DocumentCode
2767533
Title
An Improved Minibrain That Learns Through Both Positive and Negative Feedback
Author
Wee Phua, Chee ; Blair, Alan
Author_Institution
Univ. of New South Wales, Sydney
fYear
0
fDate
0-0 0
Firstpage
812
Lastpage
819
Abstract
A new reinforcement learned neural network, that follows the ideas of the minibrain network but includes exploration and learns through both positive and negative feedback, is proposed. The proposed ReL network is evaluated against the minibrain network in the n times n grid world domain and the taxi domain and is shown to perform significantly better than the minibrain network.
Keywords
feedback; learning (artificial intelligence); neural nets; ReL network; minibrain; negative feedback; positive feedback; reinforcement learned neural network; Biological neural networks; Biological system modeling; Brain modeling; Learning; Negative feedback; Neural networks; Parallel processing; Performance evaluation; Road vehicles; Vehicle driving;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9490-9
Type
conf
DOI
10.1109/IJCNN.2006.246768
Filename
1716179
Link To Document