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
Link To Document