Foraging decisions as multi-armed bandit problems: applying reinforcement learning algorithms to foraging data

DOI10.1016/J.JTBI.2019.02.002zbMath1409.92276OpenAlexW2914909556WikidataQ91421791 ScholiaQ91421791MaRDI QIDQ1730108

Juliano Morimoto

Publication date: 11 March 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2019.02.002

zbMATH Keywords

multi-armed banditreinforcement learning algorithmsforaging decisions

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic games; gambling (91A60) Animal behavior (92D50)

Uses Software

• ggplot2
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Cites Work

• Optimal patch use in a stochastic environment
• Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits
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