Peter Stone's Selected Publications

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Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning

Matthew E. Taylor, Shimon Whiteson, and Peter Stone. Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning. In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
AAMAS-2007

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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.

BibTeX Entry

@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>},
}       

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