Hado van Hasselt

107 papers A* 30A 3B 1Journal 58Unranked 14

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
2025	A*	`conf`	General Uncertainty Estimation with Delta Variances. AAAI Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2025	J	`jnl`	General Uncertainty Estimation with Delta Variances. CoRR Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2025	A*	`conf`	Scalable Meta-Learning via Mixed-Mode Differentiation. ICML Iurii Kemaev, Dan A. Calian, Luisa M. Zintgraf, Gregory Farquhar, Hado van Hasselt
2025	J	`jnl`	Scalable Meta-Learning via Mixed-Mode Differentiation. CoRR Iurii Kemaev, Dan A. Calian, Luisa M. Zintgraf, Gregory Farquhar, Hado van Hasselt
2025	A*	`conf`	Wasserstein Policy Optimization. ICML David Pfau, Ian Davies, Diana L. Borsa, João Guilherme Madeira Araújo, Brendan D. Tracey, Hado van Hasselt
2025	J	`jnl`	Wasserstein Policy Optimization. CoRR David Pfau, Ian Davies, Diana Borsa, João G. M. Araújo, Brendan D. Tracey, Hado van Hasselt
2024	J	`jnl`	A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. Trans. Mach. Learn. Res. Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni
2024	—	`conf`	Disentangling the Causes of Plasticity Loss in Neural Networks. CoLLAs Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
2024	J	`jnl`	Disentangling the Causes of Plasticity Loss in Neural Networks. CoRR Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
2024	J	`jnl`	Normalization and effective learning rates in reinforcement learning. CoRR Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
2023	J	`jnl`	A Definition of Continual Reinforcement Learning. CoRR David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh
2023	J	`jnl`	A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. CoRR Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni
2023	A*	`conf`	Exploration via Epistemic Value Estimation. AAAI Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2023	J	`jnl`	Exploration via Epistemic Value Estimation. CoRR Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2023	A*	`conf`	Human-level Atari 200x faster. ICLR Steven Kapturowski, Victor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia
2023	J	`jnl`	Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration. CoRR Chentian Jiang, Nan Rosemary Ke, Hado van Hasselt
2023	J	`jnl`	On the Convergence of Bounded Agents. CoRR David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh
2023	J	`jnl`	Optimistic Meta-Gradients. CoRR Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh
2022	A*	`conf`	Bootstrapped Meta-Learning. ICLR Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
2022	A*	`conf`	Chaining Value Functions for Off-Policy Learning. AAAI Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2022	J	`jnl`	Chaining Value Functions for Off-Policy Learning. CoRR Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2022	J	`jnl`	Human-level Atari 200x faster. CoRR Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia
2022	A*	`conf`	Introducing Symmetries to Black Box Meta Reinforcement Learning. AAAI Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
2022	A*	`conf`	Learning Expected Emphatic Traces for Deep RL. AAAI Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt
2022	A*	`conf`	Learning by Directional Gradient Descent. ICLR David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt
2022	J	`jnl`	Selective Credit Assignment. CoRR Veronica Chelu, Diana Borsa, Doina Precup, Hado van Hasselt
2021	J	`jnl`	Bootstrapped Meta-Learning. CoRR Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
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	J	`jnl`	Introducing Symmetries to Black Box Meta Reinforcement Learning. CoRR Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen
2021	J	`jnl`	Learning Expected Emphatic Traces for Deep RL. CoRR Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt
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	A	`conf`	Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity. AAMAS Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi
2021	J	`jnl`	Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity. CoRR Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi
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	J	`jnl`	Self-Consistent Models and Values. CoRR Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver
2021	J	`jnl`	Synthetic Returns for Long-Term Credit Assignment. CoRR David Raposo, Samuel Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt M. Botvinick, Hado van Hasselt, H. Francis Song
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`	Conditional Importance Sampling for Off-Policy Learning. AISTATS Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
