Hado van Hasselt

According to our database, Hado van Hasselt authored at least 72 papers between 2008 and 2024.

Collaborative distances:

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

2024

A Survey of Temporal Credit Assignment in Deep Reinforcement Learning.

Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni

Trans. Mach. Learn. Res., 2024

Normalization and effective learning rates in reinforcement learning.

Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney

CoRR, 2024

Disentangling the Causes of Plasticity Loss in Neural Networks.

Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney

CoRR, 2024

2023

On the Convergence of Bounded Agents.

David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh

CoRR, 2023

Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration.

Chentian Jiang, Nan Rosemary Ke, Hado van Hasselt

CoRR, 2023

Optimistic Meta-Gradients.

Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Definition of Continual Reinforcement Learning.

David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Human-level Atari 200x faster.

Steven Kapturowski, Victor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Exploration via Epistemic Value Estimation.

Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Selective Credit Assignment.

Veronica Chelu, Diana Borsa, Doina Precup, Hado van Hasselt

CoRR, 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

Bootstrapped Meta-Learning.

Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh

Proceedings of the Tenth International Conference on Learning Representations, 2022

Chaining Value Functions for Off-Policy Learning.

Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Introducing Symmetries to Black Box Meta Reinforcement Learning.

Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Learning Expected Emphatic Traces for Deep RL.

Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 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

Synthetic Returns for Long-Term Credit Assignment.

David Raposo, Samuel Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt M. Botvinick, Hado van Hasselt, H. Francis Song

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

Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity.

Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hasselt, David Balduzzi

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 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

Forethought and Hindsight in Credit Assignment.

Veronica Chelu, Doina Precup, Hado van Hasselt

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

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

Conditional Importance Sampling for Off-Policy Learning.

Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 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

Meta-learning of Sequential Strategies.

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

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

Hindsight Credit Assignment.

Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos

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

Universal Successor Features Approximators.

Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul

Proceedings of the 7th International Conference on Learning Representations, 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

Deep Reinforcement Learning and the Deadly Triad.

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

CoRR, 2018

The Barbados 2018 List of Open Issues in Continual Learning.

Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup

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

Meta-Gradient Reinforcement Learning.

Zhongwen Xu, Hado van Hasselt, David Silver

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems.

Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé, Hado van Hasselt

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

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

StarCraft II: A New Challenge for Reinforcement Learning.

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

CoRR, 2017

Natural Value Approximators: Learning when to Trust Past Estimates.

Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Successor Features for Transfer in Reinforcement Learning.

André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 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

Deep Reinforcement Learning with Double Q-Learning.

Hado van Hasselt, Arthur Guez, David Silver

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Learning to Predict Independent of Span.

Hado van Hasselt, Richard S. Sutton

CoRR, 2015

2014

Off-policy TD( l) with a true online equivalence.

Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Weighted importance sampling for off-policy learning with linear function approximation.

Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

A new Q(lambda) with interim forward view and Monte Carlo equivalence.

Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Estimating the Maximum Expected Value: An Analysis of (Nested) Cross Validation and the Maximum Sample Average

Hado van Hasselt

CoRR, 2013

Stacking under uncertainty: We know how to predict, but how should we act?

Hado van Hasselt, Han La Poutré

Proceedings of the IEEE Symposium on Computational Intelligence In Production And Logistics Systems, 2013

2012

Reinforcement Learning in Continuous State and Action Spaces.

Hado van Hasselt

Proceedings of the Reinforcement Learning, 2012

2011

Insights in reinforcement rearning : formal analysis and empirical evaluation of temporal-difference learning algorithms.

Hado Philip van Hasselt

PhD thesis, 2011

Exploiting Best-Match Equations for Efficient Reinforcement Learning.

Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco A. Wiering

J. Mach. Learn. Res., 2011

Reinforcement learning algorithms for solving classification problems.

Marco A. Wiering, Hado van Hasselt, Auke-Dirk Pietersma, Lambert Schomaker

Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning, 2011

2010

Double Q-learning.

Hado van Hasselt

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Using continuous action spaces to solve discrete problems.

Hado van Hasselt, Marco A. Wiering

Proceedings of the International Joint Conference on Neural Networks, 2009

Adaptive Serious Games Using Agent Organizations.

Joost Westra, Hado van Hasselt, Frank Dignum, Virginia Dignum

Proceedings of the Agents for Games and Simulations, 2009

The QV family compared to other reinforcement learning algorithms.

Marco A. Wiering, Hado van Hasselt

Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 2009

A theoretical and empirical analysis of Expected Sarsa.

Harm van Seijen, Hado van Hasselt, Shimon Whiteson, Marco A. Wiering

Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 2009

2008

Ensemble Algorithms in Reinforcement Learning.

Marco A. Wiering, Hado van Hasselt

IEEE Trans. Syst. Man Cybern. Part B, 2008

On-line adapting games using agent organizations.

Joost Westra, Hado van Hasselt, Virginia Dignum, Frank Dignum

Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games, 2008