (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.
In Machine Learning and Knowledge Discovery in Databases, Vol. 5212, pp. 488-505, September 2008.

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