DocumentCode
529477
Title
Threshold learning in the improved penalty avoiding rational policy making algorithm
Author
Miyazaki, Kazuteru ; Kobayashi, Ryouhei ; Kobayashi, Hiroaki
Author_Institution
Dept. of Assessment & Res. for Degree Awarding, Univ. Evaluation, Tokyo, Japan
fYear
2010
fDate
18-21 Aug. 2010
Firstpage
3240
Lastpage
3245
Abstract
The penalty avoiding rational policy making algorithm (PARP) previously improved to save memory and cope with uncertainty, i.e., Improved PARP (IPARP). The efficiency of IPARP is influenced by threshold of a penalty rule or a penalty basis function γ significantly. In this paper, we propose a technique for learning γ. We show the effectiveness of our proposal using a soccer game task called “Keepaway”.
Keywords
game theory; learning (artificial intelligence); PARP; keepaway; penalty avoiding rational policy making algorithm; soccer game; threshold learning; Function approximation; Games; Machine learning; Memory management; Proposals; Tiles; Uncertainty; Exploitation-oriented Learning XoL; Improved PARP; Keepaway Task; Reinforcement Learning; Threshold Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
SICE Annual Conference 2010, Proceedings of
Conference_Location
Taipei
Print_ISBN
978-1-4244-7642-8
Type
conf
Filename
5602796
Link To Document