Will Dabney - RankMe

98 papers A* 33A 6Journal 54Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments. CoRR Michael Beukman, Khimya Khetarpal, Zeyu Zheng, Will Dabney, Jakob N. Foerster, Michael Dennis, Clare Lyle
2025	A	`conf`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. AISTATS Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L. Borsa, Arthur Guez, Will Dabney
2025	J	`jnl`	Agency Is Frame-Dependent. CoRR David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	A*	`conf`	Discovering Symbolic Cognitive Models from Human and Animal Behavior. ICML Pablo Samuel Castro, Nenad Tomasev, Ankit Anand, Navodita Sharma, Rishika Mohanta, Aparna Dev, Kuba Perlin, Siddhant Jain, Kyle Levin, Noémi Élteto, Will Dabney, Alexander Novikov, Glenn C. Turner, Maria K. Eckstein, Nathaniel D. Daw, Kevin J. Miller, Kim Stachenfeld
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. CoRR Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Optimizing Return Distributions with Distributional Dynamic Programming. J. Mach. Learn. Res. Bernardo Ávila Pires, Mark Rowland, Diana Borsa, Zhaohan Daniel Guo, Khimya Khetarpal, André Barreto, David Abel, Rémi Munos, Will Dabney
2025	J	`jnl`	Plasticity as the Mirror of Empowerment. CoRR David Abel, Michael Bowling, André Barreto, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	J	`jnl`	Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability. CoRR Po-Chen Kuo, Han Hou, Will Dabney, Edgar Y. Walker
2025	J	`jnl`	Uncertainty Prioritized Experience Replay. CoRR Rodrigo Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney
2024	A*	`conf`	A Distributional Analogue to the Successor Representation. ICML Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	J	`jnl`	A Distributional Analogue to the Successor Representation. CoRR Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	J	`jnl`	A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney
2024	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. J. Mach. Learn. Res. Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
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`	Lifelong Reinforcement Learning via Neuromodulation. CoRR Sebastian Lee, Samuel Liebana Garcia, Claudia Clopath, Will Dabney
2024	A*	`conf`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. NeurIPS Mark Rowland, Kevin Kevin Li, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	J	`jnl`	Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney
2024	A*	`conf`	Normalization and effective learning rates in reinforcement learning. NeurIPS Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado Philip van Hasselt, Razvan Pascanu, 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
2024	J	`jnl`	Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney
2024	J	`jnl`	Understanding the performance gap between online and offline alignment algorithms. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
2023	J	`jnl`	An Analysis of Quantile Temporal-Difference Learning. CoRR Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	Bootstrapped Representations in Reinforcement Learning. ICML Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	Bootstrapped Representations in Reinforcement Learning. CoRR Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	Deep Reinforcement Learning with Plasticity Injection. NeurIPS Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
2023	J	`jnl`	Deep Reinforcement Learning with Plasticity Injection. CoRR Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
2023	A*	`conf`	Quantile Credit Assignment. ICML 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
2023	A*	`conf`	Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
2023	J	`jnl`	Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
2023	A*	`conf`	Settling the Reward Hypothesis. ICML Michael Bowling, John D. Martin, David Abel, Will Dabney
2023	A*	`conf`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	J	`jnl`	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
2023	A*	`conf`	Understanding Plasticity in Neural Networks. ICML Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML 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
2023	J	`jnl`	Understanding plasticity in neural networks. CoRR Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
2022	A*	`conf`	Generalised Policy Improvement with Geometric Policy Composition. ICML Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
2022	J	`jnl`	Generalised Policy Improvement with Geometric Policy Composition. CoRR Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto
2022	A*	`conf`	Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	J	`jnl`	Learning Dynamics and Generalization in Reinforcement Learning. CoRR Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
2022	A*	`conf`	On the Expressivity of Markov Reward (Extended Abstract). IJCAI David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2022	J	`jnl`	Settling the Reward Hypothesis. CoRR Michael Bowling, John D. Martin, David Abel, Will Dabney
2022	A*	`conf`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR 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
2022	A*	`conf`	Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR Clare Lyle, Mark Rowland, Will Dabney
2022	J	`jnl`	Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR Clare Lyle, Mark Rowland, Will Dabney
2021	A*	`conf`	Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML 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
