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In cooperative multi-agent tasks, a team of agents jointly interact with an environment by taking actions, receiving a team reward and observing the next state.
Multi-agent reinforcement learning: Independent versus cooperative agents
M. Tan · 1993
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Coordinated reinforcement learning
Carlos Guestrin, Michail G. Lagoudakis, and Ronald Parr · 2002
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Collaborative multiagent reinforcement learning by payoff propagation
Jelle R. Kok and Nikos Vlassis · 2006
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Coordinated multi-agent reinforcement learning in networked distributed pomdps
Chongjie Zhang and Victor Lesser · 2011
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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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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A concise introduction to decentralized POMDPs
Frans A Oliehoek and Christopher Amato · 2016
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A distributional perspective on reinforcement learning
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
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Distributional reinforcement learning with quantile regression
Will Dabney, Mark Rowland, Marc G. Bellemare, and Rémi Munos · 2017
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Multiagent cooperation and competition with deep reinforcement learning
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Implicit quantile networks for distributional reinforcement learning
Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
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QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Dota 2 with large scale deep reinforcement learning
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Towards playing full moba games with deep reinforcement learning
Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, and Wei Liu · 2020
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Jian Hu, Seth Austin Harding, Haibin Wu, Siyue Hu, and Shih-wei Liao · 2020
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Qatten: A general framework for cooperative multiagent reinforcement learning
Yaodong Yang, Jianye Hao, Ben Liao, Kun Shao, Guangyong Chen, Wulong Liu, and Hongyao Tang · 2020
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DRLE: Decentralized reinforcement learning at the edge for traffic light control in the iov
Pengyuan Zhou, Xianfu Chen, Zhi Liu, Tristan Braud, Pan Hui, and Jussi Kangasharju · 2021
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RMIX: Learning risk-sensitive policies for cooperative reinforcement learning agents
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Fully parameterized quantile function for distributional reinforcement learning
Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, and Tie-Yan Liu · 2019
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The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philiph H. S. Torr, Jakob Foerster, and Shimon Whiteson · 2019
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QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
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Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, and Zinovi Rabinovich · 2021
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DFAC framework: Factorizing the value function via quantile mixture for multi-agent distributional q-learning
Wei-Fang Sun, Cheng-Kuang Lee, and Chun-Yi Lee · 2021
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QPLEX: Duplex dueling multi-agent q-learning
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang · 2021
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