Matteo Hessel

According to our database, Matteo Hessel authored at least 34 papers between 2014 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Learning by Directional Gradient Descent.

David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Podracer architectures for scalable Reinforcement Learning.

Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt

CoRR, 2021

Discovery of Options via Meta-Learned Subgoals.

Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-Consistent Models and Values.

Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado Philip van Hasselt, David Silver

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Emphatic Algorithms for Deep Reinforcement Learning.

Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt

Proceedings of the 38th International Conference on Machine Learning, 2021

Muesli: Combining Improvements in Policy Optimization.

Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt

Proceedings of the 38th International Conference on Machine Learning, 2021

Expected Eligibility Traces.

Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Self-Tuning Deep Reinforcement Learning.

Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh

CoRR, 2020

A Self-Tuning Actor-Critic Algorithm.

Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Meta-Gradient Reinforcement Learning with an Objective Discovered Online.

Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Discovering Reinforcement Learning Algorithms.

Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

What Can Learned Intrinsic Rewards Capture?

Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the 37th International Conference on Machine Learning, 2020

Off-Policy Actor-Critic with Shared Experience Replay.

Simon Schmitt, Matteo Hessel, Karen Simonyan

Proceedings of the 37th International Conference on Machine Learning, 2020

Behaviour Suite for Reinforcement Learning.

Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

General non-linear Bellman equations.

Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto

CoRR, 2019

On Inductive Biases in Deep Reinforcement Learning.

Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver

CoRR, 2019

Discovery of Useful Questions as Auxiliary Tasks.

Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

When to use parametric models in reinforcement learning?

Hado van Hasselt, Matteo Hessel, John Aslanides

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Multi-Task Deep Reinforcement Learning with PopArt.

Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Scaling shared model governance via model splitting.

Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli

CoRR, 2018

Deep Reinforcement Learning and the Deadly Triad.

Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, Joseph Modayil

CoRR, 2018

Observe and Look Further: Achieving Consistent Performance on Atari.

Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Vecerík, Matteo Hessel, Rémi Munos, Olivier Pietquin

CoRR, 2018

Unicorn: Continual Learning with a Universal, Off-policy Agent.

Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul

CoRR, 2018

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos

Proceedings of the 35th International Conference on Machine Learning, 2018

Distributed Prioritized Experience Replay.

Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

Proceedings of the 6th International Conference on Learning Representations, 2018

Noisy Networks For Exploration.

Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

Proceedings of the 6th International Conference on Learning Representations, 2018

Rainbow: Combining Improvements in Deep Reinforcement Learning.

Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

The Predictron: End-To-End Learning and Planning.

David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Learning functions across many orders of magnitudes.

Hado van Hasselt, Arthur Guez, Matteo Hessel, David Silver

CoRR, 2016

Learning values across many orders of magnitude.

Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Dueling Network Architectures for Deep Reinforcement Learning.

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

Proceedings of the 33nd International Conference on Machine Learning, 2016

2014

Automatic Tuning of Computational Models.

Matteo Hessel, Fabio Ortalli, Francesco Borgatelli, Pier Luca Lanzi

Proceedings of the Simulation and Modeling Methodologies, Technologies and Applications, 2014

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

Proceedings of the 4th International Conference On Simulation And Modeling Methodologies, 2014

Machine Learning for Parameter Screening in Computer Simulations.

Matteo Hessel, Fabio Ortalli, Francesco Borgatelli

Proceedings of the Modelling and Simulation for Autonomous Systems, 2014