Herke van Hoof is currently assistant professor at the University of Amsterdam in the Netherlands, where he is part of the Amlab. He is interested in reinforcement learning with structured data and prior knowledge. Reinforcement learning is a very general framework, but this tends to result in extremely data-hungry algorithms. Exploiting structured prior knowledge, or using value function or policy parametrizations that respect known structural properties, is a promising avenue to learn more with less data. Examples of this line of work include reinforcement learning (RL) for combinatorial optimisation, RL with symbolic prior knowledge, and equivariant RL.  

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

  • ICML paper accepted (5/16/2022)

    Qi’s ICML paper on model-based meta learning got accepted. Congratulation, Qi! A pre-print is available on here.

  • Two IJCAI papers accepted (4/21/2022)

    Two of our IJCAI submissions were accepted:
    With Niklas Höpner & Ilaria Tiddi: Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods (pre-print)
    With Jan Wöhlke & Felix Schmitt: Value Refinement Network (VRN) (earlier version)
    Congrats Jan & Niklas!

  • New working paper on class abstractions in RL (2/4/2022)

    Niklas Höpner has recently posted his working paper on leveraging class abstraction for commonsense reinforcement learning. It is available on the arXiv.

An archive of news items can be found on the News page.

Key References

van der Pol, Elise ; van Hoof, Herke ; Oliehoek, Frans ; Welling, Max

Multi-Agent MDP Homomorphic Networks

Proceedings of the International Conference on Learning Representations, 2022.

Links | BibTeX

Kool, Wouter ; van Hoof, Herke ; Welling, Max

Estimating Gradients for Discrete Random Variables by Sampling without Replacement

International Conference on Learning Representations, 2020.

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Smith, M; van Hoof, H; Pineau, J

An Inference-Based Policy Gradient Method for Learning Options

International Conference on Machine Learning, pp. 4703-4712, 2018.

Links | BibTeX

Van Hoof, H; Neumann, G; Peters, J

Non-parametric Policy Search with Limited Information Loss

Journal of Machine Learning Research, 18 (73), pp. 1-46, 2017.

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Bakker, Tim ; van Hoof, Herke ; Welling, Max

Experimental design for MRI by greedy policy search

Advances in Neural Information Processing Systems, 2020.

Links | BibTeX

A full list of publications can be found at the Publications page.