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ATAL
2006
Springer

A hierarchical approach to efficient reinforcement learning in deterministic domains

11 years 5 months ago
A hierarchical approach to efficient reinforcement learning in deterministic domains
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-scale state spaces. We bring these three ideas together in a new algorithm. Our algorithm tackles two open problems from the reinforcement-learning literature, and provides a solution to those problems in deterministic domains. First, it shows how models can improve learning speed in the hierarchybased MaxQ framework without disrupting opportunities e abstraction. Second, we show how hierarchies can augment existing factored exploration algorithms to achieve not only low sample complexity for learning, but provably efficient planning as well. We illustrate the resulting performance gains in example domains. We prove polynomial bounds on the computational effort needed to attain near optimal performance within the hierarchy. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: Artificial Intelligen...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where ATAL
Authors Carlos Diuk, Alexander L. Strehl, Michael L. Littman
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