Knowledge gradient exploration in online kernel-based LSPI

Title

Knowledge gradient exploration in online kernel-based LSPI

 Author

Yahyaa, S.
Manderick, B.

Date

2013-11-08

 Abstract

We introduce online kernel-based LSPI (or least squares policy iteration) which combines feature of online LSPI and offline kernel-based LSPI. The knowledge gradient is used as exploration policy in both online LSPI and online kernel-based LSPI in order to compare their performance on 2 discrete Markov decision problems. Automatic feature selection in online kernel-based LSPI, which is a result of the approximate linear dependency based kernel sparsification, improves the performance when compared to online LSPI.

 To reference this document use:

http://resolver.tudelft.nl/uuid:02f49672-936c-430f-a30e-243388aeabe4

 Part of collection

Conference proceedings

 Document type

conference paper

 Rights

(c) 2013 Yahyaa, S.; Manderick, B.
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