Research of a parallel learning adaptive dynamic programming based on genetic algorithms

Zhenyu Wang*, Yaping Dai, Yuan Yao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In adaptive dynamic programming (ADP), the utility function is always completely designed by experience which cannot evaluate system cost very well. A novel adaptive dynamic programming based on genetic algorithms (GAADP) method is proposed with global searching and fast learning speed. First, a normal utility function of ADP was designed according to the error between current states and expected values. Second, genetic algorithms (GAs) were used to search for the optimal parameters of utility function in ADP. Finally, we employed GAADP on an inverted pendulum system control. The simulation experiments indicated that GAADP method can increase the learning speed and easily achieve the balancing state. The learning speed doubled than general ADP method, meanwhile the control performance of successful trials also improved.

Original languageEnglish
Title of host publication2010 2nd International Conference on Communication Systems, Networks and Applications, ICCSNA 2010
Pages350-353
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 2nd International Conference on Communication Systems, Networks and Applications, ICCSNA 2010 - Hong Kong, China
Duration: 29 Jun 20101 Jul 2010

Publication series

Name2010 2nd International Conference on Communication Systems, Networks and Applications, ICCSNA 2010
Volume1

Conference

Conference2010 2nd International Conference on Communication Systems, Networks and Applications, ICCSNA 2010
Country/TerritoryChina
CityHong Kong
Period29/06/101/07/10

Keywords

  • Adaptive dynamic programming
  • Genetic algorithms
  • Inverted pendulum control
  • Utility function

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