1 code implementation • 1 Sep 2022 • Wolfram Barfuss, Janusz Meylahn
We find that next to a high caring for future rewards, a low exploration rate, and a small learning rate, it is primarily intrinsic stochastic fluctuations of the reinforcement learning process which double the final rate of cooperation to up to 80%.
1 code implementation • 15 Sep 2021 • Wolfram Barfuss, Richard P. Mann
We find that partial observability creates unintuitive benefits in a number of specific contexts, pointing the way to further research on a general understanding of such effects.
1 code implementation • 15 Aug 2019 • Felix M. Strnad, Wolfram Barfuss, Jonathan F. Donges, Jobst Heitzig
Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their interactions.
1 code implementation • 19 Sep 2018 • Wolfram Barfuss, Jonathan F. Donges, Jürgen Kurths
Reinforcement learning in multi-agent systems has been studied in the fields of economic game theory, artificial intelligence and statistical physics by developing an analytical understanding of the learning dynamics (often in relation to the replicator dynamics of evolutionary game theory).
Multiagent Systems
no code implementations • 23 Feb 2016 • Wolfram Barfuss, Guido Previde Massara, T. Di Matteo, Tomaso Aste
We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors.