A two-step algorithm for learning from unspecific reinforcement
Reimer Kühn; Ion-Olimpiu Stamatescu
Журнал:
Journal of Physics A: Mathematical and General
Дата:
1999-08-06
Аннотация:
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. Morever, 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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