Approximate policy iteration using large-margin classifiers

Authors:
Michail G. Lagoudakis;Ronald Parr
Affiliations:
Duke University, Durham, NC;Duke University, Durham, NC
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

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Abstract

Speculative execution of information gathering plans can dramatically reduce the effect of source I/O latencies on overall performance. However, the utility of speculation is closely tied to how accurately data values are predicted at runtime. Caching ...