Factored temporal difference learning in the new ties environment

Gyenes Viktor and Bontovics Ákos and Lőrincz András: Factored temporal difference learning in the new ties environment. In: Acta cybernetica, (18) 4. pp. 651-668. (2008)

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Although reinforcement learning is a popular method for training an agent for decision making based on rewards, well studied tabular methods are not applicable for large, realistic problems. In this paper, we experiment with a factored version of temporal difference learning, which boils down to a linear function approximation scheme utilising natural features coming from the structure of the task. We conducted experiments in the New Ties environment, which is a novel platform for multi-agent simulations. We show that learning utilising a factored representation is effective even in large state spaces, furthermore it outperforms tabular methods even in smaller problems both in learning speed and stability, because of its generalisation capabilities.

Item Type: Article
Journal or Publication Title: Acta cybernetica
Date: 2008
Volume: 18
Number: 4
ISSN: 0324-721X
Page Range: pp. 651-668
Language: English
Place of Publication: Szeged
Event Title: Symposium of Young Scientists on Intelligent Systems (2.) (2007) (Budapest)
Related URLs: http://acta.bibl.u-szeged.hu/38526/
Uncontrolled Keywords: Számítástechnika, Kibernetika
Additional Information: Bibliogr.: p. 667-668. ; összefoglalás angol nyelven
Subjects: 01. Natural sciences
01. Natural sciences > 01.02. Computer and information sciences
Date Deposited: 2016. Oct. 15. 12:25
Last Modified: 2022. Jun. 16. 14:51
URI: http://acta.bibl.u-szeged.hu/id/eprint/12840

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