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Recent works have proven that intricate cooperative behaviors can emerge in agents trained using meta reinforcement learning on open ended task distributions using self-play.
Multi-agent reinforcement learning: Independent vs. cooperative agents
Ming Tan · 1993
Earlier work this paper cites.
Simple principles of metalearning
Juergen Schmidhuber, Jieyu Zhao, and MA Wiering · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
An approach to the pursuit problem on a heterogeneous multiagent system using reinforcement learning
Yuko Ishiwaka, Takamasa Sato, and Yukinori Kakazu · 2003
Earlier work this paper cites.
Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
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 A. Riedmiller, Andreas Kirkeby Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Rl 2 : Fast reinforcement learning via slow reinforcement learning, 2016
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Critic learning in multi agent credit assignment problem
Zahra Rahaie and Hamid Beigy · 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.
A multi-agent reinforcement learning model of common-pool resource appropriation
Julien Perolat, Joel Z Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, and Thore Graepel · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Learning to reinforcement learn, 2017
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2017
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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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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
Cited alongside, same era.
The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder De Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson · 2019
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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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Emergent tool use from multi-agent autocurricula, 2020
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2020
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Is independent learning all you need in the starcraft multi-agent challenge?
Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip HS Torr, Mingfei Sun, and Shimon Whiteson · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
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Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
Cited alongside, same era.
Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Emergent coordination through competition, 2019
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, and Thore Graepel · 2019
Cited alongside, same era.
Concurrent meta reinforcement learning
Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy, Yuhuai Wu, and Ruslan Salakhutdinov · 2019
Cited alongside, same era.
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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Simple-playgrounds
Michael Garcia Ortiz, Vince Jankovics, Hugo Caselles-Dupre, and Louis Annabi · 2021
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Open-ended learning leads to generally capable agents, 2021
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, and Wojciech Marian Czarnecki · 2021
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Maestro: Open-ended environment design for multi-agent reinforcement learning
Mikayel Samvelyan, Akbir Khan, Michael Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Roberta Raileanu, and Tim Rocktäschel · 2023
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Human-timescale adaptation in an open-ended task space
Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, et al · 2023
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