Temporal-difference reinforcement learning with distributed representations.

Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the beli...

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Autores principales: Zeb Kurth-Nelson, A David Redish
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2009
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Acceso en línea:https://doaj.org/article/10b71edf81334d619f75d3ba97df1661
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