• DocumentCode
    1810776
  • Title

    An Analysis and Hierarchical Decomposition for HAMs

  • Author

    Du Xiaoqin ; Qinghua, Li ; Jianjun, Han

  • Author_Institution
    Coll. of Comput. Sci., Wuhan Univ. of Sci. & Eng., Wuhan, China
  • Volume
    2
  • fYear
    2009
  • fDate
    29-31 Aug. 2009
  • Firstpage
    1050
  • Lastpage
    1054
  • Abstract
    In the HRL field, there are several main methods such as HAMs, options, MAXQ. These methods all rely on the theory of SMDPs. However, SMDPs does not specify how the overall task can be decomposed into a collection of subtasks. This paper introduces the concept of ldquopolicy-coupledrdquo SMDPs into HAMs. It defines the concept of HAM-decomposable and makes the relations among the HAM machine, HAM-decomposable, and ldquopolicy-coupledrdquo SMDPs clear. It also proves that HAMs is suitable for solving the ldquopolicy-coupledrdquo SMDPs problem. Based on these, this paper gives a method for hierarchical decomposition on a class of ldquopolicy-coupledrdquo SMDPs with a DAG call graph and presents a precondition that can be used for determining whether or not can generate a valid hierarchical decomposition. Lastly, a typical experiment is tested for illustrating the characteristics of this method.
  • Keywords
    Markov processes; finite automata; learning (artificial intelligence); MAXQ; call graph; hierarchical abstract machine; hierarchical decomposition; hierarchical reinforcement learning; semiMarkov decision process; Algorithms; Computer science; Concrete; Educational institutions; Learning; State-space methods; Testing; HAMs; Hierarchical Reinforcement Learning; Reinforcement Learning; SMDPs;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Science and Engineering, 2009. CSE '09. International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    978-1-4244-5334-4
  • Electronic_ISBN
    978-0-7695-3823-5
  • Type

    conf

  • DOI
    10.1109/CSE.2009.22
  • Filename
    5283538