papers
Publications (15)
cs.AI2018
Successor Features for Transfer in Reinforcement Learning
André Barreto, Will Dabney, Rémi Munos +4
cs.LG2017
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Marc G. Bellemare, Ivo Danihelka, Will Dabney +4
cs.LG2014
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces
Sridhar Mahadevan, Bo Liu, Philip Thomas +5
cs.LG2017
A Distributional Perspective on Reinforcement Learning
Marc G. Bellemare, Will Dabney, Rémi Munos
cs.LG2018
Distributed Distributional Deterministic Policy Gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden +6
cs.LG2018
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver +1
cs.LG2018
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery
Thomas Stepleton, Razvan Pascanu, Will Dabney +3
cs.LG2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare, Will Dabney, Robert Dadashi +6
stat.ML2019
Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar +3
cs.AI2017
Distributional Reinforcement Learning with Quantile Regression
Will Dabney, Mark Rowland, Marc G. Bellemare +1
cs.AI2017
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt +7
cs.AI2019
The Termination Critic
Anna Harutyunyan, Will Dabney, Diana Borsa +3
stat.ML2018
An Analysis of Categorical Distributional Reinforcement Learning
Mark Rowland, Marc G. Bellemare, Will Dabney +2
cs.LG2018
Autoregressive Quantile Networks for Generative Modeling
Georg Ostrovski, Will Dabney, Rémi Munos
cs.AI2018
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar +3