Will Dabney

According to our database, Will Dabney authored at least 50 papers between 2017 and 2024.

Collaborative distances:

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

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

A Distributional Analogue to the Successor Representation.

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

CoRR, 2024

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model.

Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney

CoRR, 2024

Off-policy Distributional Q(λ): Distributional RL without Importance Sampling.

Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney

CoRR, 2024

2023

An Analysis of Quantile Temporal-Difference Learning.

Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney

CoRR, 2023

Deep Reinforcement Learning with Plasticity Injection.

Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto

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

Understanding Self-Predictive Learning for Reinforcement Learning.

Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

Proceedings of the International Conference on Machine Learning, 2023

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation.

Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

Quantile Credit Assignment.

Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos

Proceedings of the International Conference on Machine Learning, 2023

Understanding Plasticity in Neural Networks.

Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

Bootstrapped Representations in Reinforcement Learning.

Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition.

Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa

Proceedings of the International Conference on Machine Learning, 2023

Settling the Reward Hypothesis.

Michael Bowling, John D. Martin, David Abel, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

2022

Learning Dynamics and Generalization in Reinforcement Learning.

Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

CoRR, 2022

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning.

Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Expressivity of Markov Reward (Extended Abstract).

David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Generalised Policy Improvement with Geometric Policy Composition.

Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto

Proceedings of the International Conference on Machine Learning, 2022

Learning Dynamics and Generalization in Deep Reinforcement Learning.

Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

Proceedings of the International Conference on Machine Learning, 2022

Understanding and Preventing Capacity Loss in Reinforcement Learning.

Clare Lyle, Mark Rowland, Will Dabney

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

The Difficulty of Passive Learning in Deep Reinforcement Learning.

Georg Ostrovski, Pablo Samuel Castro, Will Dabney

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

On the Expressivity of Markov Reward.

David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh

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

Counterfactual Credit Assignment in Model-Free Reinforcement Learning.

Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Rémi Munos

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

Revisiting Peng's Q(λ) for Modern Reinforcement Learning.

Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

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

Temporally-Extended ε-Greedy Exploration.

Will Dabney, Georg Ostrovski, André Barreto

Proceedings of the 9th International Conference on Learning Representations, 2021

On the Effect of Auxiliary Tasks on Representation Dynamics.

Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning.

Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver

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

2020

A distributional code for value in dopamine-based reinforcement learning.

Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew M. Botvinick

Nat., 2020

Counterfactual Credit Assignment in Model-Free Reinforcement Learning.

Thomas Mesnard, Théophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Tom Stepleton, Nicolas Heess, Arthur Guez, Marcus Hutter, Lars Buesing, Rémi Munos

CoRR, 2020

Deep Reinforcement Learning and its Neuroscientific Implications.

Matthew M. Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson

CoRR, 2020

Revisiting Fundamentals of Experience Replay.

William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

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

Fast Task Inference with Variational Intrinsic Successor Features.

Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih

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

Adaptive Trade-Offs in Off-Policy Learning.

Mark Rowland, Will Dabney, Rémi Munos

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Adapting Behaviour for Learning Progress.

Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero

CoRR, 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

A Geometric Perspective on Optimal Representations for Reinforcement Learning.

Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

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

Statistics and Samples in Distributional Reinforcement Learning.

Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney

Proceedings of the 36th International Conference on Machine Learning, 2019

Recurrent Experience Replay in Distributed Reinforcement Learning.

Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney

Proceedings of the 7th International Conference on Learning Representations, 2019

The Termination Critic.

Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery.

Thomas S. Stepleton, Razvan Pascanu, Will Dabney, Siddhant M. Jayakumar, Hubert Soyer, Rémi Munos

CoRR, 2018

Autoregressive Quantile Networks for Generative Modeling.

Georg Ostrovski, Will Dabney, Rémi Munos

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

Implicit Quantile Networks for Distributional Reinforcement Learning.

Will Dabney, Georg Ostrovski, David Silver, Rémi Munos

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

The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning.

Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos

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

Distributed Distributional Deterministic Policy Gradients.

Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap

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

An Analysis of Categorical Distributional Reinforcement Learning.

Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 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

Distributional Reinforcement Learning With Quantile Regression.

Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos

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

2017

The Cramer Distance as a Solution to Biased Wasserstein Gradients.

Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos

CoRR, 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

A Distributional Perspective on Reinforcement Learning.

Marc G. Bellemare, Will Dabney, Rémi Munos

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