Daniel Hein

Orcid: 0000-0002-8375-1592

Affiliations:

Technical University of Munich, Germany
Siemens AG, Germany

According to our database¹, Daniel Hein authored at least 20 papers between 2016 and 2024.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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PhD thesis

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Bibliography

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

2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation.

[BibT_eX]

[DOI]

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021

Trustworthy AI for process automation on a Chylla-Haase polymerization reactor.

[BibT_eX]

[DOI]

Daniel Hein

Daniel Labisch

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Behavior Constraining in Weight Space for Offline Reinforcement Learning.

[BibT_eX]

[DOI]

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

2020

Interpretable Control by Reinforcement Learning.

[BibT_eX]

[DOI]

Daniel Hein

Steffen Limmer

Thomas A. Runkler

CoRR, 2020

2019

Interpretable Reinforcement Learning Policies by Evolutionary Computation.

[BibT_eX]

[DOI]

Daniel Hein

PhD thesis, 2019

Generating interpretable reinforcement learning policies using genetic programming.

[BibT_eX]

[DOI]

Daniel Hein

Steffen Udluft

Thomas A. Runkler

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

2018

Interpretable policies for reinforcement learning by genetic programming.

[BibT_eX]

[DOI]

Daniel Hein

Steffen Udluft

Thomas A. Runkler

Eng. Appl. Artif. Intell., 2018

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

[BibT_eX]

[DOI]

Daniel Hein

Steffen Udluft

Thomas A. Runkler

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

2017

Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 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

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

Daniel Hein

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