Daniel Hein

According to our database, Daniel Hein authored at least 18 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

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

Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl

Trans. Mach. Learn. Res., 2023

Learning Control Policies for Variable Objectives from Offline Data.

Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, Volkmar Sterzing

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

Workshop Summary: Quantum Machine Learning.

Volker Tresp, Steffen Udluft, Daniel Hein, Werner Hauptmann, Martin Leib, 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.

Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler

CoRR, 2022

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

Henri Meess, Jeremias Gerner, Daniel Hein, Stefanie Schmidtner, Gordon Elger

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.

Daniel Hein, Daniel Labisch

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

Behavior Constraining in Weight Space for Offline Reinforcement Learning.

Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler

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

2020

Interpretable Control by Reinforcement Learning.

Daniel Hein, Steffen Limmer, Thomas A. Runkler

CoRR, 2020

2019

Interpretable Reinforcement Learning Policies by Evolutionary Computation.

Daniel Hein

PhD thesis, 2019

Generating interpretable reinforcement learning policies using genetic programming.

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.

Daniel Hein, Steffen Udluft, Thomas A. Runkler

Eng. Appl. Artif. Intell., 2018

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

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.

Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft

Eng. Appl. Artif. Intell., 2017

A benchmark environment motivated by industrial control problems.

Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing

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

Batch reinforcement learning on the industrial benchmark: First experiences.

Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing

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.

Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft

Int. J. Swarm Intell. Res., 2016

Introduction to the "Industrial Benchmark".

Daniel Hein, Alexander Hentschel, Volkmar Sterzing, Michel Tokic, Steffen Udluft

CoRR, 2016

Particle Swarm Optimization for Generating Fuzzy Reinforcement Learning Policies.

Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft

CoRR, 2016