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@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="The Sixth International Joint Conference on Autonomous Agents and  Multiagent Systems",
        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 \emph{learning} such mappings when they are not available, and give results showing they perform comparably to hand-coded mappings.},
       wwwnote={<a href="http://www.aamas2007.nl/">AAMAS-2007</a>},
}