Publications

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

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Mark Rowland, Will Dabney, Rémi Munos (2020). Adaptive Trade-Offs in Off-Policy Learning. AISTATS.

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

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Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick (2020). A distributional code for value in dopamine-based reinforcement learning. Nature.

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Anna Harutyunyan, Will Dabney, Thomas Mesnard, Nicolas Heess, Mohammad G. Azar, Bilal Piot, Hado van Hasselt, Satinder Singh, Greg Wayne, Doina Precup, Rémi Munos (2019). Hindsight Credit Assignment. NeurIPS.

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

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Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney (2019). Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR.

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Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup (2019). The Termination Critic. AISTATS.

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Will Dabney, Georg Ostrovski, David Silver, Rémi Munos (2018). Implicit Quantile Networks for Distributional Reinforcement Learning. ICML.

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Georg Ostrovski, Will Dabney, Rémi Munos (2018). Autoregressive Quantile Networks for Generative Modeling. ICML.

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Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc Bellemare, Remi Munos (2018). The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. ICLR.

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Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap (2018). Distributed Distributional Deterministic Policy Gradients. ICLR.

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

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Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver (2018). Rainbow: Combining improvements in deep reinforcement learning. AAAI.

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

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André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, Hado van Hasselt, David Silver (2017). Successor features for transfer in reinforcement learning. NeurIPS.

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Marc G. Bellemare, Will Dabney, Rémi Munos (2017). A distributional perspective on reinforcement learning. ICML.

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Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney (2017). Statistics and Samples in Distributional Reinforcement Learning. ICML.

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