- RUB
- Computer Science
- INI
- People
- Moritz Lange, M.Sc.
Moritz Lange, M.Sc.
Theory of Neural Systems
Theory of Neural Systems
Ruhr-Universität Bochum
Institut für Neuroinformatik
Universitätsstraße 150
Building NB, Room NB 3/35
Universitätsstraße 150
Building NB, Room NB 3/35
D-44801 Bochum, Germany
Selected Publications
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Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation TasksLange, M., Engelhardt, R. C., Konen, W., & Wiskott, L.In eXplainable AI approaches for Deep Reinforcement Learning
@inproceedings{LangeEngelhardtKonenEtAl2024, author = {Lange, Moritz and Engelhardt, Raphael C. and Konen, Wolfgang and Wiskott, Laurenz}, title = {Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks}, booktitle = {eXplainable AI approaches for Deep Reinforcement Learning}, year = {2024}, }Lange, M., Engelhardt, R. C., Konen, W., & Wiskott, L.. (2024). Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks. In eXplainable AI approaches for Deep Reinforcement Learning. Retrieved from https://openreview.net/forum?id=s1oVgaZ3dQ*Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A ComparisonLange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 177–191) Cham: Springer Nature Switzerland@inproceedings{LangeKrystiniakEngelhardtEtAl2024, author = {Lange, Moritz and Krystiniak, Noah and Engelhardt, Raphael C. and Konen, Wolfgang and Wiskott, Laurenz}, title = {*Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison}, booktitle = {Machine Learning, Optimization, and Data Science}, editor = {Nicosia, Giuseppe and Ojha, Varun and La Malfa, Emanuele and La Malfa, Gabriele and Pardalos, Panos M. and Umeton, Renato}, pages = {177–191}, publisher = {Springer Nature Switzerland}, address = {Cham}, year = {2024}, doi = {10.1007/978-3-031-53966-4_14}, }Lange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.. (2024). *Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison. In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 177–191). Cham: Springer Nature Switzerland. http://doi.org/10.1007/978-3-031-53966-4_142023
Benchmarks for Physical Reasoning AIMelnik, A., Schiewer, R., Lange, M., Muresanu, A. I., Saeidi, M., Garg, A., & Ritter, H.Transactions on Machine Learning Research@article{MelnikSchiewerLangeEtAl2023, author = {Melnik, Andrew and Schiewer, Robin and Lange, Moritz and Muresanu, Andrei Ioan and Saeidi, Mozhgan and Garg, Animesh and Ritter, Helge}, title = {Benchmarks for Physical Reasoning AI}, journal = {Transactions on Machine Learning Research}, year = {2023}, }Melnik, A., Schiewer, R., Lange, M., Muresanu, A. I., Saeidi, M., Garg, A., & Ritter, H. (2023). Benchmarks for Physical Reasoning AI. Transactions on Machine Learning Research. Retrieved from https://openreview.net/forum?id=cHroS8VIyN-
Ökolopoly: Case Study on Large Action Spaces in Reinforcement LearningEngelhardt, R. C., Raycheva, R., Lange, M., Wiskott, L., & Konen, W.In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 109–123) Cham: Springer Nature Switzerland
@inproceedings{EngelhardtRaychevaLangeEtAl2024, author = {Engelhardt, Raphael C. and Raycheva, Ralitsa and Lange, Moritz and Wiskott, Laurenz and Konen, Wolfgang}, title = {Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning}, booktitle = {Machine Learning, Optimization, and Data Science}, editor = {Nicosia, Giuseppe and Ojha, Varun and La Malfa, Emanuele and La Malfa, Gabriele and Pardalos, Panos M. and Umeton, Renato}, pages = {109–123}, publisher = {Springer Nature Switzerland}, address = {Cham}, year = {2024}, }Engelhardt, R. C., Raycheva, R., Lange, M., Wiskott, L., & Konen, W. (2024). Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning. In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 109–123). Cham: Springer Nature Switzerland.Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation TasksLange, M., Engelhardt, R. C., Konen, W., & Wiskott, L.In eXplainable AI approaches for Deep Reinforcement Learning@inproceedings{LangeEngelhardtKonenEtAl2024, author = {Lange, Moritz and Engelhardt, Raphael C. and Konen, Wolfgang and Wiskott, Laurenz}, title = {Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks}, booktitle = {eXplainable AI approaches for Deep Reinforcement Learning}, year = {2024}, }Lange, M., Engelhardt, R. C., Konen, W., & Wiskott, L.. (2024). Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks. In eXplainable AI approaches for Deep Reinforcement Learning. Retrieved from https://openreview.net/forum?id=s1oVgaZ3dQ*Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A ComparisonLange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 177–191) Cham: Springer Nature Switzerland@inproceedings{LangeKrystiniakEngelhardtEtAl2024, author = {Lange, Moritz and Krystiniak, Noah and Engelhardt, Raphael C. and Konen, Wolfgang and Wiskott, Laurenz}, title = {*Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison}, booktitle = {Machine Learning, Optimization, and Data Science}, editor = {Nicosia, Giuseppe and Ojha, Varun and La Malfa, Emanuele and La Malfa, Gabriele and Pardalos, Panos M. and Umeton, Renato}, pages = {177–191}, publisher = {Springer Nature Switzerland}, address = {Cham}, year = {2024}, doi = {10.1007/978-3-031-53966-4_14}, }Lange, M., Krystiniak, N., Engelhardt, R. C., Konen, W., & Wiskott, L.. (2024). *Best Paper Award* Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison. In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M., & Umeton, R. (Eds.), Machine Learning, Optimization, and Data Science (pp. 177–191). Cham: Springer Nature Switzerland. http://doi.org/10.1007/978-3-031-53966-4_142023
Sample-Based Rule Extraction for Explainable Reinforcement LearningEngelhardt, R. C., Lange, M., Wiskott, L., & Konen, W.In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P., Di Fatta, G., et al. (Eds.), Machine Learning, Optimization, and Data Science (pp. 330–345) Cham: Springer Nature Switzerland@inproceedings{EngelhardtLangeWiskottEtAl2023, author = {Engelhardt, Raphael C. and Lange, Moritz and Wiskott, Laurenz and Konen, Wolfgang}, title = {Sample-Based Rule Extraction for Explainable Reinforcement Learning}, booktitle = {Machine Learning, Optimization, and Data Science}, editor = {Nicosia, Giuseppe and Ojha, Varun and La Malfa, Emanuele and La Malfa, Gabriele and Pardalos, Panos and Di Fatta, Giuseppe and Giuffrida, Giovanni and Umeton, Renato}, pages = {330–345}, publisher = {Springer Nature Switzerland}, address = {Cham}, year = {2023}, }Engelhardt, R. C., Lange, M., Wiskott, L., & Konen, W. (2023). Sample-Based Rule Extraction for Explainable Reinforcement Learning. In G. Nicosia, Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P., Di Fatta, G., et al. (Eds.), Machine Learning, Optimization, and Data Science (pp. 330–345). Cham: Springer Nature Switzerland.Iterative Oblique Decision Trees Deliver Explainable RL ModelsEngelhardt, R. C., Oedingen, M., Lange, M., Wiskott, L., & Konen, W.Algorithms, 16(6)@article{EngelhardtOedingenLangeEtAl2023, author = {Engelhardt, Raphael C. and Oedingen, Marc and Lange, Moritz and Wiskott, Laurenz and Konen, Wolfgang}, title = {Iterative Oblique Decision Trees Deliver Explainable RL Models}, journal = {Algorithms}, volume = {16}, number = {6}, year = {2023}, doi = {10.3390/a16060282}, }Engelhardt, R. C., Oedingen, M., Lange, M., Wiskott, L., & Konen, W. (2023). Iterative Oblique Decision Trees Deliver Explainable RL Models. Algorithms, 16(6). http://doi.org/10.3390/a16060282Benchmarks for Physical Reasoning AIMelnik, A., Schiewer, R., Lange, M., Muresanu, A. I., Saeidi, M., Garg, A., & Ritter, H.Transactions on Machine Learning Research@article{MelnikSchiewerLangeEtAl2023, author = {Melnik, Andrew and Schiewer, Robin and Lange, Moritz and Muresanu, Andrei Ioan and Saeidi, Mozhgan and Garg, Animesh and Ritter, Helge}, title = {Benchmarks for Physical Reasoning AI}, journal = {Transactions on Machine Learning Research}, year = {2023}, }Melnik, A., Schiewer, R., Lange, M., Muresanu, A. I., Saeidi, M., Garg, A., & Ritter, H. (2023). Benchmarks for Physical Reasoning AI. Transactions on Machine Learning Research. Retrieved from https://openreview.net/forum?id=cHroS8VIyNSummer Term 2023
Lab courses Introduction to Python Summer Term 2022
Lab courses Introduction to Python The Institut für Neuroinformatik (INI) is a research unit of the Faculty of Computer Science at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, GermanyTel: (+49) 234 32-28967
Fax: (+49) 234 32-14210
2024