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Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning.
1902
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1905
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2013
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2013
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Human-level control through deep reinforcement learning,
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis, · 2015
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2015
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2016
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Learning to communicate with deep multi-agent reinforcement learning,
J. N. Foerster, Y. M. Assael, N. de Freitas, S. Whiteson, · 2016
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J. L. Ba, J. R. Kiros, G. E. Hinton, Layer normalization, 2016. arXiv:1607.06450
2016
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2017
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Cooperative multi-agent control using deep reinforcement learning,
J. K. Gupta, M. Egorov, M. Kochenderfer, · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments,
R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, I. Mordatch, · 2017
Cited alongside, same era.
Attention is all you need,
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, u. Kaiser, I. Polosukhin, · 2017
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E. Jang, S. Gu, B. Poole, Categorical reparameterization with gumbel-softmax, 2017. arXiv:1611.01144
2017
Cited alongside, same era.
Addressing function approximation error in actor-critic methods,
S. Fujimoto, H. van Hoof, D. Meger, · 2018
Cited alongside, same era.
A survey and critique of multiagent deep reinforcement learning,
P. Hernandez-Leal, B. Kartal, M. E. Taylor, · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning,
2019
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Individualized controlled continuous communication model for multiagent cooperative and competitive tasks,
A. Singh, T. Jain, S. Sukhbaatar, · 2019
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Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning,
T. Rashid, M. Samvelyan, C. S. de Witt, G. Farquhar, J. Foerster, S. Whiteson, · 2020
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UAV-Enabled Secure Communications by Multi-Agent Deep Reinforcement Learning,
Y. Zhang, Z. Mou, F. Gao, J. Jiang, R. Ding, Z. Han, · 2020
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Distributed Non-Communicating Multi-Robot Collision Avoidance via Map-Based Deep Reinforcement Learning,
G. Chen, S. Yao, J. Ma, L. Pan, Y. Chen, P. Xu, J. Ji, X. Chen, · 2020
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O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, J. Oh, D. Horgan, M. Kroiss, I. Danihelka, A. Huang, L. Sifre, T. Cai, J. P. Agapiou, M. Jaderberg, A. S. Vezhnevets, R. Leblond, T. Pohlen, V. Dalibard, D. Budden, Y. Sulsky, J. Molloy, T. L. Paine, C. Gulcehre, Z. Wang, T. Pfaff, Y. Wu, R. Ring, D. Yogatama, D. Wunsch, K. McKinney, O. Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, D. Silver, · 2019
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The StarCraft Multi-Agent Challenge,
M. Samvelyan, T. Rashid, C. S. de Witt, G. Farquhar, N. Nardelli, T. G. J. Rudner, C.-M. Hung, P. H. S. Torr, J. Foerster, S. Whiteson, · 2019
Cited alongside, same era.
Multiagent Reinforcement Learning for Swarm Confrontation Environments,
G. Zhang, Y. Li, X. Xu, H. Dai, · 2019
Cited alongside, same era.
An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach,
J. Chen, W. Hu, D. Cao, B. Zhang, Q. Huang, Z. Chen, F. Blaabjerg, · 2019
Cited alongside, same era.
A data-driven approach for designing STATCOM additional damping controller for wind farms,
G. Zhang, W. Hu, D. Cao, J. Yi, Q. Huang, Z. Liu, Z. Chen, F. Blaabjerg, · 2019
Cited alongside, same era.
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG,
H. Mao, Z. Zhang, Z. Xiao, Z. Gong, · 2019
Cited alongside, same era.
Multi-Robot Flocking Control Based on Deep Reinforcement Learning,
P. Zhu, W. Dai, W. Yao, J. Ma, Z. Zeng, H. Lu, · 2020
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Learning multi-agent communication with double attentional deep reinforcement learning,
H. Mao, Z. Zhang, Z. Xiao, Z. Gong, Y. Ni, · 2020
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A TD3-based multi-agent deep reinforcement learning method in mixed cooperation-competition environment,
F. Zhang, J. Li, Z. Li, · 2020
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Reward is enough,
D. Silver, S. Singh, D. Precup, R. S. Sutton, · 2021
Closest in time.
2021
Closest in time.