• DocumentCode
    2801787
  • Title

    Motor primitive discovery

  • Author

    Thomas, P.S. ; Barto, A.G.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Massachusetts Amherst, Amherst, MA, USA
  • fYear
    2012
  • fDate
    7-9 Nov. 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    We present a method for autonomous on-line discovery of motor primitives for Markov decision processes with high-dimensional continuous action spaces. These biologically-inspired motor primitives require overhead to compute but form a compressed representation of the action set that allows for improved performance on subsequent learning tasks that have similar dynamics.
  • Keywords
    Markov processes; learning (artificial intelligence); neurophysiology; Markov decision process; autonomous online discovery; compressed representation; continuous action spaces; motor primitive discovery; Aerospace electronics; Animals; Joints; Muscles; Optimization; Search problems; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Development and Learning and Epigenetic Robotics (ICDL), 2012 IEEE International Conference on
  • Conference_Location
    San Diego, CA
  • Print_ISBN
    978-1-4673-4964-2
  • Electronic_ISBN
    978-1-4673-4963-5
  • Type

    conf

  • DOI
    10.1109/DevLrn.2012.6400845
  • Filename
    6400845
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2801787