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Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot

May 21, 2020
Malte Schilling, Kai Konen, Frank W. Ohl, Timo Korthals

* Submitted as an IEEE conference paper (updated to 15 seeds in comparisons) 

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A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing

Nov 01, 2019
Timo Korthals, Malte Schilling, Jürgen Leitner

* Extended Abstract for the IROS 2019 Workshop on Deep Probabilistic Generative Models for Cognitive Architecture in Robotics 

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From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility

Aug 13, 2019
Malte Schilling, Helge Ritter, Frank W. Ohl

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Setup of a Recurrent Neural Network as a Body Model for Solving Inverse and Forward Kinematics as well as Dynamics for a Redundant Manipulator

Apr 12, 2019
Malte Schilling

* Pre-print (accepted to IJCNN 2019) 

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Modularization of End-to-End Learning: Case Study in Arcade Games

Jan 27, 2019
Andrew Melnik, Sascha Fleer, Malte Schilling, Helge Ritter

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Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

Apr 02, 2018
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Miłoś, Błażej Osiński, Andrew Melnik, Malte Schilling, Helge Ritter, Sean Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp

* 27 pages, 17 figures 

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