• DocumentCode
    2717114
  • Title

    Particle Swarn Optimized Adaptive Dynamic Programming

  • Author

    Zhao, Dongbin ; Yi, Jianqiang ; Liu, Derong

  • Author_Institution
    Key Lab. of Complex Syst. & Intelligence Sci., Chinese Acad. of Sci., Beijing
  • fYear
    2007
  • fDate
    1-5 April 2007
  • Firstpage
    32
  • Lastpage
    37
  • Abstract
    Particle swarm optimization is used for the training of the action network and critic network of the adaptive dynamic programming approach. The typical structures of the adaptive dynamic programming and particle swarm optimization are adopted for comparison to other learning algorithms such as gradient descent method. Besides simulation on the balancing of a cart pole plant, a more complex plant pendulum robot (pendubot) is tested for the learning performance. Compared to traditional adaptive dynamic programming approaches, the proposed evolutionary learning strategy is verified as faster convergence and higher efficiency. Furthermore, the structure becomes simple because the plant model does not need to be identified beforehand
  • Keywords
    dynamic programming; evolutionary computation; learning (artificial intelligence); particle swarm optimisation; robots; adaptive dynamic programming; evolutionary learning; learning algorithms; particle swarm optimization; plant pendulum robot; pole balancing; Adaptive systems; Backpropagation; Cost function; Dynamic programming; Evolutionary computation; Learning; Neural networks; Particle swarm optimization; Robots; Testing; adaptive dynamic programming; particle swarm optimization; pendubot; pole balancing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on
  • Conference_Location
    Honolulu, HI
  • Print_ISBN
    1-4244-0706-0
  • Type

    conf

  • DOI
    10.1109/ADPRL.2007.368166
  • Filename
    4220811