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Wolfram Barfuss

Showing results (1-10 of 14) with videos related to

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Neural Computing & Applications|February 28, 2022
Dynamical systems as a level of cognitive analysis of multi-agent learning: Algorithmic foundations of temporal-difference learning dynamicsWolfram Barfuss
Scientific Reports|January 24, 2023
Intrinsic fluctuations of reinforcement learning promote cooperationWolfram Barfuss, Janusz M Meylahn
Physical Review. E|April 16, 2022
Modeling the effects of environmental and perceptual uncertainty using deterministic reinforcement learning dynamics with partial observabilityWolfram Barfuss, Richard P Mann
Plos Computational Biology|September 4, 2024
Moderate confirmation bias enhances decision-making in groups of reinforcement-learning agentsClémence Bergerot, Wolfram Barfuss, Pawel Romanczuk
Physical Review. E|May 22, 2019
Deterministic limit of temporal difference reinforcement learning for stochastic gamesWolfram Barfuss, Jonathan F Donges, Jürgen Kurths
Chaos (Woodbury, N.Y.)|January 3, 2020
Deep reinforcement learning in World-Earth system models to discover sustainable management strategiesFelix M Strnad, Wolfram Barfuss, Jonathan F Donges, et al.
Nature Communications|June 17, 2018
When optimization for governing human-environment tipping elements is neither sustainable nor safeWolfram Barfuss, Jonathan F Donges, Steven J Lade, et al.
Physical Review. E|January 14, 2017
Parsimonious modeling with information filtering networksWolfram Barfuss, Guido Previde Massara, T Di Matteo, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 22, 2020
Caring for the future can turn tragedy into comedy for long-term collective action under risk of collapseWolfram Barfuss, Jonathan F Donges, Vítor V Vasconcelos, et al.
Soft Matter|October 23, 2019
Geometric effects in random assemblies of ellipsesJakov Lovrić, Sara Kaliman, Wolfram Barfuss, et al.
Pageof 2

Showing results (1-10 of 14) with videos related to

Sort By:
Pageof 2
Neural Computing & Applications|February 28, 2022
Dynamical systems as a level of cognitive analysis of multi-agent learning: Algorithmic foundations of temporal-difference learning dynamicsWolfram Barfuss
Scientific Reports|January 24, 2023
Intrinsic fluctuations of reinforcement learning promote cooperationWolfram Barfuss, Janusz M Meylahn
Physical Review. E|April 16, 2022
Modeling the effects of environmental and perceptual uncertainty using deterministic reinforcement learning dynamics with partial observabilityWolfram Barfuss, Richard P Mann
Plos Computational Biology|September 4, 2024
Moderate confirmation bias enhances decision-making in groups of reinforcement-learning agentsClémence Bergerot, Wolfram Barfuss, Pawel Romanczuk
Physical Review. E|May 22, 2019
Deterministic limit of temporal difference reinforcement learning for stochastic gamesWolfram Barfuss, Jonathan F Donges, Jürgen Kurths
Chaos (Woodbury, N.Y.)|January 3, 2020
Deep reinforcement learning in World-Earth system models to discover sustainable management strategiesFelix M Strnad, Wolfram Barfuss, Jonathan F Donges, et al.
Nature Communications|June 17, 2018
When optimization for governing human-environment tipping elements is neither sustainable nor safeWolfram Barfuss, Jonathan F Donges, Steven J Lade, et al.
Physical Review. E|January 14, 2017
Parsimonious modeling with information filtering networksWolfram Barfuss, Guido Previde Massara, T Di Matteo, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 22, 2020
Caring for the future can turn tragedy into comedy for long-term collective action under risk of collapseWolfram Barfuss, Jonathan F Donges, Vítor V Vasconcelos, et al.
Soft Matter|October 23, 2019
Geometric effects in random assemblies of ellipsesJakov Lovrić, Sara Kaliman, Wolfram Barfuss, et al.
Pageof 2