Payoff Performance of Fictitious Play. - Georg Ostrovski; Sebastian van Strien (CoRR, 2013)

Human-level control through deep reinforcement learning. - Volodymyr Mnih; Koray Kavukcuoglu; David Silver; Andrei A. Rusu; Joel Veness; Marc G. Bellemare; Alex Graves; Martin A. Riedmiller; Andreas Fidjeland; Georg Ostrovski; Stig Petersen; Charles Beattie; et al. (Nat., 2015)

Increasing the Action Gap: New Operators for Reinforcement Learning. - Marc G. Bellemare; Georg Ostrovski; Arthur Guez; Philip S. Thomas; Rémi Munos (AAAI, 2016)

Unifying Count-Based Exploration and Intrinsic Motivation. - Marc G. Bellemare; Sriram Srinivasan 005; Georg Ostrovski; Tom Schaul; David Saxton; Rémi Munos (NIPS, 2016)

Hybrid computing using a neural network with dynamic external memory. - Alex Graves; Greg Wayne; Malcolm Reynolds; Tim Harley; Ivo Danihelka; Agnieszka Grabska-Barwinska; Sergio Gomez Colmenarejo; Edward Grefenstette; Tiago Ramalho; John P. Agapiou; Adrià Puigdomènech Badia; Karl Moritz Hermann; et al. (Nat., 2016)

Count-Based Exploration with Neural Density Models. - Georg Ostrovski; Marc G. Bellemare; Aäron van den Oord; Rémi Munos (ICML, 2017)

Symmetric Decomposition of Asymmetric Games. - Karl Tuyls; Julien Pérolat; Marc Lanctot; Georg Ostrovski; Rahul Savani; Joel Z. Leibo; Toby Ord; Thore Graepel; Shane Legg (CoRR, 2017)

Rainbow: Combining Improvements in Deep Reinforcement Learning. - Matteo Hessel; Joseph Modayil; Hado van Hasselt; Tom Schaul; Georg Ostrovski; Will Dabney; Dan Horgan; Bilal Piot; Mohammad Gheshlaghi Azar; David Silver (AAAI, 2018)

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

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

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

Adapting Behaviour for Learning Progress. - Tom Schaul; Diana Borsa; David Ding; David Szepesvari; Georg Ostrovski; Will Dabney; Simon Osindero (CoRR, 2019)

On the Effect of Auxiliary Tasks on Representation Dynamics. - Clare Lyle; Mark Rowland 001; Georg Ostrovski; Will Dabney (AISTATS, 2021)

Temporally-Extended ε-Greedy Exploration. - Will Dabney; Georg Ostrovski; André Barreto 001 (ICLR, 2021)

The Difficulty of Passive Learning in Deep Reinforcement Learning. - Georg Ostrovski; Pablo Samuel Castro; Will Dabney (NeurIPS, 2021)

Return-based Scaling: Yet Another Normalisation Trick for Deep RL. - Tom Schaul; Georg Ostrovski; Iurii Kemaev; Diana Borsa (CoRR, 2021)

When should agents explore? - Miruna Pislar; David Szepesvari; Georg Ostrovski; Diana L. Borsa; Tom Schaul (ICLR, 2022)

The Phenomenon of Policy Churn. - Tom Schaul; André Barreto 001; John Quan; Georg Ostrovski (NeurIPS, 2022)

An empirical study of implicit regularization in deep offline RL. - Çaglar Gülçehre; Srivatsan Srinivasan; Jakub Sygnowski; Georg Ostrovski; Mehrdad Farajtabar; Matthew Hoffman 002; Razvan Pascanu; Arnaud Doucet (Trans. Mach. Learn. Res., 2022)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

Deep Reinforcement Learning with Plasticity Injection. - Evgenii Nikishin; Junhyuk Oh; Georg Ostrovski; Clare Lyle; Razvan Pascanu; Will Dabney; André Barreto 001 (NeurIPS, 2023)

An Analysis of Quantile Temporal-Difference Learning. - Mark Rowland 001; Rémi Munos; Mohammad Gheshlaghi Azar; Yunhao Tang; Georg Ostrovski; Anna Harutyunyan; Karl Tuyls; Marc G. Bellemare; Will Dabney (J. Mach. Learn. Res., 2024)