Preprint available - reinforcement learning for continuous functions

[D.Gorse@cs.ucl.ac.uk]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/gorse.reinforce.ps.Z

The file gorse.reinforce.ps.Z is now available for copying from the 
Neuroprose archive. This is a 6 page paper, submitted to WCNN '94
San Diego. A longer and more detailed paper describing this work is in
preparation and will be available soon.
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		A PULSE-BASED REINFORCEMENT ALGORITHM 
		  FOR LEARNING CONTINUOUS FUNCTIONS

			      D Gorse
		     Department of Computer Science
	 University College, Gower Street, London WC1E 6BT, UK

			    J G Taylor
		    Department of Mathematics
			   T G Clarkson
	  Department of Electrical and Electronic Engineering
	      King's College, Strand, London WC2R 2LS, UK

ABSTRACT:
An algorithm is presented which allows continuous functions to be learned
by a neural network using spike-based reinforcement learning. Both the
mean and the variance of the weights are changed during training; the
latter is accomplished by manipulating the lengths of the spike trains
used to represent real-valued quantities. The method is here applied to
the probabilistic RAM (pRAM) model, but it may be adapted for use with any
pulse-based stochastic model in which individual weights behave as 
random variables.

Denise Gorse (D.Gorse at cs.ucl.ac.uk)
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