Hado van Hasselt

11.9k citations

31 papers · 5.2k indexed · 3 hit papers · h-index 15

Artificial Intelligence top 0.2%
Electrical and Electronic Engineering top 5%
Computer Networks and Communications top 1%
Control and Systems Engineering top 1%
Computer Vision and Pattern Recognition top 1%

Co-authors: David Silver Arthur Guez Matteo Hessel Tom Schaul Marco Wiering Marc Lanctot Ziyu Wang Nando de Freitas
Topics: Reinforcement Learning in Robotics (25 papers)Evolutionary Algorithms and Applications (11 papers)Adaptive Dynamic Programming Control (7 papers)
Cited by: Artificial Intelligence Automotive Engineering Computer Networks and Communications
Journals: Nature IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)University of Groningen research database (University of Groningen / Centre for Information Technology)
Partner nations: United Kingdom Netherlands Canada

In The Last Decade

Hado van Hasselt

30 papers receiving 5.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Reinforcement Learning with Double Q-Learning

2016 2.2k citations Hado van Hasselt, Arthur Guez et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
Dueling Network Architectures for Deep Reinforcement Learning

2015 1.0k citations Tom Schaul, Matteo Hessel et al. arXiv (Cornell University) profile →
Rainbow: Combining Improvements in Deep Reinforcement Learning

2018 995 citations Matteo Hessel, Hado van Hasselt et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers

Countries citing papers authored by Hado van Hasselt

Since Specialization

Citations

This map shows the geographic impact of Hado van Hasselt's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hado van Hasselt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hado van Hasselt more than expected).

Fields of papers citing papers by Hado van Hasselt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hado van Hasselt. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hado van Hasselt. The network helps show where Hado van Hasselt may publish in the future.

Co-authorship network of co-authors of Hado van Hasselt

This figure shows the co-authorship network connecting the top 25 collaborators of Hado van Hasselt. A scholar is included among the top collaborators of Hado van Hasselt based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hado van Hasselt. Hado van Hasselt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Discovering state-of-the-art reinforcement learning algorithms 2025 ·Nature ·Junhyuk Oh,(unknown),(unknown),Dan A. Calian,Matteo Hessel,Luisa Zintgraf,Satinder Singh,Hado van Hasselt,David Silver	1
2	Exploration via Epistemic Value Estimation 2023 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Simon Schmitt,John Shawe‐Taylor,Hado van Hasselt	1
3	Introducing Symmetries to Black Box Meta Reinforcement Learning 2022 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown),(unknown),Hado van Hasselt,(unknown),Junhyuk Oh,Yutian Chen	9
4	Emphatic Algorithms for Deep Reinforcement Learning 2021 ·arXiv (Cornell University) ·(unknown),Tom Zahavy,Zhongwen Xu,Adam White,Matteo Hessel,Charles Blundell,Hado van Hasselt	2
5	Self-Tuning Deep Reinforcement Learning 2020 ·arXiv (Cornell University) ·Tom Zahavy,Zhongwen Xu,Vivek Veeriah,Matteo Hessel,Junhyuk Oh,Hado van Hasselt,David Silver,Satinder Singh	3
6	Multi-Task Deep Reinforcement Learning with PopArt 2019 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Matteo Hessel,Hubert Soyer,Lasse Espeholt,Wojciech Marian Czarnecki,Simon Schmitt,Hado van Hasselt	106
7	Distributed Prioritized Experience Replay 2018 ·International Conference on Learning Representations ·Dan Horgan,John Quan,David Budden,Gabriel Barth-Maron,Matteo Hessel,Hado van Hasselt,David Silver	56
8	Meta-gradient reinforcement learning 2018 ·arXiv (Cornell University) ·Zhongwen Xu,Hado van Hasselt,David Silver	27
9	The predictron: end-to-end learning and planning 2017 ·International Conference on Machine Learning ·David Silver,Hado van Hasselt,Matteo Hessel,Tom Schaul,Arthur Guez,Tim Harley,Gabriel Dulac-Arnold,David Reichert,Neil C. Rabinowitz,André Barreto,Thomas Degris	25
10	Deep Reinforcement Learning with Double Q-Learningbreakdown → 2016 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Hado van Hasselt,Arthur Guez,David Silver	2158
11	A new Q(lambda) with interim forward view and Monte Carlo equivalence 2014 ·International Conference on Machine Learning ·(unknown),(unknown),Doina Precup,Hado van Hasselt	8
12	Off-policy TD(λ) with a true online equivalence 2014 ·Uncertainty in Artificial Intelligence ·Hado van Hasselt,(unknown),Richard S. Sutton	9
13	Double Q-learning 2010 ·Neural Information Processing Systems ·Hado van Hasselt	215
14	Insights in reinforcement ;learning: Formal analysis end empirical evaluation of temporal-difference learning 2010 ·Hado van Hasselt	2
15	The QV family compared to other reinforcement learning algorithms 2009 ·Marco Wiering,Hado van Hasselt	14
16	Using continuous action spaces to solve discrete problems 2009 ·Hado van Hasselt,Marco Wiering	31
17	Ensemble Algorithms in Reinforcement Learning 2008 ·IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ·Marco Wiering,Hado van Hasselt	107
18	Reinforcement Learning in Continuous Action Spaces 2007 ·Utrecht University Repository (Utrecht University) ·Hado van Hasselt,Marco Wiering	121
19	Two Novel On-policy Reinforcement Learning Algorithms based on TD(λ)-methods 2007 ·Marco Wiering,Hado van Hasselt	18
20	Convergence of Model-Based Temporal Difference Learning for Control 2007 ·Utrecht University Repository (Utrecht University) ·Hado van Hasselt,Marco Wiering	1

About Hado van Hasselt

Hado van Hasselt is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics, having authored 31 papers that have together received 5.2k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (25 papers), Evolutionary Algorithms and Applications (11 papers) and Adaptive Dynamic Programming Control (7 papers). The work is most often cited by research in Artificial Intelligence (2.7k citations), Automotive Engineering (598 citations) and Computer Networks and Communications (1.1k citations). Hado van Hasselt has collaborated with scholars based in United Kingdom, Netherlands and Canada. Frequent co-authors include David Silver, Arthur Guez, Matteo Hessel, Tom Schaul, Marco Wiering, Marc Lanctot, Ziyu Wang, Nando de Freitas, Will Dabney and Georg Ostrovski. Their work appears in journals such as Nature, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) and University of Groningen research database (University of Groningen / Centre for Information Technology).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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