Thomas Rueckstiess
Thomas Rueckstiess
PhD Candidate, TU München
Verified email at in.tum.de
TitleCited byYear
PyBrain
T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T Rückstieß, ...
The Journal of Machine Learning Research 11, 743-746, 2010
3672010
Parameter-exploring policy gradients
F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber
Neural Networks 23 (4), 551-559, 2010
1702010
Exploring parameter space in reinforcement learning
T Rückstiess, F Sehnke, T Schaul, D Wierstra, Y Sun, J Schmidhuber
Paladyn, Journal of Behavioral Robotics 1 (1), 14-24, 2010
752010
State-dependent exploration for policy gradient methods
T Rückstieß, M Felder, J Schmidhuber
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2008
692008
Policy gradients with parameter-based exploration for control
F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber
International Conference on Artificial Neural Networks, 387-396, 2008
582008
Sequential Feature Selection for Classification
T Rückstieß, C Osendorfer, P van der Smagt
AI 2011 23 (4), 132--141, 2011
322011
Minimizing data consumption with sequential online feature selection
T Rückstieß, C Osendorfer, P van der Smagt
International Journal of Machine Learning and Cybernetics 4 (3), 235-243, 2013
192013
Fast and accurate environment modelling using omnidirectional vision
P Heinemann, T Rückstieß, A Zell
Dynamic Perception, 9-14, 2004
172004
climin-A pythonic framework for gradient-based function optimization
J Bayer, C Osendorfer, S Diot-Girard, T Rueckstiess, S Urban
TUM, Tech. Rep., 2015
52015
A Python Experiment Suite
T Rückstieß, J Schmidhuber
The Python Papers 6 (1), 2, 2011
42011
Python Experiment Suite Implementation
T Rückstieß, J Schmidhuber
The Python Papers Source Codes 2 (4), 2011
22011
Reinforcement Learning in Supervised Problem Domains
TF Rückstieß
Technische Universität München, 2016
12016
Echtzeit-Objekterkennung mit Omnidirektionaler Kamera
T Rückstieß
Studienarbeit, Universität Tübingen 190, 2005
12005
Minimizing Data Consumption in Sequential Classification
T Rückstiess, C Osendorfer, P Smagt
International Journal of Machine Learning and Cybernetics, 2012
2012
1IDSIA, University of Lugano, Galleria 2, Manno-Lugano, 6900, Switzerland 2Technische Universität München, Garching D-86748, Germany
T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T Rückstieß, ...
Journal of Machine Learning Research 1, 999-1000, 2010
2010
Python Experiment Suite Implementation.
T Rückstieβ, J Schmidhuber
Python Papers Source Codes 2, 2010
2010
Parametric Policy Gradients for Robotics
F Sehnke, T Rückstieß, M Felder, J Schmidhuber
1st International Workshop on Cognition for Technical Systems, 2008, 2008
2008
Robot Learning with State-Dependent Exploration
T Rückstieß, M Felder, F Sehnke, J Schmidhuber
1st International Workshop on Cognition for Technical Systems, 2008
2008
The system can't perform the operation now. Try again later.
Articles 1–18