1999 | OriginalPaper | Buchkapitel
Efficient State-Space Representation by Neural Maps for Reinforcement Learning
verfasst von : Michael Herrmann, Ralf Der
Erschienen in: Classification in the Information Age
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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For some reinforcement learning algorithms the optimality of the generated strategies can be proven. In practice, however, restrictions in the number of training examples and computational resources corrupt optimality. The efficiency of the algorithms depends strikingly on the formulation of the task, including the choice of the learning parameters and the representation of the system states. We propose here to improve the learning efficiency by an adaptive classification of the system states which tends to group together states if they are similar and aquire the same action during learning. The approach is illustrated by two simple examples. Two further applications serve as a test of the proposed algorithm.