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Concurrent Modular Q-Learning with Local Rewards on Linked Multi-Component Robotic Systems

  • Borja Fernandez-Gauna
  • Jose Manuel Lopez-Guede
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

Applying conventional Q-Learning to Multi-Component Robotic Systems (MCRS) increasing the number of components produces an exponential growth of state storage requirements. Modular approaches limit the state size growth to be polynomial on the number of components, allowing more manageable state representation and manipulation. In this article, we advance on previous works on a modular Q-learning approach to learn the distributed control of a Linked MCRS. We have chosen a paradigmatic application of this kind of systems using only local rewards: a set of robots carrying a hose from some initial configuration to a desired goal. The hose dynamics are simplified to be a distance constraint on the robots positions.

Keywords

Constraint Module Negative Reward Goal Module Local Reward Veto System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Borja Fernandez-Gauna
    • 1
  • Jose Manuel Lopez-Guede
    • 1
  • Manuel Graña
    • 1
  1. 1.University of the Basque Country(UPV/EHU)Spain

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