On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning Using a Local Third Factor
Abstract
In this theoretical contribution we provide mathematical proof that two of the most important classes of network learning - correlation-based differential Hebbian learning and reward-based temporal difference learning - are asymptotically equivalent when timing the learning with a local modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation based perspective that is more closely related to the biophysics of neurons.
Cite
Text
Kolodziejski et al. "On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning Using a Local Third Factor." Neural Information Processing Systems, 2008.Markdown
[Kolodziejski et al. "On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning Using a Local Third Factor." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/kolodziejski2008neurips-asymptotic/)BibTeX
@inproceedings{kolodziejski2008neurips-asymptotic,
title = {{On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning Using a Local Third Factor}},
author = {Kolodziejski, Christoph and Porr, Bernd and Tamosiunaite, Minija and Wörgötter, Florentin},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {857-864},
url = {https://mlanthology.org/neurips/2008/kolodziejski2008neurips-asymptotic/}
}