DocumentCode
3479760
Title
HAM homomorphism for state abstraction
Author
Du Xiaoqin ; Qinghua, Li ; Jianjun, Han
Author_Institution
Wuhan Univ. of Sci., Wuhan Univ. of Sci. & Eng., Wuhan, China
fYear
2009
fDate
5-7 Aug. 2009
Firstpage
1184
Lastpage
1188
Abstract
In the HRL field, there are several main methods such as HAMs, options, MAXQ. A main problem that exists in HAMs is its joint state space consisting of the cross-product of the machine states in the HAM and the states in the original MDP, which can not be completely solved by a subroutine-based state abstraction method. This paper analyzes this problem in detail, provides formal definitions of homomorphism in HAMs and proves the invariance of the optimal solution for HAMs. Several typical examples are analyzed and evaluated. The results show that HAM homomorphism can conquer this problem.
Keywords
finite automata; learning (artificial intelligence); HAM homomorphism; hierarchical abstract machine; hierarchical reinforcement learning; state abstraction; Accelerated aging; Algorithms; Automation; Computer science; Educational institutions; Learning; Logistics; State-space methods; HAMs; Hierarchical Reinforcement Learning; Homomorphism;
fLanguage
English
Publisher
ieee
Conference_Titel
Automation and Logistics, 2009. ICAL '09. IEEE International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-4794-7
Electronic_ISBN
978-1-4244-4795-4
Type
conf
DOI
10.1109/ICAL.2009.5262638
Filename
5262638
Link To Document