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Learning when to communicate and doing that effectively is essential in multi-agent tasks.
Multi-agent reinforcement learning: independent versus cooperative agents
Ming Tan · 1993
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Cooperative multi-agent learning: The state of the art
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L. Matignon, G. J. Laurent, and N. L. Fort-Piat · 2007
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A comprehensive survey of multiagent reinforcement learning
Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems
Laetitia Matignon, Guillaume J Laurent, and Nadine Le Fort-Piat · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Stefan Wender and Ian Watson · 2012
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 2012
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A survey of real-time strategy game ai research and competition in starcraft
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas 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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Counterfactual multi-agent policy gradients
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Cooperative multi-agent control using deep reinforcement learning
Jayesh K. Gupta, Maxim Egorov, and Mykel J. Kochenderfer · 2017
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Emergence of language with multi-agent games: learning to communicate with sequences of symbols
Serhii Havrylov and Ivan Titov · 2017
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Vain: Attentional multi-agent predictive modeling
Yedid Hoshen · 2017
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Natural language does not emerge ‘naturally’in multi-agent dialog
Satwik Kottur, José Moura, Stefan Lee, and Dhruv Batra · 2017
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Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni · 2016
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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, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
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Learning language games through interaction
Sida I Wang, Percy Liang, and Christopher D Manning · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson
Cited in the paper.
Jason Lee, Kyunghyun Cho, Jason Weston, and Douwe Kiela · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, YI WU, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2017
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Deep decentralized multi-task multi-agent reinforcement learning under partial observability
Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P How, and John Vian · 2017
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Multiagent bidirectionally-coordinated nets for learning to play starcraft combat games
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, and Jun Wang · 2017
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Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Julie Beaulieu, Peter J Bentley, Samuel Bernard, Guillaume Belson, David M Bryson, Nick Cheney, et al · 2018
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