• DocumentCode
    2717600
  • Title

    Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark

  • Author

    Riedmiller, Martin ; Peters, Jan ; Schaal, Stefan

  • Author_Institution
    Neuroinformatics Group, Osnabrueck Univ.
  • fYear
    2007
  • fDate
    1-5 April 2007
  • Firstpage
    254
  • Lastpage
    261
  • Abstract
    In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, ´vanilla´ policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease
  • Keywords
    finite difference methods; gradient methods; learning (artificial intelligence); search problems; cart pole regulator benchmark; finite difference gradients; model-free policy gradient methods; natural policy gradients; parameterized policy search algorithms; vanilla policy gradients; Dynamic programming; Finite difference methods; Gradient methods; Learning; Legged locomotion; Motor drives; Optimization methods; Regulators; Solids; Stochastic processes;
  • 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.2007.368196
  • Filename
    4220841