Matteo Hessel - RankMe

53 papers A* 19C 1Journal 28Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	DataRater: Meta-Learned Dataset Curation. CoRR Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeffrey Dean, Hado van Hasselt, David Silver
2025	J	`jnl`	Discovering state-of-the-art reinforcement learning algorithms. Nat. Junhyuk Oh, Gregory Farquhar, Iurii Kemaev, Dan A. Calian, Matteo Hessel, Luisa M. Zintgraf, Satinder Singh, Hado van Hasselt, David Silver
2022	A*	`conf`	Learning by Directional Gradient Descent. ICLR David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt
2021	A*	`conf`	Discovery of Options via Meta-Learned Subgoals. NeurIPS Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
2021	J	`jnl`	Discovery of Options via Meta-Learned Subgoals. CoRR Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
2021	A*	`conf`	Emphatic Algorithms for Deep Reinforcement Learning. ICML Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
2021	J	`jnl`	Emphatic Algorithms for Deep Reinforcement Learning. CoRR Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
2021	A*	`conf`	Expected Eligibility Traces. AAAI Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa
2021	A*	`conf`	Muesli: Combining Improvements in Policy Optimization. ICML Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
2021	J	`jnl`	Muesli: Combining Improvements in Policy Optimization. CoRR Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
2021	J	`jnl`	Podracer architectures for scalable Reinforcement Learning. CoRR Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt
2021	A*	`conf`	Self-Consistent Models and Values. NeurIPS Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado Philip van Hasselt, David Silver
2021	J	`jnl`	Self-Consistent Models and Values. CoRR Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver
2020	A*	`conf`	A Self-Tuning Actor-Critic Algorithm. NeurIPS Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2020	A*	`conf`	Behaviour Suite for Reinforcement Learning. ICLR 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
2020	A*	`conf`	Discovering Reinforcement Learning Algorithms. NeurIPS Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
2020	J	`jnl`	Discovering Reinforcement Learning Algorithms. CoRR Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
2020	J	`jnl`	Expected Eligibility Traces. CoRR Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa
2020	A*	`conf`	Meta-Gradient Reinforcement Learning with an Objective Discovered Online. NeurIPS Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
2020	J	`jnl`	Meta-Gradient Reinforcement Learning with an Objective Discovered Online. CoRR Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
2020	A*	`conf`	Off-Policy Actor-Critic with Shared Experience Replay. ICML Simon Schmitt, Matteo Hessel, Karen Simonyan
2020	J	`jnl`	Self-Tuning Deep Reinforcement Learning. CoRR Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2020	A*	`conf`	What Can Learned Intrinsic Rewards Capture? ICML Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	Behaviour Suite for Reinforcement Learning. CoRR 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
2019	A*	`conf`	Discovery of Useful Questions as Auxiliary Tasks. NeurIPS Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	Discovery of Useful Questions as Auxiliary Tasks. CoRR Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard L. Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	General non-linear Bellman equations. CoRR Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto
2019	A*	`conf`	Multi-Task Deep Reinforcement Learning with PopArt. AAAI Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt
2019	J	`jnl`	Off-Policy Actor-Critic with Shared Experience Replay. CoRR Simon Schmitt, Matteo Hessel, Karen Simonyan
2019	J	`jnl`	On Inductive Biases in Deep Reinforcement Learning. CoRR Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver
2019	J	`jnl`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2019	J	`jnl`	What Can Learned Intrinsic Rewards Capture? CoRR Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
2019	A*	`conf`	When to use parametric models in reinforcement learning? NeurIPS Hado van Hasselt, Matteo Hessel, John Aslanides
2019	J	`jnl`	When to use parametric models in reinforcement learning? CoRR Hado van Hasselt, Matteo Hessel, John Aslanides
2018	J	`jnl`	Deep Reinforcement Learning and the Deadly Triad. CoRR Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, Joseph Modayil
2018	—	`conf`	Distributed Prioritized Experience Replay. ICLR (Poster) Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver
2018	J	`jnl`	Distributed Prioritized Experience Replay. CoRR Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver
2018	J	`jnl`	Multi-task Deep Reinforcement Learning with PopArt. CoRR Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt
2018	—	`conf`	Noisy Networks For Exploration. ICLR (Poster) 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
2018	J	`jnl`	Observe and Look Further: Achieving Consistent Performance on Atari. CoRR 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
2018	A*	`conf`	Rainbow: Combining Improvements in Deep Reinforcement Learning. AAAI Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
2018	J	`jnl`	Scaling shared model governance via model splitting. CoRR Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli
2018	A*	`conf`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2018	J	`jnl`	Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul
2017	J	`jnl`	Rainbow: Combining Improvements in Deep Reinforcement Learning. CoRR Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Daniel Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
2017	A*	`conf`	The Predictron: End-To-End Learning and Planning. ICML 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
2016	A*	`conf`	Dueling Network Architectures for Deep Reinforcement Learning. ICML Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
2016	J	`jnl`	Learning functions across many orders of magnitudes. CoRR Hado van Hasselt, Arthur Guez, Matteo Hessel, David Silver
2016	—	`conf`	Learning values across many orders of magnitude. NIPS Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
2016	J	`jnl`	The Predictron: End-To-End Learning and Planning. CoRR 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
2014	C	`conf`	A novel approach to model design and tuning through automatic parameter screening and optimization theory and application to a helicopter flight simulator case-study. SIMULTECH Matteo Hessel, Francesco Borgatelli, Fabio Ortalli
2014	—	`conf`	Automatic Tuning of Computational Models. SIMULTECH (Selected Papers) Matteo Hessel, Fabio Ortalli, Francesco Borgatelli, Pier Luca Lanzi
2014	—	`conf`	Machine Learning for Parameter Screening in Computer Simulations. MESAS Matteo Hessel, Fabio Ortalli, Francesco Borgatelli