| Interpretable policies for reinforcement learning by genetic programming D Hein, S Udluft, TA Runkler Engineering Applications of Artificial Intelligence 76, 158-169, 2018 | 150 | 2018 |
| 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 | 96 | 2017 |
| 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 | 69 | 2018 |
| Comparing model-free and model-based algorithms for offline reinforcement learning P Swazinna, S Udluft, D Hein, T Runkler IFAC-PapersOnLine 55 (15), 19-26, 2022 | 33 | 2022 |
| 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 | 32 | 2016 |
| 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 | 23 | 2018 |
| 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 | 18 | 2017 |
| 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 | 14 | 2022 |
| Interpretable Control by Reinforcement Learning D Hein, S Limmer, TA Runkler IFAC-PapersOnLine 53 (2), 8082-8089, 2020 | 14 | 2020 |
| 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 | 9 | 2018 |
| Generating interpretable reinforcement learning policies using genetic programming D Hein, S Udluft, TA Runkler Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 8 | 2019 |
| Interpretable Reinforcement Learning Policies by Evolutionary Computation D Hein Technische Universität München, 2019 | 7 | 2019 |
| Learning Control Policies for Variable Objectives from Offline Data M Weber, P Swazinna, D Hein, S Udluft, V Sterzing 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 1674-1681, 2023 | 6 | 2023 |
| Behavior constraining in weight space for offline reinforcement learning P Swazinna, S Udluft, D Hein, T Runkler arXiv preprint arXiv:2107.05479, 2021 | 6 | 2021 |
| Introduction to the" Industrial Benchmark" D Hein, A Hentschel, V Sterzing, M Tokic, S Udluft arXiv preprint arXiv:1610.03793, 2016 | 6 | 2016 |
| Computer-aided control and/or regulation of a technical system S Düll, D Hein, A Hentschel, T Runkler, S Udluft US Patent 10,107,205, 2018 | 2 | 2018 |
| Model-based Offline Quantum Reinforcement Learning S Eisenmann, D Hein, S Udluft, TA Runkler arXiv preprint arXiv:2404.10017, 2024 | 1 | 2024 |
| 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 | 1 | 2021 |
| Why long model-based rollouts are no reason for bad Q-value estimates P Wissmann, D Hein, S Udluft, V Tresp arXiv preprint arXiv:2407.11751, 2024 | | 2024 |
| Workshop Summary: Quantum Machine Learning V Tresp, S Udluft, D Hein, W Hauptmann, M Leib, C Mutschler, ... 2023 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2023 | | 2023 |
Thomas RunklerProfessor of Computer Science, Siemens AG, Technische Universität MünchenGeverifieerd e-mailadres voor siemens.com
