Hyperbolically discounted temporal difference learning.

Neural Computation
William H Alexander, Joshua W Brown

Abstract

Hyperbolic discounting of future outcomes is widely observed to underlie choice behavior in animals. Additionally, recent studies (Kobayashi & Schultz, 2008) have reported that hyperbolic discounting is observed even in neural systems underlying choice. However, the most prevalent models of temporal discounting, such as temporal difference learning, assume that future outcomes are discounted exponentially. Exponential discounting has been preferred largely because it can be expressed recursively, whereas hyperbolic discounting has heretofore been thought not to have a recursive definition. In this letter, we define a learning algorithm, hyperbolically discounted temporal difference (HDTD) learning, which constitutes a recursive formulation of the hyperbolic model.

References

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Apr 25, 2008·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Nicolas SchweighoferKenji Doya
Aug 1, 2008·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Shunsuke Kobayashi, Wolfram Schultz

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Citations

Sep 8, 2011·Proceedings of the National Academy of Sciences of the United States of America·Kazuki EnomotoMinoru Kimura
May 17, 2015·Neuroscience and Biobehavioral Reviews·Chong ChenIchiro Kusumi
Sep 11, 2012·Neural Networks : the Official Journal of the International Neural Network Society·Yoshiya Yamaguchi, Yutaka Sakai
Mar 25, 2014·Frontiers in Behavioral Neuroscience·Giles W StoryRaymond J Dolan
Sep 21, 2018·Frontiers in Psychology·Johan E KortelingAlexander Toet
Aug 31, 2021·Frontiers in Psychology·J E Hans KortelingAlexander Toet

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