A two step algorithm for learning from unspecific reinforcement

Reimer Kuehn; Ion-Olimpiu Stamatescu

doi:10.1088/0305-4470/32/31/301

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A two step algorithm for learning from unspecific reinforcement

Reimer Kuehn·Ion-Olimpiu Stamatescu

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Abstract

We study a simple learning model based on the Hebb rule to cope with "delayed", unspecific reinforcement. In spite of the unspecific nature of the information-feedback, convergence to asymptotically perfect generalization is observed, with a rate depending, however, in a non- universal way on learning parameters. Asymptotic convergence can be as fast as that of Hebbian learning, but may be slower. Moreover, for a certain range of parameter settings, it depends on initial conditions whether the system can reach the regime of asymptotically perfect generalization, or rather approaches a stationary state of poor generalization.

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BibTeX RIS

Reimer Kuehn, Ion-Olimpiu Stamatescu. 1999-07-16. A two step algorithm for learning from unspecific reinforcement. https://doi.org/10.1088/0305-4470%2F32%2F31%2F301

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