World's Best Scientists 2026 revealed!

Overview

Hado van Hasselt is affiliated with University College London in the United Kingdom. Their research primarily focuses on computer science, with a significant portion of their work concentrated in artificial intelligence. Additional subfields of their research include management science and operations research, electrical and electronic engineering, computational theory and mathematics, and computer vision and pattern recognition.

The scientist's main research topics cover a variety of areas related to machine learning and optimization techniques. These topics include reinforcement learning in robotics, data stream mining techniques, adversarial robustness in machine learning, domain adaptation and few-shot learning, advanced bandit algorithms research, smart grid energy management, and evolutionary algorithms and applications.

Hado van Hasselt has published extensively, with a notable number of papers appearing in the venue arXiv (Cornell University), followed by the Proceedings of the AAAI Conference on Artificial Intelligence and Nature. Recent papers include:

  • "Discovering Reinforcement Learning Algorithms" (2020), published in arXiv (Cornell University)
  • "Meta-Gradient Reinforcement Learning with an Objective Discovered Online" (2020), published in arXiv (Cornell University)
  • "A Self-Tuning Actor-Critic Algorithm" (2020), published in arXiv (Cornell University)
  • "Introducing Symmetries to Black Box Meta Reinforcement Learning" (2022), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Muesli: Combining Improvements in Policy Optimization" (2021), published in arXiv (Cornell University)

The scientist has frequently collaborated with several co-authors throughout their career. Notable co-authors include:

  • Matteo Hessel
  • David Silver
  • Junhyuk Oh
  • Satinder Singh
  • John Shawe-Taylor

Best Publications

  • Deep reinforcement learning with double Q-Learning

    Hado van Hasselt;Arthur Guez;David Silver

  • Dueling network architectures for deep reinforcement learning

    Ziyu Wang;Tom Schaul;Matteo Hessel;Hado Van Hasselt

  • Rainbow: Combining Improvements in Deep Reinforcement Learning

    Matteo Hessel;Joseph Modayil;Hado van Hasselt;Tom Schaul

  • Double Q-learning

    Hado V. Hasselt

  • StarCraft II: A New Challenge for Reinforcement Learning

    Oriol Vinyals;Timo Ewalds;Sergey Bartunov;Petko Georgiev

  • Distributed Prioritized Experience Replay

    Dan Horgan;John Quan;David Budden;Gabriel Barth-Maron

  • Successor Features for Transfer in Reinforcement Learning

    Andre Barreto;Will Dabney;Remi Munos;Jonathan J. Hunt

  • Proceedings of the International Joint Conference on Neural Networks

    Hado van Hasselt;Marco Wiering

  • Reinforcement Learning in Continuous State and Action Spaces

    Hado P. van Hasselt

  • A theoretical and empirical analysis of Expected Sarsa

    Harm van Seijen;Hado van Hasselt;Shimon Whiteson;Marco Wiering

  • Ensemble Algorithms in Reinforcement Learning

    M.A. Wiering;H. van Hasselt

  • The predictron: end-to-end learning and planning

    David Silver;Hado van Hasselt;Matteo Hessel;Tom Schaul

  • Multi-task Deep Reinforcement Learning with PopArt

    Matteo Hessel;Hubert Soyer;Lasse Espeholt;Wojciech Czarnecki

  • Meta-gradient reinforcement learning

    Zhongwen Xu;Hado van Hasselt;David Silver

  • Learning values across many orders of magnitude

    Hado van Hasselt;Arthur Guez;Matteo Hessel;Volodymyr Mnih

  • Deep Reinforcement Learning and the Deadly Triad

    Hado van Hasselt;Yotam Doron;Florian Strub;Matteo Hessel

  • Weighted importance sampling for off-policy learning with linear function approximation

    A. Rupam Mahmood;Hado P van Hasselt;Richard S Sutton

  • Observe and Look Further: Achieving Consistent Performance on Atari

    Tobias Pohlen;Bilal Piot;Todd Hester;Mohammad Gheshlaghi Azar

  • When to use parametric models in reinforcement learning

    Hado P. van Hasselt;Matteo Hessel;John Aslanides

  • Behaviour Suite for Reinforcement Learning

    Ian Osband;Yotam Doron;Matteo Hessel;John Aslanides

Frequent Co-Authors

David Silver
David Silver DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Marco Wiering
Marco Wiering University of Groningen
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Richard S. Sutton
Richard S. Sutton University of Alberta
Shimon Whiteson
Shimon Whiteson University of Oxford
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Lambert Schomaker
Lambert Schomaker University of Groningen
Doina Precup
Doina Precup McGill University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

With the rise of digital education, pursuing a Computer Science career in the USA is more accessible than ever. Students can choose from a wide range of affordable, accredited online degrees and certifications that align with their interests and professional goals.

For those who want to broaden their technical expertise, exploring an online physics degree can provide a solid foundation in problem-solving and analytical thinking. If you are interested in interpreting and leveraging data, a data scientist degree from a U.S. institution opens doors to one of today’s fastest-growing tech fields.

Engineering enthusiasts can also consider enrolling in one of the top online electrical engineering schools for broader career options in technology and innovation.

Additionally, if you’re seeking a faster path to a lucrative career, there are a variety of quick certifications that pay well. These options allow learners to quickly gain marketable skills aligned with industry demands.

Best Scientists Citing Hado van Hasselt

Trending Scientists

Recently Published Articles