Članki z zahtevami za javni dostop - Martin RiedmillerVeč o tem
Ni na voljo nikjer: 2
Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real …
W Böhmer, JT Springenberg, J Boedecker, M Riedmiller, K Obermayer
KI-Künstliche Intelligenz 29 (4), 353-362, 2015
Zahteve: German Research Foundation
Adaptive long-term control of biological neural networks with deep reinforcement learning
JM Wülfing, SS Kumar, J Boedecker, M Riedmiller, U Egert
Neurocomputing 342, 66-74, 2019
Zahteve: German Research Foundation, Federal Ministry of Education and Research, Germany
Na voljo nekje: 29
Discriminative unsupervised feature learning with convolutional neural networks
A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox
Advances in neural information processing systems 27, 2014
Zahteve: German Research Foundation, European Commission
Embed to control: A locally linear latent dynamics model for control from raw images
M Watter, J Springenberg, J Boedecker, M Riedmiller
Advances in neural information processing systems 28, 2015
Zahteve: German Research Foundation
Magnetic control of tokamak plasmas through deep reinforcement learning
J Degrave, F Felici, J Buchli, M Neunert, B Tracey, F Carpanese, T Ewalds, ...
Nature 602 (7897), 414-419, 2022
Zahteve: Swiss National Science Foundation
Multimodal deep learning for robust RGB-D object recognition
A Eitel, JT Springenberg, L Spinello, M Riedmiller, W Burgard
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
Zahteve: German Research Foundation
Reinforcement learning for robot soccer
M Riedmiller, T Gabel, R Hafner, S Lange
Autonomous Robots 27, 55-73, 2009
Zahteve: German Research Foundation
Approximate real-time optimal control based on sparse gaussian process models
J Boedecker, JT Springenberg, J Wülfing, M Riedmiller
2014 IEEE symposium on adaptive dynamic programming and reinforcement …, 2014
Zahteve: German Research Foundation
On a successful application of multi-agent reinforcement learning to operations research benchmarks
T Gabel, M Riedmiller
2007 IEEE international symposium on approximate dynamic programming and …, 2007
Zahteve: German Research Foundation
Is bang-bang control all you need? solving continuous control with bernoulli policies
T Seyde, I Gilitschenski, W Schwarting, B Stellato, M Riedmiller, ...
Advances in Neural Information Processing Systems 34, 27209-27221, 2021
Zahteve: US Department of Defense
Incremental GRLVQ: Learning relevant features for 3D object recognition
TC Kietzmann, S Lange, M Riedmiller
Neurocomputing 71 (13-15), 2868-2879, 2008
Zahteve: German Research Foundation
A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping
T Lampe, LDJ Fiederer, M Voelker, A Knorr, M Riedmiller, T Ball
Proceedings of the 19th international conference on Intelligent User …, 2014
Zahteve: German Research Foundation
Approximate model-assisted neural fitted q-iteration
T Lampe, M Riedmiller
2014 International Joint Conference on Neural Networks (IJCNN), 2698-2704, 2014
Zahteve: German Research Foundation
Making a robot learn to play soccer using reward and punishment
H Müller, M Lauer, R Hafner, S Lange, A Merke, M Riedmiller
Annual Conference on Artificial Intelligence, 220-234, 2007
Zahteve: German Research Foundation
Scaling adaptive agent-based reactive job-shop scheduling to large-scale problems
T Gabel, M Riedmiller
2007 IEEE Symposium on Computational Intelligence in Scheduling, 259-266, 2007
Zahteve: German Research Foundation
Bridging the gap: Learning in the RoboCup simulation and midsize league
T Gabel, R Hafner, S Lange, M Lauer, M Riedmiller
Proceedings of the 7th Portuguese Conference on Automatic Control, 2006
Zahteve: German Research Foundation
Autonomous optimization of targeted stimulation of neuronal networks
SS Kumar, J Wülfing, S Okujeni, J Boedecker, M Riedmiller, U Egert
PLoS computational biology 12 (8), e1005054, 2016
Zahteve: German Research Foundation
Modeling effects of intrinsic and extrinsic rewards on the competition between striatal learning systems
J Boedecker, T Lampe, M Riedmiller
Frontiers in psychology 4, 739, 2013
Zahteve: German Research Foundation
Computational object recognition: a biologically motivated approach
TC Kietzmann, S Lange, M Riedmiller
Biological cybernetics 100, 59-79, 2009
Zahteve: German Research Foundation
Reducing policy degradation in neuro-dynamic programming.
T Gabel, MA Riedmiller
ESANN, 653-658, 2006
Zahteve: German Research Foundation
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