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1 Ergebnisse
1
Reinforcement Learning Model With Dynamic State Space Teste..:
Katakura, Tokio
;
Yoshida, Mikihiro
;
Hisano, Haruki
..
Frontiers in Computational Neuroscience. 15 (2022) - p. , 2022
Link:
https://doi.org/10.3389/fncom.2021.784592
RT Journal T1
Reinforcement Learning Model With Dynamic State Space Tested on Target Search Tasks for Monkeys: Self-Determination of Previous States Based on Experience Saturation and Decision Uniqueness
UL https://suche.suub.uni-bremen.de/peid=cr-10.3389_fncom.2021.784592&Exemplar=1&LAN=DE A1 Katakura, Tokio A1 Yoshida, Mikihiro A1 Hisano, Haruki A1 Mushiake, Hajime A1 Sakamoto, Kazuhiro PB Frontiers Media SA YR 2022 SN 1662-5188 JF Frontiers in Computational Neuroscience VO 15 LK http://dx.doi.org/https://doi.org/10.3389/fncom.2021.784592 DO https://doi.org/10.3389/fncom.2021.784592 SF ELIB - SuUB Bremen
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