Hado van Hasselt

Publication Activity (10 Years)

Years Active: 2008-2023
Publications (10 Years): 80

Top Topics

Reinforcement Learning

Eligibility Traces

Top Venues

Publications

Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
Exploration via Epistemic Value Estimation. CoRR (2023)
Steven Kapturowski, Victor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia
Human-level Atari 200x faster. ICLR (2023)
Chentian Jiang, Nan Rosemary Ke, Hado van Hasselt
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration. CoRR (2023)
David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh
On the Convergence of Bounded Agents. CoRR (2023)
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh
Optimistic Meta-Gradients. CoRR (2023)
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
Exploration via Epistemic Value Estimation. AAAI (2023)
Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. CoRR (2023)
David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh
A Definition of Continual Reinforcement Learning. CoRR (2023)
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
Chaining Value Functions for Off-Policy Learning. AAAI (2022)
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
Introducing Symmetries to Black Box Meta Reinforcement Learning. AAAI (2022)
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
Chaining Value Functions for Off-Policy Learning. CoRR (2022)
Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia
Human-level Atari 200x faster. CoRR (2022)
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
Bootstrapped Meta-Learning. ICLR (2022)
Veronica Chelu, Diana Borsa, Doina Precup, Hado van Hasselt
Selective Credit Assignment. CoRR (2022)
David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt
Learning by Directional Gradient Descent. ICLR (2022)
Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt
Learning Expected Emphatic Traces for Deep RL. AAAI (2022)
David Raposo, Samuel Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt M. Botvinick, Hado van Hasselt, H. Francis Song
Synthetic Returns for Long-Term Credit Assignment. CoRR (2021)
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
Emphatic Algorithms for Deep Reinforcement Learning. ICML (2021)
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa
Expected Eligibility Traces. AAAI (2021)
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity. CoRR (2021)
Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt
Podracer architectures for scalable Reinforcement Learning. CoRR (2021)
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
Muesli: Combining Improvements in Policy Optimization. ICML (2021)
Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver
Self-Consistent Models and Values. CoRR (2021)
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
Emphatic Algorithms for Deep Reinforcement Learning. CoRR (2021)
Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt
Learning Expected Emphatic Traces for Deep RL. CoRR (2021)
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt
Muesli: Combining Improvements in Policy Optimization. CoRR (2021)
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
Introducing Symmetries to Black Box Meta Reinforcement Learning. CoRR (2021)
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Options via Meta-Learned Subgoals. CoRR (2021)
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity. AAMAS (2021)
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Options via Meta-Learned Subgoals. NeurIPS (2021)
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
Bootstrapped Meta-Learning. CoRR (2021)
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
Discovering Reinforcement Learning Algorithms. CoRR (2020)
Veronica Chelu, Doina Precup, Hado van Hasselt
Forethought and Hindsight in Credit Assignment. CoRR (2020)
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
A Self-Tuning Actor-Critic Algorithm. NeurIPS (2020)
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
Behaviour Suite for Reinforcement Learning. ICLR (2020)
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
What Can Learned Intrinsic Rewards Capture? ICML (2020)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. AISTATS (2020)
Veronica Chelu, Doina Precup, Hado van Hasselt
Forethought and Hindsight in Credit Assignment. NeurIPS (2020)
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
Discovering Reinforcement Learning Algorithms. NeurIPS (2020)
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa
Expected Eligibility Traces. CoRR (2020)
Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
Meta-Gradient Reinforcement Learning with an Objective Discovered Online. CoRR (2020)
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Self-Tuning Deep Reinforcement Learning. CoRR (2020)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. CoRR (2019)
Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto
General non-linear Bellman equations. CoRR (2019)
Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver
On Inductive Biases in Deep Reinforcement Learning. CoRR (2019)
Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt
Multi-Task Deep Reinforcement Learning with PopArt. AAAI (2019)
Hado van Hasselt, Matteo Hessel, John Aslanides
When to use parametric models in reinforcement learning? CoRR (2019)
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Useful Questions as Auxiliary Tasks. NeurIPS (2019)
Hado van Hasselt, Matteo Hessel, John Aslanides
When to use parametric models in reinforcement learning? NeurIPS (2019)
