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Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning. In Proceedings of the Sixth
International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 156–163, May 2007. 22%
acceptance rate
AAMAS-2007
The ambitious goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. While many past transfer methods have focused on transferring value-functions, this paper presents a method for transferring policies across tasks with different state and action spaces. In particular, this paper utilizes transfer via inter-task mappings for policy search methods (\sc tvitm-ps) to construct a transfer functional that translates a population of neural network policies trained via policy search from a source task to a target task. Empirical results in robot soccer Keepaway and Server Job Scheduling show that \sc tvitm-ps can markedly reduce learning time when full inter-task mappings are available. The results also demonstrate that \sc tvitm-ps still succeeds when given only incomplete inter-task mappings. Furthermore, we present a novel method for learning such mappings when they are not available, and give results showing they perform comparably to hand-coded mappings.
@InProceedings{AAMAS07-taylor, author="Matthew E.\ Taylor and Shimon Whiteson and Peter Stone", title="Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning", booktitle="Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multiagent Systems ({AAMAS})", pages="156--163", month="May",year="2007", abstract={ The ambitious goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. While many past transfer methods have focused on transferring value-functions, this paper presents a method for transferring policies across tasks with different state and action spaces. In particular, this paper utilizes transfer via inter-task mappings for policy search methods ({\sc tvitm-ps}) to construct a transfer functional that translates a population of neural network policies trained via policy search from a source task to a target task. Empirical results in robot soccer Keepaway and Server Job Scheduling show that {\sc tvitm-ps} can markedly reduce learning time when full inter-task mappings are available. The results also demonstrate that {\sc tvitm-ps} still succeeds when given only incomplete inter-task mappings. Furthermore, we present a novel method for learning such mappings when they are not available, and give results showing they perform comparably to hand-coded mappings. }, note = {22% acceptance rate}, wwwnote={<a href="http://www.aamas2007.nl/">AAMAS-2007</a>}, }
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