• DocumentCode
    463398
  • Title

    Hierarchical Reinforcement Learning with OMQ

  • Author

    Shen, Jing ; Liu, Haibo ; Gu, Guochang

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Harbin Eng. Univ.
  • Volume
    1
  • fYear
    2006
  • fDate
    17-19 July 2006
  • Firstpage
    584
  • Lastpage
    588
  • Abstract
    A novel method of hierarchical reinforcement learning, named OMQ, by integrating options into MAXQ is presented. In OMQ, the MAXQ is used as basic framework to design hierarchies experientially and learn online, and the option is used to construct hierarchies automatically. The performance of OMQ is demonstrated in taxi domain and compared with Option and MAXQ. The simulation results show that the OMQ is more practical than option and MAXQ in partial known environment
  • Keywords
    learning (artificial intelligence); OMQ; hierarchical reinforcement learning; Aggregates; Automata; Cognitive informatics; Computer science; Design engineering; Encoding; Formal specifications; Learning; Navigation; State-space methods; MAXQ; Option; hierarchical reinforcement learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    1-4244-0475-4
  • Type

    conf

  • DOI
    10.1109/COGINF.2006.365550
  • Filename
    4216467
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=463398