Dabney, Will

53 publications

AISTATS 2025 A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney

ICML 2025 Discovering Symbolic Cognitive Models from Human and Animal Behavior Pablo Samuel Castro, Nenad Tomasev, Ankit Anand, Navodita Sharma, Rishika Mohanta, Aparna Dev, Kuba Perlin, Siddhant Jain, Kyle Levin, Noemi Elteto, Will Dabney, Alexander Novikov, Glenn C Turner, Maria K Eckstein, Nathaniel D. Daw, Kevin J Miller, Kim Stachenfeld

JMLR 2025 Optimizing Return Distributions with Distributional Dynamic Programming Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney

NeurIPS 2025 Plasticity as the Mirror of Empowerment David Abel, Michael Bowling, Andre Barreto, Will Dabney, Shi Dong, Steven Stenberg Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh

NeurIPS 2025 Predictive Coding Enhances Meta-RL to Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability Po-Chen Kuo, Han Hou, Will Dabney, Edgar Y. Walker

ICML 2024 A Distributional Analogue to the Successor Representation Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, Andre Barreto, Will Dabney, Marc G Bellemare, Mark Rowland

NeurIPSW 2024 A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L Borsa, Arthur Guez, Will Dabney

JMLR 2024 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

CoLLAs 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

NeurIPSW 2024 Learning Bayes-Optimal Representation in Partially Observable Environments via Meta-Reinforcement Learning with Predictive Coding Po-Chen Kuo, Han Hou, Will Dabney, Edgar Y. Walker

NeurIPS 2024 Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney

NeurIPS 2024 Normalization and Effective Learning Rates in Reinforcement Learning Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney

ICML 2023 Bootstrapped Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney

ICLRW 2023 Bootstrapped Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney

NeurIPS 2023 Deep Reinforcement Learning with Plasticity Injection Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, Andre Barreto

ICLRW 2023 Deep Reinforcement Learning with Plasticity Injection Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, Andre Barreto

ICML 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, Remi Munos

ICML 2023 Representations and Exploration for Deep Reinforcement Learning Using Singular Value Decomposition Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Remi Munos, Will Dabney, Diana L Borsa

ICML 2023 Settling the Reward Hypothesis Michael Bowling, John D Martin, David Abel, Will Dabney

ICML 2023 The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation Mark Rowland, Yunhao Tang, Clare Lyle, Remi Munos, Marc G Bellemare, Will Dabney

ICML 2023 Understanding Plasticity in Neural Networks Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu, Will Dabney

ICML 2023 Understanding Self-Predictive Learning for Reinforcement Learning Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Avila Pires, Yash Chandak, Remi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

ICML 2022 Generalised Policy Improvement with Geometric Policy Composition Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Remi Munos, Andre Barreto

ICML 2022 Learning Dynamics and Generalization in Deep Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

IJCAI 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

NeurIPS 2022 The Nature of Temporal Difference Errors in Multi-Step Distributional Reinforcement Learning Yunhao Tang, Remi Munos, Mark Rowland, Bernardo Avila Pires, Will Dabney, Marc Bellemare

ICLR 2022 Understanding and Preventing Capacity Loss in Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney

AISTATS 2021 On the Effect of Auxiliary Tasks on Representation Dynamics Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

ICML 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, Remi Munos

NeurIPS 2021 On the Expressivity of Markov Reward David Abel, Will Dabney, Anna Harutyunyan, Mark K Ho, Michael L. Littman, Doina Precup, Satinder P. Singh

ICML 2021 Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning Tadashi Kozuno, Yunhao Tang, Mark Rowland, Remi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

ICLR 2021 Temporally-Extended Ε-Greedy Exploration Will Dabney, Georg Ostrovski, Andre Barreto

NeurIPS 2021 The Difficulty of Passive Learning in Deep Reinforcement Learning Georg Ostrovski, Pablo Samuel Castro, Will Dabney

AAAI 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

NeurIPSW 2021 Understanding and Preventing Capacity Loss in Reinforcement Learning Clare Lyle, Mark Rowland, Will Dabney

AISTATS 2020 Adaptive Trade-Offs in Off-Policy Learning Mark Rowland, Will Dabney, Remi Munos

AISTATS 2020 Conditional Importance Sampling for Off-Policy Learning Mark Rowland, Anna Harutyunyan, Hado Hasselt, Diana Borsa, Tom Schaul, Remi Munos, Will Dabney

ICLR 2020 Fast Task Inference with Variational Intrinsic Successor Features Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih

ICML 2020 Revisiting Fundamentals of Experience Replay William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

NeurIPS 2019 A Geometric Perspective on Optimal Representations for Reinforcement Learning Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

NeurIPS 2019 Hindsight Credit Assignment Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Remi Munos

ICLR 2019 Recurrent Experience Replay in Distributed Reinforcement Learning Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, Will Dabney

ICML 2019 Statistics and Samples in Distributional Reinforcement Learning Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney

AISTATS 2019 The Termination Critic Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup

AISTATS 2018 An Analysis of Categorical Distributional Reinforcement Learning Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

ICML 2018 Autoregressive Quantile Networks for Generative Modeling Georg Ostrovski, Will Dabney, Remi Munos

ICLR 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 Lillicrap

AAAI 2018 Distributional Reinforcement Learning with Quantile Regression Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos

ICML 2018 Implicit Quantile Networks for Distributional Reinforcement Learning Will Dabney, Georg Ostrovski, David Silver, Remi Munos

AAAI 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

ICLR 2018 The Reactor: A Fast and Sample-Efficient Actor-Critic Agent for Reinforcement Learning Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc Bellemare, Remi Munos

ICML 2017 A Distributional Perspective on Reinforcement Learning Marc G. Bellemare, Will Dabney, Rémi Munos

NeurIPS 2017 Successor Features for Transfer in Reinforcement Learning Andre Barreto, Will Dabney, Remi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, David Silver