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Daniel Hein
Daniel Hein
Research Scientist, Siemens AG
Verified email at siemens.com
Title
Cited by
Cited by
Year
Interpretable policies for reinforcement learning by genetic programming
D Hein, S Udluft, TA Runkler
Engineering Applications of Artificial Intelligence 76, 158-169, 2018
1372018
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies
D Hein, A Hentschel, T Runkler, S Udluft
Engineering Applications of Artificial Intelligence 65, 87-98, 2017
922017
A Benchmark Environment Motivated by Industrial Control Problems
D Hein, S Depeweg, M Tokic, S Udluft, A Hentschel, TA Runkler, ...
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2018
632018
Reinforcement learning with particle swarm optimization policy (PSO-P) in continuous state and action spaces
D Hein, A Hentschel, TA Runkler, S Udluft
International Journal of Swarm Intelligence Research (IJSIR) 7 (3), 23-42, 2016
292016
Comparing model-free and model-based algorithms for offline reinforcement learning
P Swazinna, S Udluft, D Hein, T Runkler
IFAC-PapersOnLine 55 (15), 19-26, 2022
252022
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
D Hein, S Udluft, TA Runkler
GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference …, 2018
182018
Batch reinforcement learning on the industrial benchmark: First experiences
D Hein, S Udluft, M Tokic, A Hentschel, TA Runkler, V Sterzing
Neural Networks (IJCNN), 2017 International Joint Conference on, 4214-4221, 2017
172017
Interpretable Control by Reinforcement Learning
D Hein, S Limmer, TA Runkler
IFAC-PapersOnLine 53 (2), 8082-8089, 2020
132020
Quantum Policy Iteration via Amplitude Estimation and Grover Search--Towards Quantum Advantage for Reinforcement Learning
S Wiedemann, D Hein, S Udluft, C Mendl
arXiv preprint arXiv:2206.04741, 2022
102022
Particle Swarm Optimization for Model Predictive Control in Reinforcement Learning Environments
D Hein, A Hentschel, TA Runkler, S Udluft
Critical Developments and Applications of Swarm Intelligence, 401-427, 2018
92018
Generating interpretable reinforcement learning policies using genetic programming
D Hein, S Udluft, TA Runkler
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019
72019
Behavior constraining in weight space for offline reinforcement learning
P Swazinna, S Udluft, D Hein, T Runkler
arXiv preprint arXiv:2107.05479, 2021
62021
Interpretable Reinforcement Learning Policies by Evolutionary Computation
D Hein
Technische Universität München, 2019
62019
Introduction to the" Industrial Benchmark"
D Hein, A Hentschel, V Sterzing, M Tokic, S Udluft
arXiv preprint arXiv:1610.03793, 2016
62016
Learning Control Policies for Variable Objectives from Offline Data
M Weber, P Swazinna, D Hein, S Udluft, V Sterzing
2023 IEEE Symposium Series on Computational Intelligence (SSCI), 1674-1681, 2023
22023
Computer-aided control and/or regulation of a technical system
S Düll, D Hein, A Hentschel, T Runkler, S Udluft
US Patent 10,107,205, 2018
22018
Trustworthy AI for process automation on a Chylla-Haase polymerization reactor
D Hein, D Labisch
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
12021
Why long model-based rollouts are no reason for bad Q-value estimates
P Wissmann, D Hein, S Udluft, V Tresp
arXiv preprint arXiv:2407.11751, 2024
2024
Model-based Offline Quantum Reinforcement Learning
S Eisenmann, D Hein, S Udluft, TA Runkler
arXiv preprint arXiv:2404.10017, 2024
2024
Workshop Summary: Quantum Machine Learning
V Tresp, S Udluft, D Hein, W Hauptmann, M Leib, C Mutschler, ...
2023 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2023
2023
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