title: Hyperbolically discounted temporal difference learning.
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.
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA. wialexan@indiana.edu
authors.forename: William H
authors.initials: WH
authors.lastname: Alexander
authors.forename: Joshua W
authors.initials: JW
authors.lastname: Brown
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D055641:Mathematical Concepts; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D013997:Time Factors; D013998:Time Perception / Q000502:physiology*