Matteo Hessel

Most-cited papers

Dueling Network Architectures for Deep Reinforcement Learning
Zi-Yun Wang, T. Schaul, Matteo Hessel, H. V. Hasselt +2 more
2015International Conference on Machine Learning4,641 citations
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Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, E. Bieber, Mike Schaekermann, Ice Pasupat +496 more
2025arXiv.org4,448 citations
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Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, H. V. Hasselt, T. Schaul +6 more
2017AAAI Conference on Artificial Intelligence2,743 citations Open access
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Noisy Networks for Exploration
Meire Fortunato, M. G. Azar, Bilal Piot, Jacob Menick +9 more
2017International Conference on Learning Representations1,053 citations
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Distributed Prioritized Experience Replay
Dan Horgan, John Quan, D. Budden, Gabriel Barth-Maron +3 more
2018International Conference on Learning Representations860 citations
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Multi-task Deep Reinforcement Learning with PopArt
Matteo Hessel, Hubert Soyer, L. Espeholt, W. Czarnecki +2 more
2018AAAI Conference on Artificial Intelligence376 citations Open access
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The Predictron: End-To-End Learning and Planning
David Silver, H. V. Hasselt, Matteo Hessel, T. Schaul +7 more
2016International Conference on Machine Learning315 citations
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Deep Reinforcement Learning and the Deadly Triad
H. V. Hasselt, Yotam Doron, Florian Strub, Matteo Hessel +2 more
2018arXiv.org297 citations
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Learning values across many orders of magnitude
H. V. Hasselt, A. Guez, Matteo Hessel, Volodymyr Mnih +1 more
2016Neural Information Processing Systems223 citations
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Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides +10 more
2019International Conference on Learning Representations222 citations
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When to use parametric models in reinforcement learning?
H. V. Hasselt, Matteo Hessel, John Aslanides
2019Neural Information Processing Systems215 citations
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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
André Barreto, Diana Borsa, John Quan, T. Schaul +5 more
2018International Conference on Machine Learning206 citations
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Discovering Reinforcement Learning Algorithms
Junhyuk Oh, Matteo Hessel, W. Czarnecki, Zhong-Wen Xu +3 more
2020Neural Information Processing Systems156 citations
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Observe and Look Further: Achieving Consistent Performance on Atari
Tobias Pohlen, Bilal Piot, Todd Hester, M. G. Azar +9 more
2018arXiv.org130 citations
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A Self-Tuning Actor-Critic Algorithm
Tom Zahavy, Zhong-Wen Xu, Vivek Veeriah, Matteo Hessel +4 more
2020Neural Information Processing Systems106 citations
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Discovery of Useful Questions as Auxiliary Tasks
Vivek Veeriah, Matteo Hessel, Zhong-Wen Xu, Janarthanan Rajendran +5 more
2019Neural Information Processing Systems91 citations
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Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Zhong-Wen Xu, H. V. Hasselt, Matteo Hessel, Junhyuk Oh +2 more
2020Neural Information Processing Systems90 citations
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What Can Learned Intrinsic Rewards Capture?
Ze-Yu Zheng, Junhyuk Oh, Matteo Hessel, Zhong-Wen Xu +4 more
2019International Conference on Machine Learning88 citations
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Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt, Matteo Hessel, K. Simonyan
2019International Conference on Machine Learning73 citations
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Muesli: Combining Improvements in Policy Optimization
Matteo Hessel, I. Danihelka, Fabio Viola, A. Guez +5 more
2021International Conference on Machine Learning70 citations
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Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Sebastian Gehrmann, H. Strobelt, Alexander M. Rush, Fabio Kepler +496 more
201955 citations Open access
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Podracer architectures for scalable Reinforcement Learning
Matteo Hessel, M. Kroiss, Aidan Clark, Iurii Kemaev +4 more
2021arXiv.org52 citations
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Expected Eligibility Traces
H. V. Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver +2 more
2020AAAI Conference on Artificial Intelligence51 citations Open access
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On Inductive Biases in Deep Reinforcement Learning
Matteo Hessel, H. V. Hasselt, Joseph Modayil, David Silver
2019arXiv.org50 citations
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Learning by Directional Gradient Descent
David Silver, Anirudh Goyal, I. Danihelka, Matteo Hessel +1 more
2022International Conference on Learning Representations49 citations
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Unicorn: Continual Learning with a Universal, Off-policy Agent
D. Mankowitz, A. Žídek, André Barreto, Dan Horgan +6 more
2018arXiv.org49 citations
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Discovering state-of-the-art reinforcement learning algorithms
Junhyuk Oh, Gregory Farquhar, Iurii Kemaev, Dan A. Calian +5 more
2025Nature47 citations Open access
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Discovery of Options via Meta-Learned Subgoals
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhong-Wen Xu +5 more
2021Neural Information Processing Systems38 citations
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Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhong-Wen Xu, Adam White +3 more
2021International Conference on Machine Learning26 citations
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DataRater: Meta-Learned Dataset Curation
Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf +8 more
2025Neural Information Processing Systems20 citations
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Learning functions across many orders of magnitudes
H. V. Hasselt, A. Guez, Matteo Hessel, David Silver
2016arXiv.org17 citations
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Self-Tuning Deep Reinforcement Learning
Tom Zahavy, Zhong-Wen Xu, Vivek Veeriah, Matteo Hessel +4 more
2020arXiv.org15 citations
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General non-linear Bellman equations
H. V. Hasselt, John Quan, Matteo Hessel, Zhong-Wen Xu +2 more
2019arXiv.org14 citations
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Self-Consistent Models and Values
Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos +3 more
2021Neural Information Processing Systems11 citations
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Scaling shared model governance via model splitting
Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel +2 more
2018arXiv.org2 citations
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Machine Learning for Parameter Screening in Computer Simulations
Matteo Hessel, Fabio Ortalli, Francesco Borgatelli
2014International Workshop on Modelling and Simulation for Autonomous Systems2 citations
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A novel approach to model design and tuning through automatic parameter screening and optimization theory and application to a helicopter flight simulator case-study
Matteo Hessel, Francesco Borgatelli, Fabio Ortalli
2014International Conference on Simulation and Modeling Methodologies, Technologies and Applications2 citations Open access
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Automatic Tuning of Computational Models
Matteo Hessel, Fabio Ortalli, Francesco Borgatelli, P. Lanzi
2014International Conference on Simulation and Modeling Methodologies, Technologies and Applications1 citations
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Simulation and Modeling Methodologies, Technologies and Applications
M. Obaidat, T. Ören, J. Kacprzyk, Anderson Luís +59 more
2020Advances in Intelligent Systems and Computing0 citations
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Intrinsic Reward Episode 1 Episode 2 Lifetime with task
Ze-Yu Zheng, Junhyuk Oh, Matteo Hessel, Zhong-Wen Xu +4 more
20190 citations
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Towards Consistent Performance on Atari using Expert Demonstrations
Tobias Pohlen, Bilal Piot, Todd Hester, M. G. Azar +9 more
20180 citations
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