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Attentional Action Selection Using Reinforcement Learning

  • Dario Di Nocera
  • Alberto Finzi
  • Silvia Rossi
  • Mariacarla Staffa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7426)

Abstract

Reinforcement learning is typically used to model and optimize action selection strategies, in this work we deploy it to optimize attentional allocation strategies while action selection is obtained as a side effect. We present a reinforcement learning approach to attentional allocation and action selection in a behavior-based robotic systems. We detail our attentional allocation mechanisms describing the reinforcement learning problem and analysing its performance in a survival domain.

Keywords

attention allocation reinforcement learning action selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dario Di Nocera
    • 1
  • Alberto Finzi
    • 1
  • Silvia Rossi
    • 1
  • Mariacarla Staffa
    • 2
  1. 1.Dipartimento di Scienze FisicheUniversity of Naples “Federico II”NaplesItaly
  2. 2.Dipartimento di Informatica e SistemisticaUniversity of Naples “Federico II”NaplesItaly

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