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This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game.
A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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A Turing test for computer game bots
Philip Hingston · 2009
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The 2009 Mario AI competition
Julian Togelius, Sergey Karakovskiy, and Robin Baumgarten · 2010
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Ms Pac-man versus ghost team CEC 2011 competition
Philipp Rohlfshagen and Simon M Lucas · 2011
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Measuring intelligence through games
Tom Schaul, Julian Togelius, and Jürgen Schmidhuber · 2011
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Real-time strategy game competitions
Michael Buro and David Churchill · 2012
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
George E Dahl, Dong Yu, Li Deng, and Alex Acero · 2012
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Classq-l: A q-learning algorithm for adversarial real-time strategy games
Ulit Jaidee and Héctor Muñoz-Avila · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 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 survey of real-time strategy game AI research and competition in StarCraft
Santiago Ontanón, Gabriel Synnaeve, Alberto Uriarte, Florian Richoux, David Churchill, and Mike Preuss · 2013
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A video game description language for model-based or interactive learning
Tom Schaul · 2013
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Global state evaluation in StarCraft
Graham Kurtis Stephen Erickson and Michael Buro · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Move evaluation in Go using deep convolutional neural networks
Chris J Maddison, Aja Huang, Ilya Sutskever, and David Silver · 2014
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A review of real-time strategy game AI
Glen Robertson and Ian Watson · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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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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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Sim-to-real robot learning from pixels with progressive nets
Andrei A Rusu, Matej Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell · 2016
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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
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Torchcraft: a library for machine learning research on real-time strategy games
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The 2014 general video game playing competition
Diego Perez, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon Lucas, Adrien Couëtoux, Jeyull Lee, Chong-U Lim, and Tommy Thompson · 2015
Cited alongside, same era.
ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
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Playing SNES in the retro learning environment
Nadav Bhonker, Shai Rozenberg, and Itay Hubara · 2016
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 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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Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, and Nicolas Usunier · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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http://bwapi.github.io/ , 2017
The Brood War API · 2017
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Learning from demonstrations for real world reinforcement learning
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, and John Agapiou · 2017
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Learning macromanagement in StarCraft from replays using deep learning
Niels Justesen and Sebastian Risi · 2017
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Multiagent bidirectionally-coordinated nets for learning to play starcraft combat games
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, and Jun Wang · 2017
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Data-efficient deep reinforcement learning for dexterous manipulation
Ivaylo Popov, Nicolas Heess, Timothy Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerik, Thomas Lampe, Yuval Tassa, Tom Erez, and Martin Riedmiller · 2017
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Episodic exploration for deep deterministic policies for StarCraft micromanagement
Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, and Soumith Chintala · 2017
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