Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (Artificial Intelligence and Applications, 2006)

The Recurrent Control Neural Network. - Anton Maximilian Schäfer; Steffen Udluft; Hans-Georg Zimmermann (ESANN, 2007)

Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (ESANN, 2007)

Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (ESANN, 2007)

Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (ICANN (1), 2007)

A Neural Reinforcement Learning Approach to Gas Turbine Control. - Anton Maximilian Schäfer; Daniel Schneegaß; Volkmar Sterzing; Steffen Udluft (IJCNN, 2007)

Safe exploration for reinforcement learning. - Alexander Hans; Daniel Schneegaß; Anton Maximilian Schäfer; Steffen Udluft (ESANN, 2008)

Uncertainty propagation for quality assurance in Reinforcement Learning. - Daniel Schneegaß; Steffen Udluft; Thomas Martinetz (IJCNN, 2008)

Learning long-term dependencies with recurrent neural networks. - Anton Maximilian Schäfer; Steffen Udluft; Hans-Georg Zimmermann (Neurocomputing, 2008)

Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data. - Alexander Hans; Steffen Udluft (ICANN (1), 2009)

Dateneffizientes Reinforcement-Learning. - Volkmar Sterzing; Steffen Udluft (Künstliche Intell., 2009)

Uncertainty Propagation for Efficient Exploration in Reinforcement Learning. - Alexander Hans; Steffen Udluft (ECAI, 2010)

The Markov Decision Process Extraction Network. - Siegmund Duell; Alexander Hans; Steffen Udluft (ESANN, 2010)

Ensembles of Neural Networks for Robust Reinforcement Learning. - Alexander Hans; Steffen Udluft (ICMLA, 2010)

Agent self-assessment: Determining policy quality without execution. - Alexander Hans; Siegmund Duell; Steffen Udluft (ADPRL, 2011)

Ensemble Usage for More Reliable Policy Identification in Reinforcement Learning. - Alexander Hans; Steffen Udluft (ESANN, 2011)

Recurrent Neural State Estimation in Domains with Long-Term Dependencies. - Siegmund Duell; Lina Weichbrodt; Alexander Hans; Steffen Udluft (ESANN, 2012)

Datenbasierte Optimalsteuerung mit neuronalen Netzen und dateneffizientem Reinforcement Learning. - Thomas A. Runkler; Steffen Udluft; Siegmund Düll (Autom., 2012)

Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks. - Siegmund Duell; Steffen Udluft; Volkmar Sterzing (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

Ensembles for Continuous Actions in Reinforcement Learning. - Siegmund Duell; Steffen Udluft (ESANN, 2013)

Regularized Recurrent Neural Networks for Data Efficient Dual-Task Learning. - Sigurd Spieckermann; Siegmund Düll; Steffen Udluft; Thomas A. Runkler (ICANN, 2014)

Exploiting similarity in system identification tasks with recurrent neural networks. - Sigurd Spieckermann; Siegmund Düll; Steffen Udluft; Alexander Hentschel; Thomas A. Runkler (Neurocomputing, 2015)

Particle Swarm Optimization for Generating Fuzzy Reinforcement Learning Policies. - Daniel Hein 001; Alexander Hentschel; Thomas A. Runkler; Steffen Udluft (CoRR, 2016)

Introduction to the "Industrial Benchmark". - Daniel Hein 001; Alexander Hentschel; Volkmar Sterzing; Michel Tokic; Steffen Udluft (CoRR, 2016)

Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces. - Daniel Hein 001; Alexander Hentschel; Thomas A. Runkler; Steffen Udluft (Int. J. Swarm Intell. Res., 2016)

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (ICLR (Poster), 2017)

Batch reinforcement learning on the industrial benchmark: First experiences. - Daniel Hein 001; Steffen Udluft; Michel Tokic; Alexander Hentschel; Thomas A. Runkler; Volkmar Sterzing (IJCNN, 2017)

A benchmark environment motivated by industrial control problems. - Daniel Hein 001; Stefan Depeweg; Michel Tokic; Steffen Udluft; Alexander Hentschel; Thomas A. Runkler; Volkmar Sterzing (SSCI, 2017)

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (CoRR, 2017)

Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies. - Daniel Hein 001; Alexander Hentschel; Thomas A. Runkler; Steffen Udluft (Eng. Appl. Artif. Intell., 2017)

Sensitivity analysis for predictive uncertainty. - Stefan Depeweg; José Miguel Hernández-Lobato; Steffen Udluft; Thomas A. Runkler (ESANN, 2018)

Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming. - Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (GECCO (Companion), 2018)

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (ICML, 2018)

Interpretable policies for reinforcement learning by genetic programming. - Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (Eng. Appl. Artif. Intell., 2018)

Generating interpretable reinforcement learning policies using genetic programming. - Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (GECCO (Companion), 2019)

Behavior Constraining in Weight Space for Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Daniel Hein 001; Thomas A. Runkler (ESANN, 2021)

Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (SSCI, 2021)

Overcoming model bias for robust offline deep reinforcement learning. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (Eng. Appl. Artif. Intell., 2021)

Safe Policy Improvement Approaches on Discrete Markov Decision Processes. - Philipp Scholl 003; Felix Dietrich; Clemens Otte; Steffen Udluft (ICAART (2), 2022)

Safe Policy Improvement Approaches and Their Limitations. - Philipp Scholl 003; Felix Dietrich; Clemens Otte; Steffen Udluft (ICAART (Revised Selected Paper, 2022)

Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Daniel Hein 001; Thomas A. Runkler (CoRR, 2022)

Automatic Trade-off Adaptation in Offline RL. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (ESANN, 2023)

User-Interactive Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (ICLR, 2023)

Workshop Summary: Quantum Machine Learning. - Volker Tresp; Steffen Udluft; Daniel Hein 001; Werner Hauptmann; Martin Leib; Christopher Mutschler; Daniel D. Scherer; Wolfgang Mauerer (QCE, 2023)

Learning Control Policies for Variable Objectives from Offline Data. - Marc Weber; Phillip Swazinna; Daniel Hein 001; Steffen Udluft; Volkmar Sterzing (SSCI, 2023)

Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. - Simon Wiedemann; Daniel Hein 001; Steffen Udluft; Christian B. Mendl (Trans. Mach. Learn. Res., 2023)

Why long model-based rollouts are no reason for bad Q-value estimates. - Philipp Wissmann; Daniel Hein 001; Steffen Udluft; Volker Tresp (ESANN, 2024)

Model-Based Offline Quantum Reinforcement Learning. - Simon Eisenmann; Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (QCE, 2024)

Neural-ANOVA: Model Decomposition for Interpretable Machine Learning. - Steffen Limmer; Steffen Udluft; Clemens Otte (CoRR, 2024)

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning. - Batikan Bora Ormanci; Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (CoRR, 2024)

Is Q-learning an Ill-posed Problem? - Philipp Wissmann; Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (CoRR, 2025)