2021	J	`jnl`	On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
2021	A	`conf`	On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney
2021	A*	`conf`	On the Expressivity of Markov Reward. NeurIPS David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	J	`jnl`	On the Expressivity of Markov Reward. CoRR David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	A*	`conf`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. ICML Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	J	`jnl`	Revisiting Peng's Q(λ) for Modern Reinforcement Learning. CoRR Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel
2021	A*	`conf`	Temporally-Extended ε-Greedy Exploration. ICLR Will Dabney, Georg Ostrovski, André Barreto
2021	A*	`conf`	The Difficulty of Passive Learning in Deep Reinforcement Learning. NeurIPS Georg Ostrovski, Pablo Samuel Castro, Will Dabney
2021	J	`jnl`	The Difficulty of Passive Learning in Deep Reinforcement Learning. CoRR Georg Ostrovski, Pablo Samuel Castro, Will Dabney
2021	A*	`conf`	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
2020	J	`jnl`	A distributional code for value in dopamine-based reinforcement learning. Nat. Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew M. Botvinick
2020	A	`conf`	Adaptive Trade-Offs in Off-Policy Learning. AISTATS Mark Rowland, Will Dabney, Rémi Munos
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	J	`jnl`	Counterfactual Credit Assignment in Model-Free Reinforcement Learning. CoRR 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
2020	J	`jnl`	Deep Reinforcement Learning and its Neuroscientific Implications. CoRR Matthew M. Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson
2020	A*	`conf`	Fast Task Inference with Variational Intrinsic Successor Features. ICLR Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih
2020	A*	`conf`	Revisiting Fundamentals of Experience Replay. ICML William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
2020	J	`jnl`	Revisiting Fundamentals of Experience Replay. CoRR William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney
2020	J	`jnl`	Temporally-Extended ε-Greedy Exploration. CoRR Will Dabney, Georg Ostrovski, André Barreto
2020	J	`jnl`	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
2019	A*	`conf`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2019	J	`jnl`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2019	J	`jnl`	Adapting Behaviour for Learning Progress. CoRR Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero
2019	J	`jnl`	Adaptive Trade-Offs in Off-Policy Learning. CoRR Mark Rowland, Will Dabney, Rémi Munos
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	J	`jnl`	Fast Task Inference with Variational Intrinsic Successor Features. CoRR Steven Hansen, Will Dabney, André Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih
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	—	`conf`	Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR (Poster) Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney
2019	A*	`conf`	Statistics and Samples in Distributional Reinforcement Learning. ICML Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	J	`jnl`	Statistics and Samples in Distributional Reinforcement Learning. CoRR Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	A	`conf`	The Termination Critic. AISTATS Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
2019	J	`jnl`	The Termination Critic. CoRR Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup
2018	A	`conf`	An Analysis of Categorical Distributional Reinforcement Learning. AISTATS Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
2018	A*	`conf`	Autoregressive Quantile Networks for Generative Modeling. ICML Georg Ostrovski, Will Dabney, Rémi Munos
2018	J	`jnl`	Autoregressive Quantile Networks for Generative Modeling. CoRR Georg Ostrovski, Will Dabney, Rémi Munos
2018	—	`conf`	Distributed Distributional Deterministic Policy Gradients. ICLR (Poster) Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap
2018	J	`jnl`	Distributed Distributional Deterministic Policy Gradients. CoRR Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap
2018	A*	`conf`	Distributional Reinforcement Learning With Quantile Regression. AAAI Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
2018	A*	`conf`	Implicit Quantile Networks for Distributional Reinforcement Learning. ICML Will Dabney, Georg Ostrovski, David Silver, Rémi Munos
2018	J	`jnl`	Implicit Quantile Networks for Distributional Reinforcement Learning. CoRR Will Dabney, Georg Ostrovski, David Silver, Rémi Munos
2018	J	`jnl`	Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery. CoRR Thomas S. Stepleton, Razvan Pascanu, Will Dabney, Siddhant M. Jayakumar, Hubert Soyer, Rémi Munos
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	—	`conf`	The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. ICLR (Poster) Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos
2017	A*	`conf`	A Distributional Perspective on Reinforcement Learning. ICML Marc G. Bellemare, Will Dabney, Rémi Munos
2017	J	`jnl`	A Distributional Perspective on Reinforcement Learning. CoRR Marc G. Bellemare, Will Dabney, Rémi Munos
2017	J	`jnl`	Distributional Reinforcement Learning with Quantile Regression. CoRR Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
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	—	`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	J	`jnl`	The Cramer Distance as a Solution to Biased Wasserstein Gradients. CoRR Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos