Iterative Oblique Decision Trees Deliver Explainable RL Models RC Engelhardt, M Oedingen, M Lange, L Wiskott, W Konen Algorithms 16 (6), 282, 2023 | 2 | 2023 |
Sample-Based Rule Extraction for Explainable Reinforcement Learning RC Engelhardt, M Lange, L Wiskott, W Konen International Conference on Machine Learning, Optimization, and Data Science …, 2022 | 2 | 2022 |
Shedding Light into the Black Box of Reinforcement Learning RC Engelhardt, M Lange, L Wiskott, W Konen Workshop “Trustworthy AI in the wild”, held at 44th German Conference on …, 2021 | 2 | 2021 |
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison M Lange, N Krystiniak, RC Engelhardt, W Konen, L Wiskott International Conference on Machine Learning, Optimization, and Data Science …, 2023 | 1 | 2023 |
Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks M Lange, RC Engelhardt, W Konen, L Wiskott arXiv preprint arXiv:2402.12067, 2024 | | 2024 |
Comparing Auxiliary Tasks for Learning Representations for Reinforcement Learning M Lange, N Krystiniak, RC Engelhardt, W Konen, L Wiskott arXiv preprint arXiv:2310.04241, 2023 | | 2023 |
Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning RC Engelhardt, R Raycheva, M Lange, L Wiskott, W Konen International Conference on Machine Learning, Optimization, and Data Science …, 2023 | | 2023 |
Finding the Relevant Samples for Decision Trees in Reinforcement Learning RC Engelhardt, M Lange, L Wiskott, W Konen https://dataninja.nrw/?page_id=1251, 2023 | | 2023 |