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

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Key References

Wang, Qi ; van Hoof, Herke

Learning Expressive Meta-Representations with Mixture of Expert Neural Processes

Advances in Neural Information Processing Systems, 2022.

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Gagrani, Mukul ; Rainone, Corrado ; Yang, Yang ; Teague, Harris ; Jeon, Wonseok ; van Hoof, Herke ; Zeng, Weiliang Will ; Zappi, Piero ; Lott, Christopher ; Bondesan, Roberto

Neural Topological Ordering for Computation Graphs

Advances in Neural Information Processing Systems, 2022.

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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.

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Höpner, Niklas ; Tiddi, Ilaria ; van Hoof, Herke

Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods

International Joint Conference on Artificial Intelligence, 2022.

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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.

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