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
Behaviour Suite for Reinforcement Learning. CoRR (2019)
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos
Hindsight Credit Assignment. NeurIPS (2019)
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard L. Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Useful Questions as Auxiliary Tasks. CoRR (2019)
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
What Can Learned Intrinsic Rewards Capture? CoRR (2019)
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg
Meta-learning of Sequential Strategies. CoRR (2019)
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Rémi Munos
Hindsight Credit Assignment. CoRR (2019)
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul
Universal Successor Features Approximators. ICLR (Poster) (2019)
Zhongwen Xu, Hado van Hasselt, David Silver
Meta-Gradient Reinforcement Learning. CoRR (2018)
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
Universal Successor Features Approximators. CoRR (2018)
Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt
Multi-task Deep Reinforcement Learning with PopArt. CoRR (2018)
Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul
Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR (2018)
Zhongwen Xu, Hado van Hasselt, David Silver
Meta-Gradient Reinforcement Learning. NeurIPS (2018)
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
Observe and Look Further: Achieving Consistent Performance on Atari. CoRR (2018)
Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, Joseph Modayil
Deep Reinforcement Learning and the Deadly Triad. CoRR (2018)
Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup
The Barbados 2018 List of Open Issues in Continual Learning. CoRR (2018)
Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé, Hado van Hasselt
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems. ICML (2018)
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver
Distributed Prioritized Experience Replay. ICLR (Poster) (2018)
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
Rainbow: Combining Improvements in Deep Reinforcement Learning. AAAI (2018)
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver
Distributed Prioritized Experience Replay. CoRR (2018)
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing
StarCraft II: A New Challenge for Reinforcement Learning. CoRR (2017)
André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt
Successor Features for Transfer in Reinforcement Learning. NIPS (2017)
Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul
Natural Value Approximators: Learning when to Trust Past Estimates. NIPS (2017)
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
The Predictron: End-To-End Learning and Planning. ICML (2017)
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Daniel Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
Rainbow: Combining Improvements in Deep Reinforcement Learning. CoRR (2017)
Hado van Hasselt, Arthur Guez, Matteo Hessel, David Silver
Learning functions across many orders of magnitudes. CoRR (2016)
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
Dueling Network Architectures for Deep Reinforcement Learning. ICML (2016)
Hado van Hasselt, Arthur Guez, David Silver
Deep Reinforcement Learning with Double Q-Learning. AAAI (2016)
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
The Predictron: End-To-End Learning and Planning. CoRR (2016)
Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
Learning values across many orders of magnitude. NIPS (2016)
Hado van Hasselt, Arthur Guez, David Silver
Deep Reinforcement Learning with Double Q-learning. CoRR (2015)
Hado van Hasselt, Richard S. Sutton
Learning to Predict Independent of Span. CoRR (2015)
Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton
Off-policy TD( l) with a true online equivalence. UAI (2014)
Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton
Weighted importance sampling for off-policy learning with linear function approximation. NIPS (2014)
Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt
A new Q(lambda) with interim forward view and Monte Carlo equivalence. ICML (2014)
Hado van Hasselt
Estimating the Maximum Expected Value: An Analysis of (Nested) Cross Validation and the Maximum Sample Average CoRR (2013)
Hado van Hasselt, Han La Poutré
Stacking under uncertainty: We know how to predict, but how should we act? CIPLS (2013)
Marco A. Wiering, Hado van Hasselt, Auke-Dirk Pietersma, Lambert Schomaker
Reinforcement learning algorithms for solving classification problems. ADPRL (2011)
Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco A. Wiering
Exploiting Best-Match Equations for Efficient Reinforcement Learning. J. Mach. Learn. Res. 12 (2011)
Hado van Hasselt
Double Q-learning. NIPS (2010)
Joost Westra, Hado van Hasselt, Frank Dignum, Virginia Dignum
Adaptive Serious Games Using Agent Organizations. AGS (2009)
Harm van Seijen, Hado van Hasselt, Shimon Whiteson, Marco A. Wiering
A theoretical and empirical analysis of Expected Sarsa. ADPRL (2009)
Hado van Hasselt, Marco A. Wiering
Using continuous action spaces to solve discrete problems. IJCNN (2009)
Marco A. Wiering, Hado van Hasselt
The QV family compared to other reinforcement learning algorithms. ADPRL (2009)
Joost Westra, Hado van Hasselt, Virginia Dignum, Frank Dignum
On-line adapting games using agent organizations. CIG (2008)
Marco A. Wiering, Hado van Hasselt
Ensemble Algorithms in Reinforcement Learning. IEEE Trans. Syst. Man Cybern. Part B 38 (4) (2008)