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`	Forethought and Hindsight in Credit Assignment. NeurIPS Veronica Chelu, Doina Precup, Hado van Hasselt
2020	J	`jnl`	Forethought and Hindsight in Credit Assignment. CoRR Veronica Chelu, Doina Precup, Hado van Hasselt
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	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	J	`jnl`	Conditional Importance Sampling for Off-Policy Learning. CoRR Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
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`	Hindsight Credit Assignment. NeurIPS Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos
2019	J	`jnl`	Hindsight Credit Assignment. CoRR Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Rémi Munos
2019	J	`jnl`	Meta-learning of Sequential Strategies. CoRR 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 M. Botvinick, Shane Legg
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`	On Inductive Biases in Deep Reinforcement Learning. CoRR Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver
2019	—	`conf`	Universal Successor Features Approximators. ICLR (Poster) Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul
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	A*	`conf`	Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems. ICML Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé, Hado van Hasselt
2018	A*	`conf`	Meta-Gradient Reinforcement Learning. NeurIPS Zhongwen Xu, Hado van Hasselt, David Silver
2018	J	`jnl`	Meta-Gradient Reinforcement Learning. CoRR Zhongwen Xu, 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	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`	The Barbados 2018 List of Open Issues in Continual Learning. CoRR Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup
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
2018	J	`jnl`	Universal Successor Features Approximators. CoRR Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
2017	—	`conf`	Natural Value Approximators: Learning when to Trust Past Estimates. NIPS Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, 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	J	`jnl`	StarCraft II: A New Challenge for Reinforcement Learning. CoRR 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
2017	—	`conf`	Successor Features for Transfer in Reinforcement Learning. NIPS André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt
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`	Deep Reinforcement Learning with Double Q-Learning. AAAI Hado van Hasselt, Arthur Guez, David Silver
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
2015	J	`jnl`	Deep Reinforcement Learning with Double Q-learning. CoRR Hado van Hasselt, Arthur Guez, David Silver
2015	J	`jnl`	Learning to Predict Independent of Span. CoRR Hado van Hasselt, Richard S. Sutton
2014	A*	`conf`	A new Q(lambda) with interim forward view and Monte Carlo equivalence. ICML Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt
2014	A	`conf`	Off-policy TD( l) with a true online equivalence. UAI Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton
2014	—	`conf`	Weighted importance sampling for off-policy learning with linear function approximation. NIPS Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton
2013	J	`jnl`	Estimating the Maximum Expected Value: An Analysis of (Nested) Cross Validation and the Maximum Sample Average CoRR Hado van Hasselt
2013	—	`conf`	Stacking under uncertainty: We know how to predict, but how should we act? CIPLS Hado van Hasselt, Han La Poutré
2012	—	`ch.`	Reinforcement Learning in Continuous State and Action Spaces. Reinforcement Learning Hado van Hasselt
2011	J	`jnl`	Exploiting Best-Match Equations for Efficient Reinforcement Learning. J. Mach. Learn. Res. Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco A. Wiering
2011	—	`conf`	Reinforcement learning algorithms for solving classification problems. ADPRL Marco A. Wiering, Hado van Hasselt, Auke-Dirk Pietersma, Lambert Schomaker
2010	—	`conf`	Double Q-learning. NIPS Hado van Hasselt
2009	—	`conf`	A theoretical and empirical analysis of Expected Sarsa. ADPRL Harm van Seijen, Hado van Hasselt, Shimon Whiteson, Marco A. Wiering
2009	—	`conf`	Adaptive Serious Games Using Agent Organizations. AGS Joost Westra, Hado van Hasselt, Frank Dignum, Virginia Dignum
2009	—	`conf`	The QV family compared to other reinforcement learning algorithms. ADPRL Marco A. Wiering, Hado van Hasselt
2009	B	`conf`	Using continuous action spaces to solve discrete problems. IJCNN Hado van Hasselt, Marco A. Wiering
2008	J	`jnl`	Ensemble Algorithms in Reinforcement Learning. IEEE Trans. Syst. Man Cybern. Part B Marco A. Wiering, Hado van Hasselt
2008	—	`conf`	On-line adapting games using agent organizations. CIG Joost Westra, Hado van Hasselt, Virginia Dignum, Frank Dignum