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Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations.
Multi-agent reinforcement learning: Independent versus cooperative agents
Ming Tan · 1993
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Joseph Farrell and Matthew Rabin · 1996
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The dynamics of reinforcement learning in cooperative multiagent systems
Caroline Claus and Craig Boutilier · 1998
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Actor-critic algorithms
Vijay R. Konda and John N. Tsitsiklis · 1999
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Multiagent systems: A survey from a machine learning perspective
Peter Stone and Manuela M. Veloso · 2000
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Dynamic programming for partially observable stochastic games
Eric A. Hansen, Daniel S. Bernstein, and Shlomo Zilberstein · 2004
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Marp: a multi-agent routing protocol for mobile wireless ad hoc networks
Romit Roy Choudhury, Krishna Paul, and Somprakash Bandyopadhyay · 2004
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Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
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Multi-agent reinforcement learning: A survey
Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2006
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Lucian Busoniu, Robert Babuska, and Bart De Schutter · 2008
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Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations
Yoav Shoham and Kevin Leyton-Brown · 2009
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A survey on sensor networks from a multiagent perspective
Meritxell Vinyals, Juan A. Rodríguez-Aguilar, and Jesús Cerquides · 2011
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Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems
Laëtitia Matignon, Guillaume J. Laurent, and Nadine Le Fort-Piat · 2012
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Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems
Laëtitia Matignon, Guillaume J. Laurent, and Nadine Le Fort-Piat · 2012
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Reinforcement learning in robotics: A survey
Jens Kober, J. Andrew Bagnell, and Jan Peters · 2013
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Combining multiple correlated reward and shaping signals by measuring confidence
Tim Brys, Ann Nowé, Daniel Kudenko, and Matthew E. Taylor · 2014
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Application impact of multi-agent systems and technologies: A survey
Jörg P. Müller and Klaus Fischer · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 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 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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Multiagent cooperation and competition with deep reinforcement learning
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, and Raul Vicente · 2015
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Trust and reputation models for multiagent systems
Jones Granatyr, Vanderson Botelho, Otto Robert Lessing, Edson Emílio Scalabrin, Jean-Paul A. Barthès, and Fabrício Enembreck · 2015
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Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
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A Concise Introduction to Decentralized POMDPs
Frans A. Oliehoek and Christopher Amato · 2016
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A Concise Introduction to Decentralized POMDPs
Frans A. Oliehoek and Christopher Amato · 2016
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and Pieter Abbeel · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson · 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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Multi-agent reinforcement learning as a rehearsal for decentralized planning
Landon Kraemer and Bikramjit Banerjee · 2016
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 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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Emergence of language with multi-agent games: Learning to communicate with sequences of symbols
Serhii Havrylov and Ivan Titov · 2017
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Revisiting the master-slave architecture in multi-agent deep reinforcement learning
Xiangyu Kong, Bo Xin, Fangchen Liu, and Yizhou Wang · 2017
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Starcraft II: A new challenge for reinforcement learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, and Rodney Tsing · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Chatbots and the new world of HCI
Asbjørn Følstad and Petter Bae Brandtzæg · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Stabilising experience replay for deep multi-agent reinforcement learning
Jakob N. Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, and Shimon Whiteson · 2017
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Counterfactual multi-agent policy gradients
Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
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Reinforcement learning: An introduction, 2nd Edition
Richard S Sutton and Andrew G Barto · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, and Thore Graepel · 2018
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QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, and Shimon Whiteson · 2018
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Learning attentional communication for multi-agent cooperation
Jiechuan Jiang and Zongqing Lu · 2018
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Deep multi-agent reinforcement learning with relevance graphs
Aleksandra Malysheva, Tegg Tae Kyong Sung, Chae-Bong Sohn, Daniel Kudenko, and Aleksei Shpilman · 2018
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Multi-agent deep reinforcement learning with extremely noisy observations
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Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, and Zinovi Rabinovich · 2020
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Akshat Agarwal, Sumit Kumar, Katia P. Sycara, and Michael Lewis · 2020
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Learning nearly decomposable value functions via communication minimization
Tonghan Wang, Jianhao Wang, Chongyi Zheng, and Chongjie Zhang · 2020
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Learning agent communication under limited bandwidth by message pruning
Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, and Yan Ni · 2020
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Learning structured communication for multi-agent reinforcement learning
Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, and Hongyuan Zha · 2020
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Ozsel Kilinc and Giovanni Montana · 2018
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Emergent communication through negotiation
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Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2018
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Thanh Thi Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi · 2018
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Multi-agent systems and blockchain: Results from a systematic literature review
Davide Calvaresi, Alevtina Dubovitskaya, Jean-Paul Calbimonte, Kuldar Taveter, and Michael Schumacher · 2018
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Emergence of communication in an interactive world with consistent speakers
Ben Bogin, Mor Geva, and Jonathan Berant · 2018
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Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
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Superhuman ai for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
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Communication learning via backpropagation in discrete channels with unknown noise
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Learning individually inferred communication for multi-agent cooperation
Ziluo Ding, Tiejun Huang, and Zongqing Lu · 2020
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Graph convolutional reinforcement learning
Jiechuan Jiang, Chen Dun, Tiejun Huang, and Zongqing Lu · 2020
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Multi-agent game abstraction via graph attention neural network
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Multi-agent reinforcement learning for networked system control
Tianshu Chu, Sandeep Chinchali, and Sachin Katti · 2020
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Intention propagation for multi-agent reinforcement learning
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Guangzheng Hu, Yuanheng Zhu, Dongbin Zhao, Mengchen Zhao, and Jianye Hao · 2020
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Sparse discrete communication learning for multi-agent cooperation through backpropagation
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Google research football: A novel reinforcement learning environment
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Multi-agent systems and complex networks: Review and applications in systems engineering
Manuel Herrera, Marco Pérez-Hernández, Ajith Kumar Parlikad, and Joaquín Izquierdo · 2020
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It takes a village: Integrating an adaptive chatbot into an online gaming community
Joseph Seering, Michal Luria, Connie Ye, Geoff Kaufman, and Jessica Hammer · 2020
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Robust multi-agent reinforcement learning with model uncertainty
Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, and Tamer Basar · 2020
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Multi-agent deep reinforcement learning: a survey
Sven Gronauer and Klaus Diepold · 2021
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DOP: off-policy multi-agent decomposed policy gradients
Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, and Chongjie Zhang · 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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Quasi-equivalence discovery for zero-shot emergent communication
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Emergent communication under competition
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Semantics-native communication with contextual reasoning
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Emergent discrete communication in semantic spaces
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Learning to ground multi-agent communication with autoencoders
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Mis-spoke or mis-lead: Achieving robustness in multi-agent communicative reinforcement learning
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Multiagent deep reinforcement learning: Challenges and directions towards human-like approaches
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Attention-based reinforcement learning for real-time UAV semantic communication
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Communication in multi-agent reinforcement learning: Intention sharing
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HAMMER: multi-level coordination of reinforcement learning agents via learned messaging
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Multi-agent graph-attention communication and teaming
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Multi-agent incentive communication via decentralized teammate modeling
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