Treffer: Swarm robots reinforcement learning convergence accuracy-based learning classifier systems with gradient descent (XCS-GD)

Title:
Swarm robots reinforcement learning convergence accuracy-based learning classifier systems with gradient descent (XCS-GD)
Source:
Neural computing & applications (Print). 25(2):263-268
Publisher Information:
London: Springer, 2014.
Publication Year:
2014
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Information Engineering. Zhengzhou Chenggong University of Finance and Economics, Zhengzhou 451200, China
ISSN:
0941-0643
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28607216
Database:
PASCAL Archive

Weitere Informationen

This paper presented a novel approach accuracy-based learning classifier system with gradient descent (XCS-GD) to research on swarm robots reinforcement learning convergence. XCS-GD combines covering operator and genetic algorithm. XCS-GD is responsible for adjusting precision and reducing search space according to some reward obtained from the environment, XCS-GD's innovation discovery component is responsible for discovering new better reinforcement learning rules. The experiment and simulation showed that XCS-GD approach can achieve convergence very quickly in swarm robots reinforcement learning.