Will Dabney

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 83

Top Topics

Temporal Difference

Quantile Regression

Reinforcement Learning

Top Venues

Publications

Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR (2024)
Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
A Distributional Analogue to the Successor Representation. CoRR (2024)
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
Understanding the performance gap between online and offline alignment algorithms. CoRR (2024)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR (2024)
Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR (2024)
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
Normalization and effective learning rates in reinforcement learning. CoRR (2024)
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
Disentangling the Causes of Plasticity Loss in Neural Networks. CoRR (2024)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR (2023)
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
Understanding plasticity in neural networks. CoRR (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. CoRR (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. CoRR (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. ICML (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML (2023)
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
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML (2023)
Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
An Analysis of Quantile Temporal-Difference Learning. CoRR (2023)
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
Quantile Credit Assignment. ICML (2023)
Michael Bowling, John D. Martin, David Abel, Will Dabney
Settling the Reward Hypothesis. ICML (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. NeurIPS (2023)
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
Understanding Plasticity in Neural Networks. ICML (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS (2022)
Clare Lyle, Mark Rowland, Will Dabney
Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR (2022)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward (Extended Abstract). IJCAI (2022)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. ICML (2022)
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
Generalised Policy Improvement with Geometric Policy Composition. CoRR (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Reinforcement Learning. CoRR (2022)
Michael Bowling, John D. Martin, David Abel, Will Dabney
Settling the Reward Hypothesis. CoRR (2022)
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
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML (2022)
Clare Lyle, Mark Rowland, Will Dabney
Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR (2022)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR (2022)
Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR (2021)
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI (2021)
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
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML (2021)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. CoRR (2021)
Georg Ostrovski, Pablo Samuel Castro, Will Dabney
The Difficulty of Passive Learning in Deep Reinforcement Learning. NeurIPS (2021)
Will Dabney, Georg Ostrovski, André Barreto
Temporally-Extended ε-Greedy Exploration. ICLR (2021)
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML (2021)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. NeurIPS (2021)
Georg Ostrovski, Pablo Samuel Castro, Will Dabney
The Difficulty of Passive Learning in Deep Reinforcement Learning. CoRR (2021)
Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR (2021)
Matthew Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson
Deep Reinforcement Learning and its Neuroscientific Implications. CoRR (2020)
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
Revisiting Fundamentals of Experience Replay. ICML (2020)
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR (2020)
Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick
A distributional code for value in dopamine-based reinforcement learning. Nat. 577 (7792) (2020)
Will Dabney, Georg Ostrovski, André Barreto
Temporally-Extended ε-Greedy Exploration. CoRR (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)
Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih
Fast Task Inference with Variational Intrinsic Successor Features. ICLR (2020)
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
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. CoRR (2020)
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
Revisiting Fundamentals of Experience Replay. CoRR (2020)
Mark Rowland, Will Dabney, Rémi Munos
Adaptive Trade-Offs in Off-Policy Learning. AISTATS (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)
Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
The Termination Critic. CoRR (2019)
Steven Hansen, Will Dabney, André Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih
Fast Task Inference with Variational Intrinsic Successor Features. CoRR (2019)
Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney
Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR (Poster) (2019)
Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
The Termination Critic. AISTATS (2019)
Mark Rowland, Will Dabney, Rémi Munos
Adaptive Trade-Offs in Off-Policy Learning. CoRR (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. ICML (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS (2019)
Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero
Adapting Behaviour for Learning Progress. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations 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)
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)
Will Dabney, Georg Ostrovski, David Silver, Rémi Munos
Implicit Quantile Networks for Distributional Reinforcement Learning. CoRR (2018)
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap
Distributed Distributional Deterministic Policy Gradients. CoRR (2018)
Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
An Analysis of Categorical Distributional Reinforcement Learning. AISTATS (2018)
Georg Ostrovski, Will Dabney, Rémi Munos
Autoregressive Quantile Networks for Generative Modeling. CoRR (2018)
Thomas S. Stepleton, Razvan Pascanu, Will Dabney, Siddhant M. Jayakumar, Hubert Soyer, Rémi Munos
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery. CoRR (2018)
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap
Distributed Distributional Deterministic Policy Gradients. ICLR (Poster) (2018)
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
Distributional Reinforcement Learning With Quantile Regression. AAAI (2018)
Georg Ostrovski, Will Dabney, Rémi Munos
Autoregressive Quantile Networks for Generative Modeling. ICML (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)
Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. ICLR (Poster) (2018)
Will Dabney, Georg Ostrovski, David Silver, Rémi Munos
Implicit Quantile Networks for Distributional Reinforcement Learning. ICML (2018)
Marc G. Bellemare, Will Dabney, Rémi Munos
A Distributional Perspective on Reinforcement Learning. ICML (2017)
Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos
The Cramer Distance as a Solution to Biased Wasserstein Gradients. CoRR (2017)
Marc G. Bellemare, Will Dabney, Rémi Munos
A Distributional Perspective on 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)
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)
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
Distributional Reinforcement Learning with Quantile Regression. CoRR (2017)