NDLI: A hybrid adaptive heuristic critic architecture for learning in large static search spaces

Content Provider	IEEE Xplore Digital Library
Author	Pipe, A.G. Jin, Y. Winfield, A.
Copyright Year	1994
Description	Author affiliation: Fac. of Eng., Univ. of the West of England, Bristol, UK (Pipe, A.G.; Jin, Y.; Winfield, A.)
Abstract	We present a hybrid Adaptive Heuristic Critic (AHC) architecture which learns an internal model of a maze environment through interaction with it. The adaptive critic's model is based around a radial basis function (RBF) neural network. Over successive trials the V-function is learned, a mapping between positions in the maze and their value. The model is based upon continuous valued spacial inputs and possesses the useful feature of "local generalisation" about the value associated with the region surrounding a position in the maze. An action policy allowing straight line movements to anywhere in the maze in a single step is adopted. This policy is implemented using a genetic algorithm (GA) which searches for an optimum movement at each time step. Although for computational convenience the GA is still based upon a discretized search of the maze-space the architecture should generalise well to evolutionary algorithms more suited to searching continuous spaces, allowing the concept of a discrete state to be dispensed with altogether.
Starting Page	237
Ending Page	242
File Size	603676
Page Count	6
File Format	PDF
ISBN	0780319907
ISSN	21589860
DOI	10.1109/ISIC.1994.367811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-08-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Genetic algorithms Computer architecture Mobile robots Hybrid intelligent systems Evolutionary computation Navigation Learning Dynamic programming Multilayer perceptrons
Content Type	Text
Resource Type	Article

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