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
- Paper Matthew accepted at ICLR (2/18/2026)
Matthew’s paper Gradient-Based Program Synthesis with Neurally Interpreted Languages, with Clément Bonnet and Levi Lelis, was accepted to ICLR! Congrats, Matthew!
- SIKS course on “Reinforcement Learning for Adaptive Hybrid Intelligence” (9/8/2025)
I am co-organizing a two-day graduate course on “Reinforcement Learning for Adaptive Hybrid Intelligence”. We will discuss RL basics and specific challenges for using RL as an assistant or collaborator. Registration and more information via the SIKS website.
- Deadline extensions “AI for safety-critical infrastructure workshop” (6/13/2025)
The deadline is now extended for submissions to the AI for safety-critical infrastructure workshop at ECML PKDD.
📅 15th September 2025
📍Porto, Portugal
⏰ NEW Submission deadline: 21st June 2025
Info and submission 👉 https://lnkd.in/drjkZ5Rt
An archive of news items can be found on the News page.
Highlighted publications
Macfarlane, M.; Bonnet, C.; van Hoof, H.; Lelis, L.: Gradient-Based Program Synthesis with Neurally Interpreted Languages. In: Proceedings of the International Conference on Learning Representations, Forthcoming. @inproceedings{macfarlane2026gradient,
title = {Gradient-Based Program Synthesis with Neurally Interpreted Languages},
author = {Macfarlane, M. and Bonnet, C. and van Hoof, H. and Lelis, L.},
url = {https://openreview.net/forum?id=NAORIWBaoO},
year = {2026},
date = {2026-04-23},
booktitle = {Proceedings of the International Conference on Learning Representations},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
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Kuric, D.; Infante, G.; Gómez, V.; Jonsson, A.; van Hoof, H.: Planning with a Learned Policy Basis to Optimally Solve Complex Tasks. In: International Conference on Automated Planning and Scheduling, 2024. @inproceedings{kuric2024planning,
title = {Planning with a Learned Policy Basis to Optimally Solve Complex Tasks},
author = {Kuric, D. and Infante, G. and Gómez, V. and Jonsson, A. and van Hoof, H. },
url = {https://openreview.net/forum?id=6N1uCtBhcL},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
booktitle = {International Conference on Automated Planning and Scheduling},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Gagrani, Mukul; Rainone, Corrado; Yang, Yang; Teague, Harris; Jeon, Wonseok; Hoof, Herke; Zeng, Weiliang Will; Zappi, Piero; Lott, Christopher; Bondesan, Roberto: Neural Topological Ordering for Computation Graphs. In: Advances in Neural Information Processing Systems, 2022. @inproceedings{gagrani2022neural,
title = {Neural Topological Ordering for Computation Graphs},
author = {Mukul Gagrani and Corrado Rainone and Yang Yang and Harris Teague and Wonseok Jeon and Herke Hoof and Weiliang Will Zeng and Piero Zappi and Christopher Lott and Roberto Bondesan},
url = {https://arxiv.org/abs/2207.05899},
year = {2022},
date = {2022-11-29},
booktitle = {Advances in Neural Information Processing Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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Kool, Wouter; Hoof, Herke Van; Welling, Max: Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. In: International Conference on Machine Learning, pp. 3499–3508, 2019. @inproceedings{kool2019stochastic,
title = {Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement},
author = {Wouter Kool and Herke Van Hoof and Max Welling},
url = {http://proceedings.mlr.press/v97/kool19a/kool19a.pdf
http://proceedings.mlr.press/v97/kool19a/kool19a-supp.pdf},
year = {2019},
date = {2019-06-10},
urldate = {2019-06-10},
booktitle = {International Conference on Machine Learning},
pages = {3499–3508},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Hoof, H. Van; Neumann, G.; Peters, J.: Non-parametric Policy Search with Limited Information Loss. In: Journal of Machine Learning Research, vol. 18, no. 73, pp. 1-46, 2017. @article{hoof2017nonparametric,
title = {Non-parametric Policy Search with Limited Information Loss},
author = {H. Van Hoof and G. Neumann and J. Peters},
editor = {K. Murphy},
url = {http://jmlr.org/papers/volume18/16-142/16-142.pdf},
year = {2017},
date = {2017-08-01},
journal = {Journal of Machine Learning Research},
volume = {18},
number = {73},
pages = {1-46},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|