Will Dabney

5.6k citations

18 papers · 2.0k indexed · 3 hit papers · h-index 13

Impact in

Artificial Intelligence top 1%
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety

Papers in

Artificial Intelligence 13
- Reinforcement Learning in Robotics 13
- Evolutionary Algorithms and Applications 4
Management Science and Operations Research 3
- Advanced Bandit Algorithms Research 2

Co-authors: Rémi Munos Georg Ostrovski David Silver Tom Schaul Mohammad Gheshlaghi Azar Bilal Piot Matteo Hessel Hado van Hasselt
Journals: Nature (1 paper)UCL Discovery (University College London) (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (9 papers)
Partner nations: United States United Kingdom Canada

In The Last Decade

Will Dabney

18 papers receiving 1.9k citations

Hit Papers

align trajectories log scale

A distributional code for value in dopamine-based reinforcement learning 2020 · 236 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2020 Nature
2018 Proceedings of the AAAI Conference on Artificial Intelligence
2018 Proceedings of the AAAI Conference on Artificial Intelligence

Peers

Countries citing papers authored by Will Dabney

Since Specialization

Citations

This map shows the geographic impact of Will Dabney's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Will Dabney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Will Dabney more than expected).

Fields of papers citing papers by Will Dabney

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Will Dabney. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Will Dabney. The network helps show where Will Dabney may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Will Dabney, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Will Dabney Line = papers co-authored together Will Dabney links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

18 of 18 papers shown

#	Work
1	Distributional Reinforcement Learning The MIT Press eBooks ·Marc G. Bellemare, Will Dabney, Mark Rowland	2023	34
2	On the Expressivity of Markov Reward (Extended Abstract) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence ·David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh	2022	1
3	A distributional code for value in dopamine-based reinforcement learning Nature ·Will Dabney, Zeb Kurth‐Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick Hit paper breakdown →	2020	236
4	Revisiting Fundamentals of Experience Replay arXiv (Cornell University) ·William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney	2020	32
5	A Geometric Perspective on Optimal Representations for Reinforcement Learning Neural Information Processing Systems ·Marc G. Bellemare, Will Dabney, Robert Dadashi, Laura Alvarez-Anton, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle	2019	8
6	Statistics and Samples in Distributional Reinforcement Learning arXiv (Cornell University) ·Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney	2019	13
7	The Termination Critic arXiv (Cornell University) ·Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup	2019	2
8	Successor Features for Transfer in Reinforcement Learning UCL Discovery (University College London) ·André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado P. van Hasselt	2019	50
9	Hindsight Credit Assignment arXiv (Cornell University) ·Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P. van Hasselt, Largu Ma, Satinder Singh, Doina Precup, Rémi Munos	2019	3
10	An Analysis of Categorical Distributional Reinforcement Learning International Conference on Artificial Intelligence and Statistics ·Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh	2018	4
11	Recurrent Experience Replay in Distributed Reinforcement Learning. International Conference on Learning Representations ·Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney	2018	94
12	Distributed Distributional Deterministic Policy Gradients arXiv (Cornell University) ·Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Jean-Pierre Bréchet, Alistair Muldal, Nicolas Heess, Timothy Lillicrap	2018	34
13	Implicit Quantile Networks for Distributional Reinforcement Learning arXiv (Cornell University) ·Will Dabney, Georg Ostrovski, David Silver, Rémi Munos	2018	19
14	Rainbow: Combining Improvements in Deep Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence ·Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver Hit paper breakdown →	2018	995
15	Distributional Reinforcement Learning With Quantile Regression Proceedings of the AAAI Conference on Artificial Intelligence ·Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos Hit paper breakdown →	2018	294
16	Rainbow: Combining Improvements in Deep Reinforcement Learning arXiv (Cornell University) ·Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, XU Weilang, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver	2017	146
17	The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning arXiv (Cornell University) ·Audrūnas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos	2017	15
18	Distributional Reinforcement Learning with Quantile Regression arXiv (Cornell University) ·Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos	2017	41

About Will Dabney

Will Dabney is a scholar working on Artificial Intelligence, Management Science and Operations Research, Economics and Econometrics, Accounting and Insect Science, having authored 18 papers that have together received 2.0k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (13 papers), Evolutionary Algorithms and Applications (4 papers), Sports Analytics and Performance (2 papers), Smart Grid Energy Management (2 papers), Advanced Bandit Algorithms Research (2 papers), Modular Robots and Swarm Intelligence (1 paper), Neural dynamics and brain function (1 paper) and Behavioral and Psychological Studies (1 paper). The work is most often cited by research in Artificial Intelligence (1.0k citations), Automotive Engineering (229 citations), Management Science and Operations Research (182 citations), Control and Systems Engineering (315 citations) and Computer Networks and Communications (308 citations). Will Dabney has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Rémi Munos, Georg Ostrovski, David Silver, Tom Schaul, Mohammad Gheshlaghi Azar, Bilal Piot, Matteo Hessel, Hado van Hasselt, Joseph Modayil and Mark Rowland. Their work appears in journals such as Nature, UCL Discovery (University College London), International Conference on Artificial Intelligence and Statistics, International Conference on Learning Representations and arXiv (Cornell University).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact