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ORCIDWill Dabney
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- William Dabney — University of Massachusetts Amherst, Department of Computer Science
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2020 – today
- 2024
[j2]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. J. Mach. Learn. Res. 25: 163:1-163:47 (2024)- [c39]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. ICML 2024 - [i45]Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney:
Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. CoRR abs/2402.05766 (2024) - [i44]Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney:
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. CoRR abs/2402.07598 (2024) - [i43]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 abs/2402.08530 (2024) - [i42]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 abs/2402.18762 (2024) - [i41]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 abs/2405.08448 (2024) - [i40]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 abs/2406.02035 (2024) - [i39]Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney:
Normalization and effective learning rates in reinforcement learning. CoRR abs/2407.01800 (2024) - [i38]Sebastian Lee, Samuel Liebana Garcia, Claudia Clopath, Will Dabney:
Lifelong Reinforcement Learning via Neuromodulation. CoRR abs/2408.08446 (2024) - 2023
- [c38]Michael Bowling, John D. Martin, David Abel, Will Dabney:
Settling the Reward Hypothesis. ICML 2023: 3003-3020 - [c37]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: 4009-4034 - [c36]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. ICML 2023: 18686-18713 - [c35]Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney:
Understanding Plasticity in Neural Networks. ICML 2023: 23190-23211 - [c34]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: 24517-24531 - [c33]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: 29210-29231 - [c32]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: 33632-33656 - [c31]Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto:
Deep Reinforcement Learning with Plasticity Injection. NeurIPS 2023 - [i37]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 abs/2301.04462 (2023) - [i36]Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney:
Understanding plasticity in neural networks. CoRR abs/2303.01486 (2023) - [i35]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 abs/2305.00654 (2023) - [i34]Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto:
Deep Reinforcement Learning with Plasticity Injection. CoRR abs/2305.15555 (2023) - [i33]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 abs/2305.18388 (2023) - [i32]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. CoRR abs/2306.10171 (2023) - 2022
- [c30]Clare Lyle, Mark Rowland, Will Dabney:
Understanding and Preventing Capacity Loss in Reinforcement Learning. ICLR 2022 - [c29]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Deep Reinforcement Learning. ICML 2022: 14560-14581 - [c28]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. ICML 2022: 21272-21307 - [c27]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: 5254-5258 - [c26]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 - [i31]Clare Lyle, Mark Rowland, Will Dabney:
Understanding and Preventing Capacity Loss in Reinforcement Learning. CoRR abs/2204.09560 (2022) - [i30]Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal:
Learning Dynamics and Generalization in Reinforcement Learning. CoRR abs/2206.02126 (2022) - [i29]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. CoRR abs/2206.08736 (2022) - [i28]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 abs/2207.07570 (2022) - [i27]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 abs/2212.03319 (2022) - [i26]Michael Bowling, John D. Martin, David Abel, Will Dabney:
Settling the Reward Hypothesis. CoRR abs/2212.10420 (2022) - 2021
- [c25]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: 7160-7168 - [c24]Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On the Effect of Auxiliary Tasks on Representation Dynamics. AISTATS 2021: 1-9 - [c23]Will Dabney, Georg Ostrovski, André Barreto:
Temporally-Extended ε-Greedy Exploration. ICLR 2021 - [c22]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: 5794-5804 - [c21]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: 7654-7664 - [c20]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. NeurIPS 2021: 7799-7812 - [c19]Georg Ostrovski, Pablo Samuel Castro, Will Dabney:
The Difficulty of Passive Learning in Deep Reinforcement Learning. NeurIPS 2021: 23283-23295 - [i25]Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney:
On The Effect of Auxiliary Tasks on Representation Dynamics. CoRR abs/2102.13089 (2021) - [i24]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 abs/2103.00107 (2021) - [i23]Georg Ostrovski, Pablo Samuel Castro, Will Dabney:
The Difficulty of Passive Learning in Deep Reinforcement Learning. CoRR abs/2110.14020 (2021) - [i22]David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh:
On the Expressivity of Markov Reward. CoRR abs/2111.00876 (2021) - 2020
- [j1]Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew M. Botvinick:
A distributional code for value in dopamine-based reinforcement learning. Nat. 577(7792): 671-675 (2020) - [c18]Mark Rowland, Will Dabney, Rémi Munos:
Adaptive Trade-Offs in Off-Policy Learning. AISTATS 2020: 34-44 - [c17]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. AISTATS 2020: 45-55 - [c16]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 - [c15]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. ICML 2020: 3061-3071 - [i21]Will Dabney, Georg Ostrovski, André Barreto:
Temporally-Extended ε-Greedy Exploration. CoRR abs/2006.01782 (2020) - [i20]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 abs/2006.02243 (2020) - [i19]Matthew M. Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson:
Deep Reinforcement Learning and its Neuroscientific Implications. CoRR abs/2007.03750 (2020) - [i18]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. CoRR abs/2007.06700 (2020) - [i17]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 abs/2011.09464 (2020)
2010 – 2019
- 2019
- [c14]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. AISTATS 2019: 2231-2240 - [c13]Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney:
Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR (Poster) 2019 - [c12]Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney:
Statistics and Samples in Distributional Reinforcement Learning. ICML 2019: 5528-5536 - [c11]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: 4360-4371 - [c10]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: 12467-12476 - [i16]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 abs/1901.11530 (2019) - [i15]Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney:
Statistics and Samples in Distributional Reinforcement Learning. CoRR abs/1902.08102 (2019) - [i14]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. CoRR abs/1902.09996 (2019) - [i13]Steven Hansen, Will Dabney, André Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih:
Fast Task Inference with Variational Intrinsic Successor Features. CoRR abs/1906.05030 (2019) - [i12]Mark Rowland, Will Dabney, Rémi Munos:
Adaptive Trade-Offs in Off-Policy Learning. CoRR abs/1910.07478 (2019) - [i11]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. CoRR abs/1910.07479 (2019) - [i10]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 abs/1912.02503 (2019) - [i9]Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero:
Adapting Behaviour for Learning Progress. CoRR abs/1912.06910 (2019) - 2018
- [c9]Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos:
Distributional Reinforcement Learning With Quantile Regression. AAAI 2018: 2892-2901 - [c8]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: 3215-3222 - [c7]Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh:
An Analysis of Categorical Distributional Reinforcement Learning. AISTATS 2018: 29-37 - [c6]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 - [c5]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 - [c4]Will Dabney, Georg Ostrovski, David Silver, Rémi Munos:
Implicit Quantile Networks for Distributional Reinforcement Learning. ICML 2018: 1104-1113 - [c3]Georg Ostrovski, Will Dabney, Rémi Munos:
Autoregressive Quantile Networks for Generative Modeling. ICML 2018: 3933-3942 - [i8]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 abs/1804.08617 (2018) - [i7]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 abs/1805.04955 (2018) - [i6]Georg Ostrovski, Will Dabney, Rémi Munos:
Autoregressive Quantile Networks for Generative Modeling. CoRR abs/1806.05575 (2018) - [i5]Will Dabney, Georg Ostrovski, David Silver, Rémi Munos:
Implicit Quantile Networks for Distributional Reinforcement Learning. CoRR abs/1806.06923 (2018) - 2017
- [c2]Marc G. Bellemare, Will Dabney, Rémi Munos:
A Distributional Perspective on Reinforcement Learning. ICML 2017: 449-458 - [c1]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: 4055-4065 - [i4]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 abs/1705.10743 (2017) - [i3]Marc G. Bellemare, Will Dabney, Rémi Munos:
A Distributional Perspective on Reinforcement Learning. CoRR abs/1707.06887 (2017) - [i2]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 abs/1710.02298 (2017) - [i1]Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos:
Distributional Reinforcement Learning with Quantile Regression. CoRR abs/1710.10044 (2017)
Coauthor Index
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