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In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion.
Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem
Christian Schroeder de Witt and Thomas Hornigold · 1905
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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 · 1905
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Wendelin Böhmer, Vitaly Kurin, and Shimon Whiteson · 1910
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Learning from delayed rewards
Christopher Watkins · 1989
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Multi-agent reinforcement learning: Independent vs. cooperative agents
Ming Tan · 1993
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Approximation theory of the mlp model in neural networks
Allan Pinkus · 1999
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An algorithm for distributed reinforcement learning in cooperative multi-agent systems
Martin Lauer and Martin Riedmiller · 2000
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Multiagent Planning with Factored MDPs
Carlos Guestrin, Daphne Koller, and Ronald Parr · 2002
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Multiagent reinforcement learning for multi-robot systems: A survey
Erfu Yang and Dongbing Gu · 2004
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Keepaway soccer: From machine learning testbed to benchmark
Peter Stone, Gregory Kuhlmann, Matthew E Taylor, and Yaxin Liu · 2005
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Acme: A research framework for distributed reinforcement learning
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, and Nando de Freitas · 2006
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Collaborative Multiagent Reinforcement Learning by Payoff Propagation
Jelle R. Kok and Nikos Vlassis · 2006
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Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams
Laëtitia Matignon, Guillaume J Laurent, and Nadine Le 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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Optimal and Approximate Q-value Functions for Decentralized POMDPs
Frans A. Oliehoek, Matthijs T. J. Spaan, and Nikos Vlassis · 2008
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Theoretical advantages of lenient learners: An evolutionary game theoretic perspective
Liviu Panait, Karl Tuyls, and Sean Luke · 2008
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Incorporating functional knowledge in neural networks
Charles Dugas, Yoshua Bengio, Franois Blisle, Claude Nadeau, and Ren Garcia · 2009
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An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination
Yongcan Cao, Wenwu Yu, Wei Ren, and Guanrong Chen · 2012
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio · 2014
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Deep Recurrent Q-Learning for Partially Observable MDPs
Matthew Hausknecht and Peter Stone · 2015
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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, and others · 2015
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Yannis M Assael, Nando de Freitas, and Shimon Whiteson · 2016
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Deep reinforcement learning from self-play in imperfect-information games
Johannes Heinrich and David Silver · 2016
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Learning to play guess who? and inventing a grounded language as a consequence
Emilio Jorge, Mikael Kågebäck, and Emil Gustavsson · 2016
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Multi-agent reinforcement learning as a rehearsal for decentralized planning
Landon Kraemer and Bikramjit Banerjee · 2016
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A Concise Introduction to Decentralized POMDPs
Frans A. Oliehoek and Christopher Amato · 2016
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Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus · 2016
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TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, and Nicolas Usunier · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Lenient learning in independent-learner stochastic cooperative games
RLlib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, and Ion Stoica · 2018
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Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, and Aleksei Shpilman · 2018
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Lenient multi-agent deep reinforcement learning
Gregory Palmer, Karl Tuyls, Daan Bloembergen, and Rahul Savani · 2018
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Multi-goal reinforcement learning: Challenging robotics environments and request for research, 2018
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, and Wojciech Zaremba · 2018
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Machine theory of mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami, and Matthew Botvinick · 2018
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Ermo Wei and Sean Luke · 2016
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, and Shimon Whiteson · 2017
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Cooperative Multi-agent Control Using Deep Reinforcement Learning
Jayesh K. Gupta, Maxim Egorov, and Mykel Kochenderfer · 2017
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HyperNetworks
David Ha, Andrew Dai, and Quoc V. Le · 2017
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Guided Deep Reinforcement Learning for Swarm Systems
Maximilian Hüttenrauch, Adrian Šošić, and Gerhard Neumann · 2017
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Multi-agent reinforcement learning in sequential social dilemmas
Joel Z Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, and Thore Graepel · 2017
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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Multi-Agent Common Knowledge Reinforcement Learning
Christian A. Schroeder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, and Shimon Whiteson · 2018
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New blizzard custom game: Starcraft master
Blizzard Entertainment · 2018
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Mean field multi-agent reinforcement learning
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang · 2018
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The representational capacity of action-value networks for multi-agent reinforcement learning
Jacopo Castellini, Frans A Oliehoek, Rahul Savani, and Shimon Whiteson · 2019
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LIIR: Learning individual intrinsic reward in multi-agent reinforcement learning
Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, and Dacheng Tao · 2019
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Deep multi-agent reinforcement learning with discrete-continuous hybrid action spaces
Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, and Changjie Fan · 2019
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Cesma: Centralized expert supervises multi-agents
Alex Tong Lin, Mark J Debord, Katia Estabridis, Gary Hewer, and Stanley Osher · 2019
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Value function transfer for deep multi-agent reinforcement learning based on n-step returns
Yong Liu, Yujing Hu, Yang Gao, Yingfeng Chen, and Changjie Fan · 2019
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Xueguang Lu and Christopher Amato · 2019
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MAVEN: Multi-Agent Variational Exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson · 2019
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Multiagent Learning and Coordination with Clustered Deep Q-Network
Simon Pageaud, Véronique Deslandres, Vassilissa Lehoux, and Salima Hassas · 2019
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Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Gregory Palmer, Rahul Savani, and Karl Tuyls · 2019
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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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rlpyt: A research code base for deep reinforcement learning in pytorch
Adam Stooke and Pieter Abbeel · 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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Hierarchical cooperative multi-agent reinforcement learning with skill discovery
Jiachen Yang, Igor Borovikov, and Hongyuan Zha · 2019
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SMIX($\lambda$): Enhancing centralized value functions for cooperative multi-agent reinforcement learning
Xinghu Yao, Chao Wen, Yuhui Wang, and Xiaoyang Tan · 2019
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Efficient communication in multi-agent reinforcement learning via variance based control
Sai Qian Zhang, Qi Zhang, and Jieyu Lin · 2019
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Learning Efficient Communication in Cooperative Multi-Agent Environment
Yuhang Zhao and Xiujun Ma · 2019
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The hanabi challenge: A new frontier for ai research
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, and Michael Bowling · 2020
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Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
Christian Schroeder de Witt, Bei Peng, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Böhmer, and Shimon Whiteson · 2020
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