• DocumentCode
    3276331
  • Title

    Reinforcement Learning for Image Understanding

  • Author

    Fengtao Xiang ; Zhengzhi Wang ; Xingsheng Yuan

  • Author_Institution
    Coll. of Electromech. Eng. & Autom., Nat. Univ. of Defense Technol. Changsha, Changsha, China
  • fYear
    2013
  • fDate
    16-18 Jan. 2013
  • Firstpage
    1102
  • Lastpage
    1105
  • Abstract
    Reinforcement Learning is one of the hottest issues in current AI research fields. It´s a effective method in solving some machine learning problems. It´s high efficiency, simpler programming, easier understanding, and better performance. Here I will share my understanding. If there are something wrong, thanks for correct. In reinforcement learning, the learner is a decision-making agent that takes actions in an environment and receives reward (or penalty) for its actions in trying to solve a problem. After a set of trial-and-error runs, it should learn the best policy, which is the sequence of actions that maximize the total reward.
  • Keywords
    image processing; learning (artificial intelligence); decision-making agent; image understanding; machine learning; reinforcement learning; Hidden Markov models; Image segmentation; Learning; Learning systems; Machine learning; Markov processes; Robots; Artificial Intelligence; Image Understanding; Machine Learning; Reinforcement Learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4673-4893-5
  • Type

    conf

  • DOI
    10.1109/ISDEA.2012.261
  • Filename
    6456071