Dynamic Algorithm Configuration
| Leitung: | Prof. Dr. Marius Lindauer |
| Jahr: | 2019 |
| Förderung: | DFG |
| Laufzeit: | 2019-2023 |
| Weitere Informationen | https://www.automl.org/automated-algorithm-design/dac/ |
Gefördert von
Publikation
Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor Awad, Theresa Eimer, Marius Lindauer, Frank Hutter. Automated Dynamic Algorithm Configuration ArXiv, May 2022 (arXiv) BibTeX
André Biedenkapp, David Speck, Silvan Sievers, Frank Hutter, Marius Lindauer, Jendrik Seipp.
Learning Domain-Independent Policies for Open List Selection. Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1-9, 2022 BibTeX
Andre Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer. TempoRL: Learning When to Act. Proceedings of the international conference on machine learning (ICML), July 2021 (arXiv) BibTeX
Gresa Shala, Andre Biedenkapp, Noor Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter. Learning Step-Size Adaptation in CMA-ES. Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}'20), September 2020 (pdf, arXiv) BibTeX
Theresa Eimer, Andre Biedenkapp, Frank Hutter, Marius Lindauer. Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning. Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20), July 2020. (pdf) BibTeX
Andre Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer. Towards TempoRL: Learning When to Act. Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20), July 2020 (pdf) BibTeX
David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
