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《Techniques of Automation and Applications》 2006-11
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Q-Learning based on the Experience knowledge

SONG Qing-kun,HU Zi-ying(Automation,Harbin Univ.Sci.Tech.,Harbin 150080,China)  
In order to enhance the study speed and the convergence rate of Q-learning algorithm,an algorithm that based on the experience knowledge about environment is proposed.Based on the experienced information function,the agent can learn the system model and avoid the repeated learning.Compared with the standard Q-learning,the results showed that the proposed algorithm has faster speed to converge and better performance.
【CateGory Index】: TP181
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