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Group by: No Grouping | Item Type | Date
Number of items: 5.

Tanneberg, Daniel ; Rueckert, Elmar ; Peters, Jan (2023)
Evolutionary training and abstraction yields algorithmic generalization of neural computers.
In: Nature Machine Intelligence, 2020, 2 (12)
doi: 10.26083/tuprints-00020535
Article, Secondary publication, Postprint

Tanneberg, Daniel ; Peters, Jan ; Rueckert, Elmar (2022)
Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks.
In: Neural Networks, 2022, 109
doi: 10.26083/tuprints-00020537
Article, Secondary publication, Postprint

Tanneberg, Daniel ; Peters, Jan ; Rueckert, Elmar (2022)
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals.
CoRL2017 - Conference on Robot Learning 2017. Mountain View, California (13.-15.11.2017)
doi: 10.26083/tuprints-00020580
Conference or Workshop Item, Secondary publication, Publisher's Version

Tanneberg, Daniel ; Ploeger, Kai ; Rueckert, Elmar ; Peters, Jan (2022)
SKID RAW: Skill Discovery From Raw Trajectories.
In: IEEE Robotics and Automation Letters, 2022, 6 (3)
doi: 10.26083/tuprints-00020536
Article, Secondary publication, Postprint

Tanneberg, Daniel (2020)
Understand-Compute-Adapt: Neural Networks for Intelligent Agents.
Technische Universität Darmstadt
doi: 10.25534/tuprints-00017234
Ph.D. Thesis, Primary publication, Publisher's Version

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