Evolutionary training and abstraction yields algorithmic generalization of neural computers

Daniel Tanneberg; Elmar Rückert; Jan Peters

doi:10.1038/s42256-020-00255-1

Evolutionary training and abstraction yields algorithmic generalization of neural computers

Daniel Tanneberg
, Elmar Rückert
, Jan Peters

Lehrstuhl für Cyber Physical Systems (190)

Technische Universität Darmstadt
Max-Planck Institut für Intelligente Systeme

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Begutachtung

Originalsprache	Englisch
Fachzeitschrift	Nature machine intelligence
DOIs	https://doi.org/10.1038/s42256-020-00255-1
Publikationsstatus	Veröffentlicht - 16 Nov. 2020

Bibliographische Notiz

Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nos. 713010 (GOAL-Robots) and 640554 (SKILLS4ROBOTS), and from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under no. 430054590. This research was supported by NVIDIA. We want to thank K. O’Regan for inspiring discussions on defining algorithmic solutions.

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.

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10.1038/s42256-020-00255-1

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