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Effective communication in Multi-Agent Reinforcement Learning (MARL) can significantly enhance coordination and collaborative performance in complex and partially observable environments.
A review of cooperative multi-agent deep reinforcement learning, 2021
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Graph neural networks for decentralized multi-robot path planning, 2020
Q. Li, F. Gama, A. Ribeiro, and A. Prorok · 1912
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On the robustness of cooperative multi-agent reinforcement learning, 2020
J. Lin, K. Dzeparoska, S. Q. Zhang, A. Leon-Garcia, and N. Papernot · 2003
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The emergence of adversarial communication in multi-agent reinforcement learning, 2020
J. Blumenkamp and A. Prorok · 2008
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Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences, 2020
R. Köster, K. R. McKee, R. Everett, L. Weidinger, W. S. Isaac, E. Hughes, E. A. Duéñez-Guzmán, T. Graepel, M. Botvinick, and J. Z. Leibo · 2010
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Learning multiagent communication with backpropagation
S. Sukhbaatar, A. Szlam, and R. Fergus · 2016
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Value-decomposition networks for cooperative multi-agent learning, 2017
P. Sunehag, G. Lever, A. Gruslys, W. M. Czarnecki, V. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls, and T. Graepel · 2017
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Counterfactual multi-agent policy gradients
J. Foerster, G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson · 2018
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Liir: Learning individual intrinsic reward in multi-agent reinforcement learning
Y. Du, L. Han, M. Fang, J. Liu, T. Dai, and D. Tao · 2019
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
N. Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. Ortega, D. Strouse, J. Z. Leibo, and N. De Freitas · 2019
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Reward redistribution mechanisms in multi-agent reinforcement learning
A. Ibrahim, A. Jitani, D. Piracha, and D. Precup · 2020
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Adversarial attacks in consensus-based multi-agent reinforcement learning, 2021
M. Figura, K. C. Kosaraju, and V. Gupta · 2021
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Learning to ground multi-agent communication with autoencoders
T. Lin, J. Huh, C. Stauffer, S. N. Lim, and P. Isola · 2021
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Multi-agent adversarial attacks for multi-channel communications, 2022
J. Dong, S. Wu, M. Sultani, and V. Tarokh · 2022
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J. Guo, Y. Chen, Y. Hao, Z. Yin, Y. Yu, and S. Li · 2022
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Sparse adversarial attack in multi-agent reinforcement learning, 2022
Y. Hu and Z. Zhang · 2022
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A theory of mind approach as test-time mitigation against emergent adversarial communication, 2023
N. Piazza and V. Behzadan · 2023
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Deconstructing cooperation and ostracism via multi-agent reinforcement learning, 2023
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J. Tu, T. Wang, J. Wang, S. Manivasagam, M. Ren, and R. Urtasun · 2021
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Fop: Factorizing optimal joint policy of maximum-entropy multi-agent reinforcement learning
T. Zhang, Y. Li, C. Wang, G. Xie, and Z. Lu · 2021
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Learning when to communicate at scale in multiagent cooperative and competitive tasks
A. Singh, T. Jain, and S. Sukhbaatar
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Learning when to communicate at scale in multiagent cooperative and competitive tasks, 2018b
A. Singh, T. Jain, and S. Sukhbaatar
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A. Ueshima, S. Omidshafiei, and H. Shirado · 2023
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The benefits of power regularization in cooperative reinforcement learning, 2024
M. Li and M. Dennis · 2024
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Attacking cooperative multi-agent reinforcement learning by adversarial minority influence, 2024
S. Li, J. Guo, J. Xiu, Y. Zheng, P. Feng, X. Yu, A. Liu, Y. Yang, B. An, W. Wu, and X. Liu · 2024
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