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Steffen Udluft
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2020 – today
- 2024
- [i21]Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Model-based Offline Quantum Reinforcement Learning. CoRR abs/2404.10017 (2024) - [i20]Philipp Wissmann, Daniel Hein, Steffen Udluft, Volker Tresp:
Why long model-based rollouts are no reason for bad Q-value estimates. CoRR abs/2407.11751 (2024) - [i19]Steffen Limmer, Steffen Udluft, Clemens Otte:
Neural-ANOVA: Model Decomposition for Interpretable Machine Learning. CoRR abs/2408.12319 (2024) - 2023
- [j9]Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl:
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c34]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Automatic Trade-off Adaptation in Offline RL. ESANN 2023 - [c33]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
User-Interactive Offline Reinforcement Learning. ICLR 2023 - [c32]Volker Tresp, Steffen Udluft, Daniel Hein, Werner Hauptmann, Martin Leib, Christopher Mutschler, Daniel D. Scherer, Wolfgang Mauerer:
Workshop Summary: Quantum Machine Learning. QCE 2023: 1-3 - [c31]Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, Volkmar Sterzing:
Learning Control Policies for Variable Objectives from Offline Data. SSCI 2023: 1674-1681 - [i18]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Automatic Trade-off Adaptation in Offline RL. CoRR abs/2306.09744 (2023) - [i17]Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, Volkmar Sterzing:
Learning Control Policies for Variable Objectives from Offline Data. CoRR abs/2308.06127 (2023) - 2022
- [c30]Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft:
Safe Policy Improvement Approaches and Their Limitations. ICAART (Revised Selected Paper 2022: 74-98 - [c29]Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft:
Safe Policy Improvement Approaches on Discrete Markov Decision Processes. ICAART (2) 2022: 142-151 - [i16]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning. CoRR abs/2201.05433 (2022) - [i15]Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft:
Safe Policy Improvement Approaches on Discrete Markov Decision Processes. CoRR abs/2201.12175 (2022) - [i14]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
User-Interactive Offline Reinforcement Learning. CoRR abs/2205.10629 (2022) - [i13]Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl:
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. CoRR abs/2206.04741 (2022) - [i12]Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft:
Safe Policy Improvement Approaches and their Limitations. CoRR abs/2208.00724 (2022) - 2021
- [j8]Phillip Swazinna
, Steffen Udluft
Overcoming model bias for robust offline deep reinforcement learning. Eng. Appl. Artif. Intell. 104: 104366 (2021) - [c28]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Behavior Constraining in Weight Space for Offline Reinforcement Learning. ESANN 2021 - [c27]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning. SSCI 2021: 1-8 - [i11]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Behavior Constraining in Weight Space for Offline Reinforcement Learning. CoRR abs/2107.05479 (2021) - [i10]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning. CoRR abs/2111.13461 (2021) - 2020
- [i9]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning. CoRR abs/2008.05533 (2020)
2010 – 2019
- 2019
- [c26]Daniel Hein
Generating interpretable reinforcement learning policies using genetic programming. GECCO (Companion) 2019: 23-24 - 2018
- [j7]Daniel Hein
Interpretable policies for reinforcement learning by genetic programming. Eng. Appl. Artif. Intell. 76: 158-169 (2018) - [c25]Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, Thomas A. Runkler:
Sensitivity analysis for predictive uncertainty. ESANN 2018 - [c24]Daniel Hein
Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming. GECCO (Companion) 2018: 1268-1275 - [c23]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. ICML 2018: 1192-1201 - [i8]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming. CoRR abs/1804.10960 (2018) - 2017
- [j6]Daniel Hein
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies. Eng. Appl. Artif. Intell. 65: 87-98 (2017) - [c22]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. ICLR (Poster) 2017 - [c21]Daniel Hein
Batch reinforcement learning on the industrial benchmark: First experiences. IJCNN 2017: 4214-4221 - [c20]Daniel Hein
A benchmark environment motivated by industrial control problems. SSCI 2017: 1-8 - [i7]Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences. CoRR abs/1705.07262 (2017) - [i6]Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
