Bio
Herke van Hoof is currently associate professor at the University of Amsterdam in the Netherlands, where he is part of the Amlab. He is interested in modular reinforcement learning. Reinforcement learning is a very general framework, but this tends to result in extremely data-hungry algorithms. Exploiting modular structures, including hierarchical structures, allows sharing information between tasks and exploiting prior knowledge, to learn more with less data.
Before joining the University of Amsterdam, Herke van Hoof was a postdoc at McGill University in Montreal, Canada, where he worked with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. He obtained his PhD at TU Darmstadt, Germany, under the supervision of Professor Jan Peters, where he graduated in November 2016. Herke got his bachelor and master degrees in Artificial Intelligence at the University of Groningen in the Netherlands.
Recent news
- Sebastiaan de Peuter to join our team as postdoc (3/26/2025)
Sebastiaan de Peuter will be joining the team as a postdoc. He will be working on various topics related to the interaction of learning agents and human operators within the AI4REALNET project. I’m looking forward to the collaboration!
- New papers accepted at AAMAS and AAAI (1/15/2025)
David and Niklas recently had their papers accepted for presentation at AAAI and at AAMAS. Congratulations to both! Pre-prints will follow soon!
- Congratulations, Matthew! (12/11/2024)
An archive of news items can be found on the News page.
Highlighted publications
| : Planning with a Learned Policy Basis to Optimally Solve Complex Tasks. In: International Conference on Automated Planning and Scheduling, 2024. |
| : Neural Topological Ordering for Computation Graphs. In: Advances in Neural Information Processing Systems, 2022. |
| : Multi-Agent MDP Homomorphic Networks. In: Proceedings of the International Conference on Learning Representations, 2022. |
| : Estimating Gradients for Discrete Random Variables by Sampling without Replacement. In: International Conference on Learning Representations, 2020. |
| : An Inference-Based Policy Gradient Method for Learning Options. In: International Conference on Machine Learning, pp. 4703-4712, 2018. |
| : Non-parametric Policy Search with Limited Information Loss. In: Journal of Machine Learning Research, vol. 18, no. 73, pp. 1-46, 2017. |