| Interpretable policies for reinforcement learning by genetic programming D Hein, S Udluft, TA Runkler Engineering Applications of Artificial Intelligence 76, 158-169, 2018 | 115 | 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 | 79 | 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 | 58 | 2018 |
| 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 | 24 | 2016 |
| 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 | 15 | 2022 |
| 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 | 15 | 2017 |
| 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 | 13 | 2018 |
| Interpretable Control by Reinforcement Learning D Hein, S Limmer, TA Runkler IFAC-PapersOnLine 53 (2), 8082-8089, 2020 | 10 | 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 | 8 | 2018 |
| 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 | 6 | 2022 |
| Generating interpretable reinforcement learning policies using genetic programming D Hein, S Udluft, TA Runkler Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 6 | 2019 |
| Introduction to the" Industrial Benchmark" D Hein, A Hentschel, V Sterzing, M Tokic, S Udluft arXiv preprint arXiv:1610.03793, 2016 | 6 | 2016 |
| Behavior constraining in weight space for offline reinforcement learning P Swazinna, S Udluft, D Hein, T Runkler arXiv preprint arXiv:2107.05479, 2021 | 5 | 2021 |
| Interpretable Reinforcement Learning Policies by Evolutionary Computation D Hein Technische Universität München, 2019 | 5 | 2019 |
| 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 |
Thomas RunklerProfessor of Computer Science, Siemens AG, Technische Universität MünchenGeverifieerd e-mailadres voor siemens.com
