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ORCIDDaniel Hein 0001
Person information
- affiliation: Technical University of Munich, Germany
- affiliation: Siemens AG, Germany
Other persons with the same name
- Daniel Hein 0002
![[0000-0001-9186-4178]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-9186-4178")
— DLR German Aerospace Center, Berlin, Germany (and 1 more) - Daniel Hein 0003 (aka: Daniel W. E. Hein) — University of Bamberg, Germany
Other persons with a similar name
- Daniel D. Hein — University of Kansas, Lawrence, KS, USA
- Daniel M. Hein — Graz University of Technology, IAIK
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2020 – today
- 2024
[i14]Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Model-based Offline Quantum Reinforcement Learning. CoRR abs/2404.10017 (2024)- [i13]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) - 2023
- [j4]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) - [c9]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 - [c8]Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, Volkmar Sterzing:
Learning Control Policies for Variable Objectives from Offline Data. SSCI 2023: 1674-1681 - [i12]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
- [c7]Henri Meess, Jeremias Gerner, Daniel Hein, Stefanie Schmidtner, Gordon Elger:
Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation. AAMAS 2022: 1699-1701 - [i11]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) - [i10]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) - 2021
- [c6]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Behavior Constraining in Weight Space for Offline Reinforcement Learning. ESANN 2021 - [c5]Daniel Hein, Daniel Labisch:
Trustworthy AI for process automation on a Chylla-Haase polymerization reactor. GECCO Companion 2021: 1570-1578 - [i9]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Behavior Constraining in Weight Space for Offline Reinforcement Learning. CoRR abs/2107.05479 (2021) - [i8]Daniel Hein, Daniel Labisch:
Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor. CoRR abs/2108.13381 (2021) - 2020
- [i7]Daniel Hein, Steffen Limmer, Thomas A. Runkler:
Interpretable Control by Reinforcement Learning. CoRR abs/2007.09964 (2020)
2010 – 2019
- 2019
- [b1]Daniel Hein:
Interpretable Reinforcement Learning Policies by Evolutionary Computation. Technical University of Munich, Germany, 2019 - [c4]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Generating interpretable reinforcement learning policies using genetic programming. GECCO (Companion) 2019: 23-24 - 2018
- [j3]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Interpretable policies for reinforcement learning by genetic programming. Eng. Appl. Artif. Intell. 76: 158-169 (2018) - [c3]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming. GECCO (Companion) 2018: 1268-1275 - [i6]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming. CoRR abs/1804.10960 (2018) - 2017
- [j2]Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft:
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies. Eng. Appl. Artif. Intell. 65: 87-98 (2017) - [c2]Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
Batch reinforcement learning on the industrial benchmark: First experiences. IJCNN 2017: 4214-4221 - [c1]Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
A benchmark environment motivated by industrial control problems. SSCI 2017: 1-8 - [i5]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) - [i4]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) - [i3]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Interpretable Policies for Reinforcement Learning by Genetic Programming. CoRR abs/1712.04170 (2017) - 2016
- [j1]Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft:
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) - [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)
Coauthor Index
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