Combining Hebbian and reinforcement learning in a minibrain model

Details Combining Hebbian and reinforcement learning in a minibrain model Combining Hebbian and reinforcement learning in a minibrain model

2003-01-31

arxiv.org/abs/cond-mat/0301627v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

Scientific paper

A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of `path interference', which makes that the neural net quickly forgets previously learned input-output relations is tackled by adding a Hebbian term (proportional to the learning rate $\eta$) to the reinforcement term (proportional to $\rho$) in the learning rule. It is shown that the number of learning steps is reduced considerably if $1/4 < \eta/\rho < 1/2$, i.e., if the Hebbian term is neither too small nor too large compared to the reinforcement term.

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