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Mobile Robot Navigation Using Reinforcement Learning Based on Neural Network with Short Term Memory

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Advanced Intelligent Computing (ICIC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6838))

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Abstract

In this paper we propose a novel bio-inspired model of a mobile robot navigation system. The novelty of our work consists in combining short term memory and online neural network learning using history of events stored in this memory. The neural network is trained with a modified error back propagation algorithm that utilizes reward and punishment principal while interacting with the environment.

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Author information

Authors and Affiliations

  1. Dept. of Production Automation in Machine Engineering, Novosibirsk State Technical University, Karl Marx av., 20, Novosibirsk, 630092, Russia

    Andrey V. Gavrilov

  2. School of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education, 1800 Chungjeol, Byeongcheon, Dongnam, Cheonan, 330-708, Korea

    Artem Lenskiy

Authors
  1. Andrey V. Gavrilov
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  2. Artem Lenskiy
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Editor information

De-Shuang Huang Yong Gan Vitoantonio Bevilacqua Juan Carlos Figueroa

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Gavrilov, A.V., Lenskiy, A. (2011). Mobile Robot Navigation Using Reinforcement Learning Based on Neural Network with Short Term Memory. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_28

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  • DOI: https://doi.org/10.1007/978-3-642-24728-6_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24727-9

  • Online ISBN: 978-3-642-24728-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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