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A Reinforcement Learning Hebb Net

Author: Marcus Hutter (1990)
Superviser: Gerhard Weiß
Reference: TU-München, Fortgeschrittenen-Praktikum
Department: Theoretische Informatik und Grundlagen der KI
Subj-class: Artificial Intelligence; Learning
ACM-class: I.2; I.2.6; I.2.8; F.1.3
Comments: 30 pages in German with Listing

Keywords: Neural nets, reinforcement/unsupervised/supervised learning, Hebb nets, Hebb learning rule, XNOR.

Abstract: This Fopra is motivated by the following observations about human learning and about human neural information processing. On the one side humans are able to learn supervised, unsupervised and by reinforcement, on the other side there is no neural distinction between informative, uninformative and evaluative feedback. Furthermore, the Hebb learning rule is the only biological inspired learning mechanism. If the human brain is indeed a Hebb net this would imply that Hebb nets are able to learn by reinforcement. The goal of this Fopra is to investigate whether and how Hebb nets could be used for reinforcement learning. It is shown that Hebb nets with a suitable prior net topology can indeed learn, at least simple tasks, by reinforcement.

Fopra: PostScript, PDF, html, original, Pascal listing, simulation protocol

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