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Matthew E. Taylor, Shimon Whiteson, and Peter Stone. Transfer Learning for Policy Search Methods. In ICML 2006: Proceedings of the Twenty-Third International Conference on Machine Learning Transfer Learning Workshop, June 2006.
An ambitious goal of transfer learning is to learn a task faster after training on a different, but related, task. In this paper we extend a previously successful temporal difference approach to transfer in reinforcement learning tasks to work with policy search. In particular, we show how to construct a mapping to translate a population of policies trained via genetic algorithms (GAs) from a source task to a target task. Empirical results in robot soccer Keepaway, a standard RL benchmark domain, demonstrate that transfer via inter-task mapping can markedly reduce the time required to learn a second, more complex, task.
@InProceedings{taylor:icml06,
author = "Matthew E. Taylor and Shimon Whiteson and Peter Stone",
title = "Transfer Learning for Policy Search Methods",
booktitle = "ICML 2006: Proceedings of the Twenty-Third International
Conference on Machine Learning Transfer Learning Workshop",
month = "June",
year = 2006,
}
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