Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be provided either a full model of the tasks or an explicit relation mapping one task into the other. An autonomous agent may not have access to such high-level information, but would be able to analyze its experience to find similarities between tasks. In this paper we introduce Modeling Approximate State Transitions by Exploiting Regression (MASTER), a method for automatically learning a mapping from one task to another through an agent's experience. We empirically demonstrate that such learned relationships can significantly improve the speed of a reinforcement learning algorithm in a series of Mountain Car tasks. Additionally, we demonstrate that our method may also assist with the difficult problem of task selection for transfer.

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In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.

@InProceedings{AAMAS08-taylor, title={Autonomous Transfer for Reinforcement Learning}, author={Matthew E. Taylor and Gregory Kuhlmann and Peter Stone}, booktitle={The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems}, month={May}, url="http://nn.cs.utexas.edu/?AAMAS08-taylor", year={2008} }