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Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills.
Q-learning
Christopher JCH Watkins and Peter Dayan. 1992 · 1992
Earlier work this paper cites.
Feudal reinforcement learning. In Advances in neural information processing systems
Peter Dayan and Geoffrey E Hinton. 1993 · 1993
Earlier work this paper cites.
Multi-agent reinforcement learning: Independent vs. cooperative agents. In Proceedings of the tenth international conference on machine learning
Ming Tan. 1993 · 1993
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman. 1994 · 1994
Earlier work this paper cites.
Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal. 1997 · 1997
Earlier work this paper cites.
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh. 1999 · 1999
Earlier work this paper cites.
Temporal Abstraction in Reinforcement Learning
Doina Precup. 2000 · 2000
Earlier work this paper cites.
Multiagent systems: A survey from a machine learning perspective
Peter Stone and Manuela Veloso. 2000 · 2000
Earlier work this paper cites.
Hierarchical multi-agent reinforcement learning. In Proceedings of the fifth international conference on Autonomous agents
Rajbala Makar, Sridhar Mahadevan, and Mohammad Ghavamzadeh. 2001 · 2001
Earlier work this paper cites.
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density. In Proceedings of the Eighteenth International Conference on Machine Learning
Amy McGovern and Andrew G Barto. 2001 · 2001
Earlier work this paper cites.
The complexity of decentralized control of Markov decision processes
Daniel S Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein. 2002 · 2002
Earlier work this paper cites.
Learning options in reinforcement learning. In International Symposium on abstraction, reformulation, and approximation
Martin Stolle and Doina Precup. 2002 · 2002
Earlier work this paper cites.
Coordinated multi-agent imitation learning. In Proceedings of the 34th International Conference on Machine Learning-Volume 70
Hoang M Le, Yisong Yue, Peter Carr, and Patrick Lucey. 2017 · 2003
Earlier work this paper cites.
Learning to take concurrent actions. In Advances in neural information processing systems
Khashayar Rohanimanesh and Sridhar Mahadevan. 2003 · 2003
Earlier work this paper cites.
Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke. 2005 · 2005
Earlier work this paper cites.
Hierarchical multi-agent reinforcement learning
Mohammad Ghavamzadeh, Sridhar Mahadevan, and Rajbala Makar. 2006 · 2006
Earlier work this paper cites.
An overview of bilevel optimization
Benoît Colson, Patrice Marcotte, and Gilles Savard. 2007 · 2007
Cited alongside, same era.
Ad hoc autonomous agent teams: Collaboration without pre-coordination. In Twenty-Fourth AAAI Conference on Artificial Intelligence
Peter Stone, Gal A Kaminka, Sarit Kraus, and Jeffrey S Rosenschein. 2010 · 2010
Cited alongside, same era.
Bayesian policy search for multi-agent role discovery. In Twenty-Fourth AAAI Conference on Artificial Intelligence
Aaron Wilson, Alan Fern, and Prasad Tadepalli. 2010 · 2010
Cited alongside, same era.
An overview of recent progress in the study of distributed multi-agent coordination
Yongcan Cao, Wenwu Yu, Wei Ren, and Guanrong Chen. 2013 · 2013
Cited alongside, same era.
Auto-encoding variational bayes. In International Conference on Learning Representations
Diederik P Kingma and Max Welling. 2014 · 2014
Cited alongside, same era.
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. In Proceedings of the 35th International Conference on Machine Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. 2018 · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto. 2018 · 2018
Later among the works it cites.
Hierarchical deep multiagent reinforcement learning
Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Changjie Fan, and Li Wang. 2018 · 2018
Later among the works it cites.
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
Jiachen Yang, Alireza Nakhaei, David Isele, Hongyuan Zha, and Kikuo Fujimura. 2018 · 2018
Later among the works it cites.
Feudal multi-agent hierarchies for cooperative reinforcement learning
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Alexander Franks, Andrew Miller, Luke Bornn, Kirk Goldsberry, et al · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 2016 · 2016
Cited alongside, same era.
Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra. 2016 · 2016
Cited alongside, same era.
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. In Advances in neural information processing systems
Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum. 2016 · 2016
Cited alongside, same era.
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
Cited alongside, same era.
The option-critic architecture. In Thirty-First AAAI Conference on Artificial Intelligence
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Cited alongside, same era.
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Closest in time.
Diversity is all you need: Learning skills without a reward function. In International Conference on Learning Representations
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. 2019 · 2019
Closest in time.
Google Research Football: A Novel Reinforcement Learning Environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zaja̧c, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, et al · 2019
Closest in time.
Emergent coordination through competition. In International Conference on Learning Representations
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, and Thore Graepel. 2019 · 2019
Closest in time.
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Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Hostallero, and Yung Yi. 2019 · 2019
Closest in time.
Zhi Zhang, Jiachen Yang, and Hongyuan Zha. 2019 · 2019
Closest in time.
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Yunqi Zhao, Igor Borovikov, Ahmad Beirami, Jason Rupert, Caedmon Somers, Jesse Harder, Fernando de Mesentier Silva, John Kolen, Jervis Pinto, Reza Pourabolghasem, et al · 2019
Closest in time.
On Multi-Agent Learning in Team Sports Games
Yunqi Zhao, Igor Borovikov, Jason Rupert, Caedmon Somers, and Ahmad Beirami. 2019b · 2019
Closest in time.
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Youngwoon Lee, Jingyun Yang, and Joseph J. Lim. 2020 · 2020
Closest in time.
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Caedmon Somers, Jason Rupert, Yunqi Zhao, Igor Borovikov, and Jiachen Yang. 2020 · 2020
Closest in time.
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Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2087
Closest in time.