Project "Reinforcement Learning"
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Reinforcement learning is learning from rewards and penalties that can be delayed, often in nondeterministic domains. The designers of reinforcement learning systems often have to choose suitable representations of reinforcement learning tasks. Consequently, we study how representations affect the performance of reinforcement learning methods. This research provides guidance for empirical reinforcement learning researchers on how to distinguish hard reinforcement learning tasks from easy ones and how to choose reward structures and value initializations in a way that allows reinforcement learning tasks to be solved efficiently, thus preventing them from making costly mistakes.
The "Reinforcement Learning Sample Complexity Analysis" webpages link to this page.
Many publishers do not want authors to make their papers available electronically after the papers have been published. Please use the electronic versions provided here only if hardcopies are not yet available. If you have comments on any of these papers, please send me an email! Also, please send me your papers if we have common interests.
This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.
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