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The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments.
Some studies in machine learning using the game of checkers
Arthur L Samuel · 1959
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Shape grammars and the generative specification of painting and sculpture
George Stiny and James Gips · 1971
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Adventures in level design: Generating missions and spaces for action adventure games
Joris Dormans · 2010
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The mario ai benchmark and competitions
Sergey Karakovskiy and Julian Togelius · 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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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
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
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Vizdoom: A doom-based ai research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
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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
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General video game ai: Competition, challenges and opportunities
Diego Perez-Liebana, Spyridon Samothrakis, Julian Togelius, Simon M Lucas, and Tom Schaul · 2016
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Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
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Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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Montezuma’s revenge solved by go-explore, a new algorithm for hard-exploration problems (sets records on pitfall too), 2018
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth Stanley, and Jeff Clune · 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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Unity: A general platform for intelligent agents
Arthur Juliani, Vincent-Pierre Berges, Esh Vckay, Yuan Gao, Hunter Henry, Marwan Mattar, and Danny Lange · 2018
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Illuminating generalization in deep reinforcement learning through procedural level generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, and Sebastian Risi · 2018
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Marlos C Machado, Marc G Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Dopamine: A research framework for deep reinforcement learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G Bellemare · 2018
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2018
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Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, and John Schulman · 2018
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Pommerman: A multi-agent playground
Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob Foerster, Julian Togelius, Kyunghyun Cho, and Joan Bruna · 2018
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Pac-man conquers academia: Two decades of research using a classic arcade game
Philipp Rohlfshagen, Jialin Liu, Diego Perez-Liebana, and Simon M Lucas · 2018
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A study on overfitting in deep reinforcement learning
Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio · 2018
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