Steffen Udluft

Clemens Otte

CoRR, 2024

Why long model-based rollouts are no reason for bad Q-value estimates.

[BibT_eX]

[DOI]

CoRR, 2024

Model-based Offline Quantum Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Learning Control Policies for Variable Objectives from Offline Data.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

Workshop Summary: Quantum Machine Learning.

[BibT_eX]

[DOI]

Christopher Mutschler

Daniel D. Scherer

Wolfgang Mauerer

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

User-Interactive Offline Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Automatic Trade-off Adaptation in Offline RL.

[BibT_eX]

[DOI]

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Safe Policy Improvement Approaches and Their Limitations.

[BibT_eX]

[DOI]

Proceedings of the Agents and Artificial Intelligence - 14th International Conference, 2022

Safe Policy Improvement Approaches on Discrete Markov Decision Processes.

[BibT_eX]

[DOI]

Proceedings of the 14th International Conference on Agents and Artificial Intelligence, 2022

2021

Overcoming model bias for robust offline deep reinforcement learning.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2021

Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Behavior Constraining in Weight Space for Offline Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2019

Generating interpretable reinforcement learning policies using genetic programming.

[BibT_eX]

[DOI]

Daniel Hein

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

2018

Interpretable policies for reinforcement learning by genetic programming.

[BibT_eX]

[DOI]

Daniel Hein

Eng. Appl. Artif. Intell., 2018

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning.

[BibT_eX]

[DOI]

Finale Doshi-Velez

Proceedings of the 35th International Conference on Machine Learning, 2018

Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming.

[BibT_eX]

[DOI]

Daniel Hein

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Sensitivity analysis for predictive uncertainty.

[BibT_eX]

[DOI]

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017

Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2017

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems.

[BibT_eX]

[DOI]

Finale Doshi-Velez

CoRR, 2017

A benchmark environment motivated by industrial control problems.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Batch reinforcement learning on the industrial benchmark: First experiences.

[BibT_eX]

[DOI]

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks.

[BibT_eX]

[DOI]

Finale Doshi-Velez

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces.

[BibT_eX]

[DOI]

Int. J. Swarm Intell. Res., 2016

Introduction to the "Industrial Benchmark".

[BibT_eX]

[DOI]

CoRR, 2016

Particle Swarm Optimization for Generating Fuzzy Reinforcement Learning Policies.

[BibT_eX]

[DOI]

CoRR, 2016

2015

Exploiting similarity in system identification tasks with recurrent neural networks.

[BibT_eX]

[DOI]

Neurocomputing, 2015

2014

Regularized Recurrent Neural Networks for Data Efficient Dual-Task Learning.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

2013

Ensembles for Continuous Actions in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 21st European Symposium on Artificial Neural Networks, 2013

2012

Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks.

[BibT_eX]

[DOI]

Volkmar Sterzing

Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Datenbasierte Optimalsteuerung mit neuronalen Netzen und dateneffizientem Reinforcement Learning.

[BibT_eX]

[DOI]

Siegmund Düll

Autom., 2012

Recurrent Neural State Estimation in Domains with Long-Term Dependencies.

[BibT_eX]

[DOI]

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011

Ensemble Usage for More Reliable Policy Identification in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Agent self-assessment: Determining policy quality without execution.

[BibT_eX]

[DOI]

Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning, 2011

2010

Ensembles of Neural Networks for Robust Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

The Markov Decision Process Extraction Network.

[BibT_eX]

[DOI]

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

Uncertainty Propagation for Efficient Exploration in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the ECAI 2010, 2010

2009

Dateneffizientes Reinforcement-Learning.

[BibT_eX]

[DOI]

Volkmar Sterzing

Künstliche Intell., 2009

Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2009

2008

Learning long-term dependencies with recurrent neural networks.

[BibT_eX]

[DOI]

Hans-Georg Zimmermann

Neurocomputing, 2008

Uncertainty propagation for quality assurance in Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2008

Safe exploration for reinforcement learning.

[BibT_eX]

[DOI]

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

2007

A Neural Reinforcement Learning Approach to Gas Turbine Control.

[BibT_eX]

[DOI]

Volkmar Sterzing

Proceedings of the International Joint Conference on Neural Networks, 2007

Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2007

Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification.

[BibT_eX]

[DOI]

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments.

[BibT_eX]

[DOI]

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

The Recurrent Control Neural Network.

[BibT_eX]

[DOI]

Hans-Georg Zimmermann

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

2006

Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach.

[BibT_eX]

[DOI]