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StarCraft, one of the most difficult esport games with long-standing history of professional tournaments, has attracted generations of players and fans, and also, intense attentions in artificial intelligence research.
Guided policy search
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Human-level control through deep reinforcement learning
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
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Fictitious self-play in extensive-form games
Johannes Heinrich, Marc Lanctot, and David Silver · 2015
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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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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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Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, and Soumith Chintala · 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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Reinforcement learning through asynchronous advantage actor-critic on a gpu
Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, and Jan Kautz · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Starcraft ii: a new challenge for reinforcement learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, et al · 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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A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Julien Perolat, David Silver, Thore Graepel, et al · 2017
Human-level performance in 3d multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C Rabinowitz, Ari S Morcos, Avraham Ruderman, et al · 2019
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Grid-wise control for multi-agent reinforcement learning in video game ai
Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, and Tong Zhang · 2019
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Liir: Learning individual intrinsic reward in multi-agent reinforcement learning
Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, and Dacheng Tao · 2019
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Open-ended learning in symmetric zero-sum games
David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M Czarnecki, Julien Perolat, Max Jaderberg, and Thore Graepel · 2019
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Seed rl: Scalable and efficient deep-rl with accelerated central inference
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https://blog.openai.com/openai-five/
Open ai five · 2018
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Tstarbots: Defeating the cheating level built-in ai in starcraft ii in the full game
Peng Sun, Xinghai Sun, Lei Han, Jiechao Xiong, Qing Wang, Bo Li, Yang Zheng, Ji Liu, Yongsheng Liu, Han Liu, et al · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Actor-critic policy optimization in partially observable multiagent environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, and Michael Bowling · 2018
Cited alongside, same era.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, and Marcin Michalski · 2019
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Arena: a toolkit for multi-agent reinforcement learning
Qing Wang, Jiechao Xiong, Lei Han, Meng Fang, Xinghai Sun, Zhuobin Zheng, Peng Sun, and Zhengyou Zhang · 2019
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Starcraft ii build order optimization using deep reinforcement learning and monte-carlo tree search
Islam Elnabarawy, Kristijana Arroyo, and Donald C Wunsch II · 2020
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Roma: Multi-agent reinforcement learning with emergent roles
Tonghan Wang, Heng Dong, Victor Lesser, and Chongjie Zhang · 2020
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Hierarchial reinforcement learning in starcraft ii with human expertise in subgoals selection
Xinyi Xu, Tiancheng Huang, Pengfei Wei, Akshay Narayan, and Tze-Yun Leong · 2020
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TLeague: A framework for competitive self-play based distributed multi-agent reinforcement learning
Peng Sun, Jiechao Xiong, Lei Han, Xinghai Sun, Shuxing Li, Jiawei Xu, Meng Fang, and Zhang Zhengyou · 2020
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