NDLI: Realization of reinforcement learning using multi-winners KFM associative memory

Content Provider	IEEE Xplore Digital Library
Author	Ikeya, T. Osana, Y.
Copyright Year	2009
Description	Author affiliation: Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology, Hachioji, Tokyo (Ikeya, T.) \|\| School of Computer Science, Tokyo University of Technology, Hachioji, Tokyo (Osana, Y.)
Abstract	In this paper, we propose a multi-winners Koho-nen Feature Map (KFM) associative memory, and apply it to reinforcement learning. In the proposed model, the patterns are trained by the successive learning algorithm of the conventional KFM associative memory. The proposed model has two kinds of recall methods, and one of them is selected based on whether or not the input pattern is the trained pattern. In one of the recall method, the output of the input/output layer is calculated as the weighted sum of the connection weights of the fired neuron in the map layer according to their internal states. In the other one method, one of the weight-fixed neurons are selected in the map layer, and the output of the input/output layer is determined based on the connection weights of the neuron. In the reinforcement learning, the proposed model can select the trained corresponding action if the known environment is given. Moreover, it can select appropriate action based on the trained similar situation even if the unknown environment is given.
Starting Page	3806
Ending Page	3811
File Size	240778
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346624
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Neurons Biological neural networks Information processing Machine learning Dynamic programming Cybernetics USA Councils Computer science Machine learning algorithms Reinforcement Learning Kohonen Feature Map(KFM) Associative Memory Successive Learning
Content Type	Text
Resource Type	Article

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