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Paralled reinforcement learning using multiple reward signals
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
2006 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 69, no 16/18, 2171-2179 p.Article in journal (Refereed) Published
Abstract [en]

Reinforcement learning is a quite general learning paradigm that can be used to solve a large set of problems. For complex problems it has been shown that by using task decomposition it may be possible for the system to learn faster. One common approach is to construct systems with multiple modules, where each module learns a sub-task. We present a parallel learning method for agents with an actor–critic architecture based on artificial neural networks. The agents have multiple modules, where the modules can learn in parallel to further increase learning speed. Each module solves a sub-problem and receives its own separate reward signal with all modules trained concurrently. We use the method on a grid world navigation task and show that parallel learning can significantly reduce learning time.

Place, publisher, year, edition, pages
2006. Vol. 69, no 16/18, 2171-2179 p.
Keyword [en]
Neural networks, Modularity, Reinforcement learning
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-8800DOI: 10.1016/j.neucom.2005.07.008ISI: 000241044700033Scopus ID: 2-s2.0-33748416863OAI: oai:DiVA.org:kth-8800DiVA: diva2:14234
Note

Updated from submitted to published, 20111216. QC 20111216

Available from: 2005-11-23 Created: 2005-11-23 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Towards a framework for reinforcement learning with artificial neural networks
Open this publication in new window or tab >>Towards a framework for reinforcement learning with artificial neural networks
2004 (English)Licentiate thesis, comprehensive summary (Other scientific)
Place, publisher, year, edition, pages
Stockholm: Numerisk analys och datalogi, 2004
Series
Trita-NA, ISSN 0348-2952 ; 0437
Keyword
aritficial neural networks, reinforcement learning, BCPNN, modularity, genetic algorithms
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-500 (URN)91-7283-928-7 (ISBN)
Presentation
2004-12-10, E32, KTH, Lindstedsvägen 3, Stockholm, 13:00
Available from: 2005-11-23 Created: 2005-11-23 Last updated: 2012-03-21

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  • apa
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