A Benchmark Environment Motivated by Industrial Control Problems. CoRR abs/1709.09480 (2017) - [i5]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems. CoRR abs/1710.07283 (2017) - [i4]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Interpretable Policies for Reinforcement Learning by Genetic Programming. CoRR abs/1712.04170 (2017) - 2016
- [j5]Daniel Hein
Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces. Int. J. Swarm Intell. Res. 7(3): 23-42 (2016) - [i3]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. CoRR abs/1605.07127 (2016) - [i2]Daniel Hein, Alexander Hentschel, Volkmar Sterzing, Michel Tokic, Steffen Udluft:
Introduction to the "Industrial Benchmark". CoRR abs/1610.03793 (2016) - [i1]Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft:
Particle Swarm Optimization for Generating Fuzzy Reinforcement Learning Policies. CoRR abs/1610.05984 (2016) - 2015
- [j4]Sigurd Spieckermann, Siegmund Düll, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler
Exploiting similarity in system identification tasks with recurrent neural networks. Neurocomputing 169: 343-349 (2015) - 2014
- [c19]Sigurd Spieckermann, Siegmund Düll, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler:
Exploiting similarity in system identification tasks with recurrent neural networks. ESANN 2014 - [c18]Sigurd Spieckermann, Siegmund Düll, Steffen Udluft, Thomas A. Runkler
Regularized Recurrent Neural Networks for Data Efficient Dual-Task Learning. ICANN 2014: 17-24 - 2013
- [c17]Siegmund Duell, Steffen Udluft:
Ensembles for Continuous Actions in Reinforcement Learning. ESANN 2013 - 2012
- [j3]Thomas A. Runkler
Datenbasierte Optimalsteuerung mit neuronalen Netzen und dateneffizientem Reinforcement Learning. Autom. 60(10): 641-647 (2012) - [c16]Siegmund Duell, Lina Weichbrodt, Alexander Hans, Steffen Udluft:
Recurrent Neural State Estimation in Domains with Long-Term Dependencies. ESANN 2012 - [p1]Siegmund Duell, Steffen Udluft, Volkmar Sterzing:
Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 709-733 - 2011
- [c15]Alexander Hans, Siegmund Duell, Steffen Udluft:
Agent self-assessment: Determining policy quality without execution. ADPRL 2011: 84-90 - [c14]Alexander Hans, Steffen Udluft:
Ensemble Usage for More Reliable Policy Identification in Reinforcement Learning. ESANN 2011 - 2010
- [c13]Alexander Hans, Steffen Udluft:
Uncertainty Propagation for Efficient Exploration in Reinforcement Learning. ECAI 2010: 361-366 - [c12]Siegmund Duell, Alexander Hans, Steffen Udluft:
The Markov Decision Process Extraction Network. ESANN 2010 - [c11]Alexander Hans, Steffen Udluft:
Ensembles of Neural Networks for Robust Reinforcement Learning. ICMLA 2010: 401-406
2000 – 2009
- 2009
- [j2]Volkmar Sterzing, Steffen Udluft:
Dateneffizientes Reinforcement-Learning. Künstliche Intell. 23(3): 19-22 (2009) - [c10]Alexander Hans, Steffen Udluft:
Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data. ICANN (1) 2009: 70-79 - 2008
- [j1]Anton Maximilian Schäfer, Steffen Udluft, Hans-Georg Zimmermann:
Learning long-term dependencies with recurrent neural networks. Neurocomputing 71(13-15): 2481-2488 (2008) - [c9]Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, Steffen Udluft:
Safe exploration for reinforcement learning. ESANN 2008: 143-148 - [c8]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Uncertainty propagation for quality assurance in Reinforcement Learning. IJCNN 2008: 2588-2595 - 2007
- [c7]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments. ESANN 2007: 301-306 - [c6]Anton Maximilian Schäfer, Steffen Udluft, Hans-Georg Zimmermann:
The Recurrent Control Neural Network. ESANN 2007: 319-324 - [c5]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification. ESANN 2007: 337-342 - [c4]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification. ICANN (1) 2007: 109-118 - [c3]Anton Maximilian Schäfer, Daniel Schneegaß, Volkmar Sterzing, Steffen Udluft:
A Neural Reinforcement Learning Approach to Gas Turbine Control. IJCNN 2007: 1691-1696 - 2006
- [c2]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach. Artificial Intelligence and Applications 2006: 428-433 - [c1]Anton Maximilian Schäfer, Steffen Udluft, Hans-Georg Zimmermann:
Learning Long Term Dependencies with Recurrent Neural Networks. ICANN (1) 2006: 71-80
Coauthor Index
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last updated on 2024-09-30 00:04 CEST by the dblp team
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