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Communication is crucial for solving cooperative Multi-Agent Reinforcement Learning tasks in partially observable Markov Decision Processes.
Learning nearly decomposable value functions via communication minimization
Wang, T.; Wang, J.; Zheng, C.; and Zhang, C. 2019 · 1910
Earlier work this paper cites.
The complexity of decentralized control of Markov decision processes
Bernstein, D. S.; Givan, R.; Immerman, N.; and Zilberstein, S. 2002 · 2002
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Emergent Specialization in Swarm Systems
Li, L.; Martinoli, A.; and Abu-Mostafa, Y. S. 2002 · 2002
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Emergent multi-agent communication in the deep learning era
Lazaridou, A.; and Baroni, M. 2020 · 2006
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Document clustering using concept space and cosine similarity measurement
Muflikhah, L.; and Baharudin, B. 2009 · 2009
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An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination
Cao, Y.; Yu, W.; Ren, W.; and Chen, G. 2013 · 2013
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Deep Embedding Network for Clustering
Huang, P.; Huang, Y.; Wang, W.; and Wang, L. 2014 · 2014
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Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Foerster, J. N.; Assael, Y. M.; de Freitas, N.; and Whiteson, S. 2016 · 2016
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Hoffman, M. D.; and Johnson, M. J. 2016 · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E.; Gu, S.; and Poole, B. 2016 · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J.; Mnih, A.; and Teh, Y. W. 2016 · 2016
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Learning Multiagent Communication with Backpropagation
Sukhbaatar, S.; Szlam, A.; and Fergus, R. 2016 · 2016
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Fast inverse-free sparse Bayesian learning via relaxed evidence lower bound maximization
Duan, H.; Yang, L.; Fang, J.; and Li, H. 2017 · 2017
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Emergence of language with multi-agent games: Learning to communicate with sequences of symbols
Havrylov, S.; and Titov, I. 2017 · 2017
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Learning discrete representations via information maximizing self-augmented training
Hu, W.; Miyato, T.; Tokui, S.; Matsumoto, E.; and Sugiyama, M. 2017 · 2017
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Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Singh, A.; Jain, T.; and Sukhbaatar, S. 2018 · 2018
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Sparse discrete communication learning for multi-agent cooperation through backpropagation
Freed, B.; James, R.; Sartoretti, G.; and Choset, H. 2020 · 2020
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Learning Multi-Agent Communication through Structured Attentive Reasoning
Rangwala, M.; and Williams, R. 2020 · 2020
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Dop: Off-policy multi-agent decomposed policy gradients
Wang, Y.; Han, B.; Wang, T.; Dong, H.; and Zhang, C. 2020 · 2020
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Emergent discrete communication in semantic spaces
Tucker, M.; Li, H.; Agrawal, S.; Hughes, D.; Sycara, K.; Lewis, M.; and Shah, J. A. 2021 · 2021
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Fop: Factorizing optimal joint policy of maximum-entropy multi-agent reinforcement learning
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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Lowe, R.; Wu, Y.; Tamar, A.; Harb, J.; Abbeel, P.; and Mordatch, I. 2017 · 2017
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Emergence of Grounded Compositional Language in Multi-Agent Populations
Mordatch, I.; and Abbeel, P. 2017 · 2017
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TarMAC: Targeted Multi-Agent Communication
Das, A.; Gervet, T.; Romoff, J.; Batra, D.; Parikh, D.; Rabbat, M.; and Pineau, J. 2018 · 2018
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Learning Attentional Communication for Multi-Agent Cooperation
Jiang, J.; and Lu, Z. 2018 · 2018
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Rashid, T.; Samvelyan, M.; Schroeder, C.; Farquhar, G.; Foerster, J.; and Whiteson, S. 2018 · 2018
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Zhang, T.; Li, Y.; Wang, C.; Xie, G.; and Lu, Z. 2021 · 2021
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Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning
Li, S.; Zhou, Y.; Allen, R.; and Kochenderfer, M. J. 2022 · 2022
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Trading off utility, informativeness, and complexity in emergent communication
Tucker, M.; Levy, R.; Shah, J. A.; and Zaslavsky, N. 2022 · 2022
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RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning
Chen, J.; Lan, T.; and Joe-Wong, C. 2023 · 2023
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