Abstract
We present the first results obtained from two implementations of a hybrid architecture which balances exploration and exploitation to solve mazes with continuous search spaces. In both cases the critic is based around a Radial Basis Function (RBF) Neural Network which uses Temporal Difference learning to acquire a continuous valued internal model of the environment through interaction with it. Also in both cases an Evolutionary Algorithm is employed in the search policy for each movement. In the first implementation a Genetic Algorithm (GA) is used, and in the second an Evolutionary Strategy (ES). Over successive trials the maze solving agent learns the V-function, a mapping between real numbered positions in the maze and the value of being at those positions.
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© 1994 Springer-Verlag Berlin Heidelberg
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Pipe, A.G., Fogarty, T.C., Winfield, A. (1994). Hybrid adaptive heuristic critic architectures for learning in mazes with continuous search spaces. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_291
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DOI: https://doi.org/10.1007/3-540-58484-6_291
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