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Generalised Policy Improvement with Geometric Policy Composition



Shantanu Thakoor , Mark Rowland , Diana Borsa , Will Dabney , R茅mi Munos , Andr茅 Barreto

* ICML 2022 

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Learning Dynamics and Generalization in Reinforcement Learning



Clare Lyle , Mark Rowland , Will Dabney , Marta Kwiatkowska , Yarin Gal


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Understanding and Preventing Capacity Loss in Reinforcement Learning



Clare Lyle , Mark Rowland , Will Dabney

* Presented at ICLR 2022 

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On the Expressivity of Markov Reward



David Abel , Will Dabney , Anna Harutyunyan , Mark K. Ho , Michael L. Littman , Doina Precup , Satinder Singh

* Accepted to NeurIPS 2021 

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The Difficulty of Passive Learning in Deep Reinforcement Learning



Georg Ostrovski , Pablo Samuel Castro , Will Dabney

* Accepted paper at NeurIPS 2021 

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Revisiting Peng's Q($位$) for Modern Reinforcement Learning



Tadashi Kozuno , Yunhao Tang , Mark Rowland , R茅mi Munos , Steven Kapturowski , Will Dabney , Michal Valko , David Abel

* 26 pages, 7 figures, 2 tables 

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On The Effect of Auxiliary Tasks on Representation Dynamics



Clare Lyle , Mark Rowland , Georg Ostrovski , Will Dabney

* AISTATS 2021 

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Counterfactual Credit Assignment in Model-Free Reinforcement Learning



Thomas Mesnard , Th茅ophane Weber , Fabio Viola , Shantanu Thakoor , Alaa Saade , Anna Harutyunyan , Will Dabney , Tom Stepleton , Nicolas Heess , Arthur Guez , Marcus Hutter , Lars Buesing , R茅mi Munos


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Revisiting Fundamentals of Experience Replay



William Fedus , Prajit Ramachandran , Rishabh Agarwal , Yoshua Bengio , Hugo Larochelle , Mark Rowland , Will Dabney

* Published at ICML 2020. First two authors contributed equally and code available at https://github.com/google-research/google-research/tree/master/experience_replay 

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Deep Reinforcement Learning and its Neuroscientific Implications



Matthew Botvinick , Jane X. Wang , Will Dabney , Kevin J. Miller , Zeb Kurth-Nelson

* 22 pages, 5 figures 

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