Particle Swarm Optimization for Generating Fuzzy Reinforcement Learning Policies. - Daniel Hein 001; Alexander Hentschel; Thomas A. Runkler; Steffen Udluft (CoRR, 2016)

Introduction to the "Industrial Benchmark". - Daniel Hein 001; Alexander Hentschel; Volkmar Sterzing; Michel Tokic; Steffen Udluft (CoRR, 2016)

Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces. - Daniel Hein 001; Alexander Hentschel; Thomas A. Runkler; Steffen Udluft (Int. J. Swarm Intell. Res., 2016)

Batch reinforcement learning on the industrial benchmark: First experiences. - Daniel Hein 001; Steffen Udluft; Michel Tokic; Alexander Hentschel; Thomas A. Runkler; Volkmar Sterzing (IJCNN, 2017)

A benchmark environment motivated by industrial control problems. - Daniel Hein 001; Stefan Depeweg; Michel Tokic; Steffen Udluft; Alexander Hentschel; Thomas A. Runkler; Volkmar Sterzing (SSCI, 2017)

Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies. - Daniel Hein 001; Alexander Hentschel; Thomas A. Runkler; Steffen Udluft (Eng. Appl. Artif. Intell., 2017)

Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming. - Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (GECCO (Companion), 2018)

Interpretable policies for reinforcement learning by genetic programming. - Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (Eng. Appl. Artif. Intell., 2018)

Generating interpretable reinforcement learning policies using genetic programming. - Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (GECCO (Companion), 2019)

Interpretable Reinforcement Learning Policies by Evolutionary Computation. - Daniel Hein 001 (2019)

Interpretable Control by Reinforcement Learning. - Daniel Hein 001; Steffen Limmer; Thomas A. Runkler (CoRR, 2020)

Behavior Constraining in Weight Space for Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Daniel Hein 001; Thomas A. Runkler (ESANN, 2021)

Trustworthy AI for process automation on a Chylla-Haase polymerization reactor. - Daniel Hein 001; Daniel Labisch (GECCO Companion, 2021)

Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation. - Henri Meess; Jeremias Gerner; Daniel Hein 001; Stefanie Schmidtner; Gordon Elger (AAMAS, 2022)

Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Daniel Hein 001; Thomas A. Runkler (CoRR, 2022)

Workshop Summary: Quantum Machine Learning. - Volker Tresp; Steffen Udluft; Daniel Hein 001; Werner Hauptmann; Martin Leib; Christopher Mutschler; Daniel D. Scherer; Wolfgang Mauerer (QCE, 2023)

Learning Control Policies for Variable Objectives from Offline Data. - Marc Weber; Phillip Swazinna; Daniel Hein 001; Steffen Udluft; Volkmar Sterzing (SSCI, 2023)

Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. - Simon Wiedemann; Daniel Hein 001; Steffen Udluft; Christian B. Mendl (Trans. Mach. Learn. Res., 2023)

Why long model-based rollouts are no reason for bad Q-value estimates. - Philipp Wissmann; Daniel Hein 001; Steffen Udluft; Volker Tresp (ESANN, 2024)

Model-Based Offline Quantum Reinforcement Learning. - Simon Eisenmann; Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (QCE, 2024)

First steps towards real-world traffic signal control optimisation by reinforcement learning. - Henri Meess; Jeremias Gerner; Daniel Hein 001; Stefanie Schmidtner; Gordon Elger; Klaus Bogenberger (J. Simulation, 2024)

Is Q-learning an Ill-posed Problem? - Philipp Wissmann; Daniel Hein 001; Steffen Udluft; Thomas A. Runkler (CoRR, 2025)