An analysis of case-based value function approximation by approximating state transition graphs
Autor(en): | Gabel, T. Riedmiller, M. |
Stichwörter: | Adaptive systems; Approximation theory; Data acquisition; Graph theory, Adaptive agents; Approximation mechanisms; Value functions, Case based reasoning | Erscheinungsdatum: | 2007 | Herausgeber: | Springer Verlag | Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Volumen: | 4626 LNAI | Startseite: | 344 | Seitenende: | 358 | Zusammenfassung: | We identify two fundamental points of utilizing CBR for an adaptive agent that tries to learn on the basis of trial and error without a model of its environment. The first link concerns the utmost efficient exploitation of experience the agent has collected by interacting within its environment, while the second relates to the acquisition and representation of a suitable behavior policy. Combining both connections, we develop a state-action value function approximation mechanism that relies on case-based, approximate transition graphs and forms the basis on which the agent improves its behavior. We evaluate our approach empirically in the context of dynamic control tasks. © Springer-Verlag Berlin Heidelberg 2007. |
Beschreibung: | Conference of 7th International Conference on Case-Based Reasoning, ICCBR 2007 ; Conference Date: 13 August 2007 Through 16 August 2007; Conference Code:71086 |
ISBN: | 9783540741381 | ISSN: | 03029743 | DOI: | 10.1007/978-3-540-74141-1_24 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049072309&doi=10.1007%2f978-3-540-74141-1_24&partnerID=40&md5=519f33efb64b160dae50cfc7534db9ef |
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