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Knowledge Modeling by ELM in RL for SRHT Problem

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Hybrid Artificial Intelligent Systems (HAIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

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Abstract

Single Robot Hose Transport (SRHT) is a limit case of Linked Multicomponent Robotic Systems (L-MCRS), when one robot moves the tip of a hose to a desired position, while the other hose extreme is attached to a source position. Reinforcement Learning (RL) algorithms have been applied to learn autonomously the robot control with success. However, RL algorithms produce large and intractable data structures. This paper addresses the problem by learning an Extreme Learning Machine (ELM) from the state-action value Q-table, obtaining very relevant data reduction. In this paper we evaluate empirically a classification strategy to formulate ELM learning to provide approximations to the Q-table, obtaining very promising results.

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Acknowledgements

The research was supported by the Computational Intelligence Group, funded by the Basque Government with grant IT874-13.

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Authors and Affiliations

  1. Computational Intelligence Group, Basque Country University (UPV/EHU), Leioa, Spain

    Jose Manuel Lopez-Guede, Asier Garmendia & Manuel Graña

Authors
  1. Jose Manuel Lopez-Guede
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  2. Asier Garmendia
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  3. Manuel Graña
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Corresponding author

Correspondence to Jose Manuel Lopez-Guede .

Editor information

Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Lopez-Guede, J.M., Garmendia, A., Graña, M. (2016). Knowledge Modeling by ELM in RL for SRHT Problem. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

  • Online ISBN: 978-3-319-32034-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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