DocumentCode :
3510304
Title :
Strategy Entropy as a Measure of Strategy Convergence in Reinforcement Learning
Author :
Zhuang, Xiaodong ; Chen, Zhuo
Author_Institution :
Electron. & Eng. Dept., Qingdao Univ., Qingdao
fYear :
2008
fDate :
1-3 Nov. 2008
Firstpage :
81
Lastpage :
84
Abstract :
The concept of entropy is introduced into reinforcement learning. The definitions of the local and global strategy entropy are presented. The global strategy entropy is experimentally proved to be the quantitative problem-independent measure of the strategypsilas convergence degree. The experimental results show that the learning based on the local strategy entropy improves the learning performance.
Keywords :
entropy; learning (artificial intelligence); global strategy entropy; local strategy entropy; quantitative problem-independent measure; reinforcement learning; strategy convergence; Automation; Control systems; Convergence; Educational institutions; Entropy; Information science; Intelligent networks; Intelligent systems; Learning; Stochastic processes; Adaptive Learning Rate; Reinforcement Learning; Strategy Convergence; Strategy Entropy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Networks and Intelligent Systems, 2008. ICINIS '08. First International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3391-9
Electronic_ISBN :
978-0-7695-3391-9
Type :
conf
DOI :
10.1109/ICINIS.2008.94
Filename :
4683173
Link To Document :
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