Learning models of relational MDPs using graph Kernels
Autor(en): | Halbritter, F. Geibel, P. |
Stichwörter: | Graph theory; Mathematical models; Support vector machines, Graph Kernels; Indirect reinforcement learning; Relational reinforcement learning; Reward function, Learning algorithms | Erscheinungsdatum: | 2007 | Herausgeber: | Springer Verlag | Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Volumen: | 4827 LNAI | Startseite: | 409 | Seitenende: | 419 | Zusammenfassung: | Relational reinforcement learning is the application of reinforcement learning to structured state descriptions. Model-based methods learn a policy based on a known model that comprises a description of the actions and their effects as well as the reward function. If the model is initially unknown, one might learn the model first and then apply the model-based method (indirect reinforcement learning). In this paper, we propose a method for model-learning that is based on a combination of several SVMs using graph kernels. Indeterministic processes can be dealt with by combining the kernel approach with a clustering technique. We demonstrate the validity of the approach by a range of experiments on various Blocksworld scenarios. © Springer-Verlag Berlin Heidelberg 2007. |
Beschreibung: | Conference of 6th Mexican International Conference on Artificial Intelligence, MICAI 2007 ; Conference Date: 4 November 2007 Through 10 November 2007; Conference Code:71204 |
ISBN: | 9783540766308 | ISSN: | 03029743 | DOI: | 10.1007/978-3-540-76631-5_39 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-38149024775&doi=10.1007%2f978-3-540-76631-5_39&partnerID=40&md5=f946c93e781e39de2c2ecf243fbdf36b |
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