DocumentCode
2717114
Title
Particle Swarn Optimized Adaptive Dynamic Programming
Author
Zhao, Dongbin ; Yi, Jianqiang ; Liu, Derong
Author_Institution
Key Lab. of Complex Syst. & Intelligence Sci., Chinese Acad. of Sci., Beijing
fYear
2007
fDate
1-5 April 2007
Firstpage
32
Lastpage
37
Abstract
Particle swarm optimization is used for the training of the action network and critic network of the adaptive dynamic programming approach. The typical structures of the adaptive dynamic programming and particle swarm optimization are adopted for comparison to other learning algorithms such as gradient descent method. Besides simulation on the balancing of a cart pole plant, a more complex plant pendulum robot (pendubot) is tested for the learning performance. Compared to traditional adaptive dynamic programming approaches, the proposed evolutionary learning strategy is verified as faster convergence and higher efficiency. Furthermore, the structure becomes simple because the plant model does not need to be identified beforehand
Keywords
dynamic programming; evolutionary computation; learning (artificial intelligence); particle swarm optimisation; robots; adaptive dynamic programming; evolutionary learning; learning algorithms; particle swarm optimization; plant pendulum robot; pole balancing; Adaptive systems; Backpropagation; Cost function; Dynamic programming; Evolutionary computation; Learning; Neural networks; Particle swarm optimization; Robots; Testing; adaptive dynamic programming; particle swarm optimization; pendubot; pole balancing;
fLanguage
English
Publisher
ieee
Conference_Titel
Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0706-0
Type
conf
DOI
10.1109/ADPRL.2007.368166
Filename
4220811
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