Title :
Research on robot motion control based on local weighted kNN-TD reinforcement learning
Author :
Han, Fei ; Jin, Lu ; Yang, Yuequan ; Cao, Zhiqiang ; Zhang, Tianping
Author_Institution :
Coll. of Inf. Eng., Yangzhou Univ., Yangzhou, China
Abstract :
Learning is an important capability for an individual robot, which provides an effective way for understanding, planning, and decision-making in a complex environment. For robot motion control, a local weighted k-nearest neighbors states selection method based on environment information and task information is presented. Based on this method, TD reinforcement learning algorithm is combined to reduce the misclassified probability of kNN-TD method, which is finally verified by the simulations.
Keywords :
decision making; learning (artificial intelligence); mobile robots; motion control; probability; decision making; environment information; local weighted k-nearest neighbor states selection method; local weighted kNN-TD reinforcement learning; misclassified probability reduction; robot motion control; task information; Automation; Computer science; Educational institutions; Learning; Markov processes; Robot motion; k-nearest neighbors; motion control; reinforcement learning;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
DOI :
10.1109/WCICA.2012.6359080
