DocumentCode
396771
Title
Reinforcement learning in associative memory
Author
Zhu, Shaojuan ; Hammerstrom, Dan
Author_Institution
Dept. of ECE, Oregon Health & Sci. Univ., OR, USA
Volume
2
fYear
2003
fDate
20-24 July 2003
Firstpage
1346
Abstract
A reinforcement learning based associative memory structure (RLAM) is proposed. In this structure, a one-layer feed forward Palm [Palm, G., 1980] model is applied to the networks. Instead of batch training, an on-line learning method is used to construct the memory. The networks are trained interactively according to reinforcement learning, which is biologically plausible. The experiment results show that the networks converge and generalize well.
Keywords
Hebbian learning; cognitive systems; content-addressable storage; feedforward; robots; Hebbian learning; associative memory structure; cognitive process; feed forward Palm model; incremental learning; online learning method; reinforcement learning; robotic system; Associative memory; Biological system modeling; Biology; Brain modeling; Feeds; Learning systems; Muscles; Process planning; Robustness; Table lookup;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223891
Filename
1223891
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