DocumentCode
352729
Title
Genetic TD(λ) learning algorithm for policy evaluation problems
Author
Xin, Xu ; Han-gen, He
Author_Institution
Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha, China
Volume
1
fYear
2000
fDate
2000
Firstpage
500
Abstract
In this paper, tabular TD(λ) learning algorithm is combined with genetic algorithm (GA) to solve stochastic policy evaluation problems. Unlike conventional TD(λ) algorithm which has fixed control parameters, the proposed genetic TD(λ) algorithm makes use of GA to optimize the control parameters while evaluating stochastic policies. Simulated experiments on stochastic policy evaluation problems show that genetic TD(λ) algorithms not only realize the auto-tuning of control parameters but also have improved performance
Keywords
genetic algorithms; learning (artificial intelligence); learning systems; auto-tuning; genetic algorithm; optimisation; reinforcement learning; stochastic policy evaluation; Algorithm design and analysis; Automatic control; Computer science; Convergence; Electronic mail; Genetic algorithms; Helium; Machine learning; Machine learning algorithms; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location
Hefei
Print_ISBN
0-7803-5995-X
Type
conf
DOI
10.1109/WCICA.2000.860017
Filename
860017
Link To Document