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The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community.
Non-cooperative games
John Nash · 1951
Earlier work this paper cites.
A generalized theorem of the maximum
Lawrence M Ausubel and Raymond J Deneckere · 1993
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
R. S. Sutton, D. Mcallester, S. Singh, and Y. Mansour · 2000
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, F. Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Counterfactual multi-agent policy gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Fully decentralized multi-agent reinforcement learning with networked agents
Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, and Tamer Basar · 2018
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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
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Multi-agent trust region policy optimization
Hepeng Li and Haibo He · 2020
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Facmac: Factored multi-agent centralised policy gradients
Bei Peng, Tabish Rashid, Christian A Schroeder de Witt, Pierre-Alexandre Kamienny, Philip HS Torr, Wendelin Böhmer, and Shimon Whiteson · 2020
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Truly proximal policy optimization
Yuhui Wang, Hao He, and Xiaoyang Tan · 2020
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An overview of multi-agent reinforcement learning from game theoretical perspective
Yaodong Yang and Jun Wang · 2020
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Settling the variance of multi-agent policy gradients
Jakub Grudzien Kuba, Muning Wen, Linghui Meng, Haifeng Zhang, David Mguni, Jun Wang, Yaodong Yang, et al · 2021
Later among the works it cites.
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Deep multi-agent reinforcement learning for decentralized continuous cooperative control
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Implementation matters in deep policy gradients: A case study on ppo and trpo
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Revisiting design choices in proximal policy optimization
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Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, and Stefano V. Albrecht · 2021
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Tianshou: a highly modularized deep reinforcement learning library
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The surprising effectiveness of mappo in cooperative, multi-agent games
Chao Yu, A. Velu, Eugene Vinitsky, Yu Wang, A. Bayen, and Yi Wu · 2021
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Trust region policy optimisation in multi-agent reinforcement learning
Jakub Grudzien Kuba, Ruiqing Chen, Munning Wen, Ying Wen, Fanglei Sun, Jun Wang, and Yaodong Yang · 2022
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