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Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward.
Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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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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A neuroevolution approach to general atari game playing
Matthew Hausknecht, Joel Lehman, Risto Miikkulainen, and Peter Stone · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
Earlier work this paper cites.
Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Van Hasselt, Marc Lanctot, and Nando De Freitas · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2016
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Learning values across many orders of magnitude
Hado P van Hasselt, Arthur Guez, Arthur Guez, Matteo Hessel, Volodymyr Mnih, and David Silver · 2016
Cited alongside, same era.
A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
Cited alongside, same era.
Noisy networks for exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, et al · 2017
Cited alongside, same era.
Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron van den Oord, and Rémi Munos · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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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 Azar, and David Silver · 2018
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Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado Van Hasselt, and David Silver · 2018
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Open source implementation of rainbow by kaixhin, 2018
Kaixhin · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C Machado, Marc G Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2018
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Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Dopamine: A research framework for deep reinforcement learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G Bellemare · 2018
Cited alongside, same era.
Implicit quantile networks for distributional reinforcement learning
Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos · 2018
Cited alongside, same era.
Montezuma’s Revenge Solved by Go-Explore, a New Algorithm for Hard-Exploration Problems
Lehman Joel Kenneth O. Stanley Ecoffet Adrien, Huizinga Joost and Jeff Clune · 2018
Cited alongside, same era.
How to match DeepMind’s Deep Q-Learning score in Breakout
Fabio M. Graetz · 2018
Cited alongside, same era.
AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
Oriol Vinyals, Igor Babuschkin, Junyoung Chung, Michael Mathieu, Max Jaderberg, Wojciech M. Czarnecki, Andrew Dudzik, Aja Huang, Petko Georgiev, Richard Powell, Timo Ewalds, Dan Horgan, Manuel Kroiss, Ivo Danihelka, John Agapiou, Junhyuk Oh, Valentin Dalibard, David Choi, Laurent Sifre, Yury Sulsky, Sasha Vezhnevets, James Molloy, Trevor Cai, David Budden, Tom Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Toby Pohlen, Yuhuai Wu, Dani Yogatama, Julia Cohen, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Chris Apps, Koray Kavukcuoglu, Demis Hassabis, and David Silver
Cited in the paper.
OpenAI Five
OpenAI · 2018
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Observe and look further: Achieving consistent performance on atari
Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, and Olivier Pietquin · 2018
Later among the works it cites.
Go-explore: a new approach for hard-exploration problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2019
Closest in time.
Reproducibility checklist
J. Pineau · 2019
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Redis database
Redis · 2019
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