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Daniel Hein
Daniel Hein
Research Scientist, Siemens AG
Bestätigte E-Mail-Adresse bei siemens.com
Titel
Zitiert von
Zitiert von
Jahr
Interpretable policies for reinforcement learning by genetic programming
D Hein, S Udluft, TA Runkler
Engineering Applications of Artificial Intelligence 76, 158-169, 2018
872018
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
662017
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
462018
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
202016
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
112017
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
82018
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
72018
Interpretable Control by Reinforcement Learning
D Hein, S Limmer, TA Runkler
IFAC-PapersOnLine 53 (2), 8082-8089, 2020
62020
Introduction to the" Industrial Benchmark"
D Hein, A Hentschel, V Sterzing, M Tokic, S Udluft
arXiv preprint arXiv:1610.03793, 2016
62016
Generating interpretable reinforcement learning policies using genetic programming
D Hein, S Udluft, TA Runkler
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019
42019
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
P Swazinna, S Udluft, D Hein, T Runkler
arXiv preprint arXiv:2201.05433, 2022
32022
Behavior constraining in weight space for offline reinforcement learning
P Swazinna, S Udluft, D Hein, T Runkler
arXiv preprint arXiv:2107.05479, 2021
32021
Interpretable Reinforcement Learning Policies by Evolutionary Computation
D Hein
Technische Universität München, 2019
32019
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
12022
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
2021
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