OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments

The arcade learning environment: An evaluation platform for general agents

Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013

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Unifying count-based exploration and intrinsic motivation

Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Rémi Munos · 2016

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Attend, infer, repeat: Fast scene understanding with generative models

S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, and Geoffrey E. Hinton · 2016

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A distributional perspective on reinforcement learning

Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017

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Investigating human priors for playing video games

Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, and Alexei A. Efros · 2018

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IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures

Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018

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Generalization and regularization in DQN

Jesse Farebrother, Marlos C. Machado, and Michael Bowling · 2018

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Unsupervised state representation learning in atari

Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, and R. Devon Hjelm · 2019

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Go-explore: a new approach for hard-exploration problems

Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2019

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Relevance-guided modeling of object dynamics for reinforcement learning

William Agnew and Pedro M. Domingos · 2020

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Agent57: Outperforming the atari human benchmark

Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, and Charles Blundell · 2020

Cited alongside, same era.

GENESIS: generative scene inference and sampling with object-centric latent representations

Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020

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Deep reinforcement learning at the edge of the statistical precipice

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021

Cited alongside, same era.

Adaptive rational activations to boost deep reinforcement learning

Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, and Kristian Kersting · 2021

Cited alongside, same era.

First return, then explore

Boosting object representation learning via motion and object continuity

Quentin Delfosse, Wolfgang Stammer, Thomas Rothenbacher, Dwarak Vittal, and Kristian Kersting · 2022

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Goal misgeneralization in deep reinforcement learning

Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, and David Krueger · 2022

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Generalization and robustness implications in object-centric learning

Andrea Dittadi, Samuele S. Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello · 2022

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Savi++: Towards end-to-end object-centric learning from real-world videos

Gamaleldin F. Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C. Mozer, and Thomas Kipf · 2022

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A typology for exploring the mitigation of shortcut behaviour

Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting · 2022

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