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Centralized training with decentralized execution has become an important paradigm in multi-agent learning.
The StarCraft Multi-Agent Challenge
Samvelyan, M., Rashid, T., de Witt, C. S., Farquhar, G., Nardelli, N., Rudner, T. G. J., Hung, C.-M., Torr, P. H. S., Foerster, J., and Whiteson, S · 1902
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The fermion process—a model of stochastic point process with repulsive points
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Applied linear algebra , volume 3
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Q-learning
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Multi-agent reinforcement learning: independent versus cooperative agents
Tan, M · 1993
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Cooperative multi-agent learning: The state of the art
Panait, L. and Luke, S · 2005
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Matrix approximation and projective clustering via volume sampling
Deshpande, A., Rademacher, L., Vempala, S., and Wang, G · 2006
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Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams
Matignon, L., Laurent, G. J., and Le Fort-Piat, N · 2007
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Optimal and approximate q-value functions for decentralized pomdps
Oliehoek, F. A., Spaan, M. T., and Vlassis, N · 2008
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Actor-critic reinforcement learning with energy-based policies
Heess, N., Silver, D., and Teh, Y. W · 2012
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Determinantal point processes for machine learning
Kulesza, A., Taskar, B., et al · 2012
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Linear algebra and geometry
Shafarevich, I. R. and Remizov, A. O · 2012
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Learning the parameters of determinantal point process kernels
Affandi, R. H., Fox, E., Adams, R., and Taskar, B · 2014
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
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Fast mixing markov chains for strongly rayleigh measures, dpps, and constrained sampling
Li, C., Sra, S., and Jegelka, S · 2016
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A concise introduction to decentralized POMDPs , volume 1
Oliehoek, F. A., Amato, C., et al · 2016
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On the complexity of constrained determinantal point processes
Celis, L. E., Deshpande, A., Kathuria, T., Straszak, D., and Vishnoi, N. K · 2017
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Improving sequential determinantal point processes for supervised video summarization
Sharghi, A., Borji, A., Li, C., Yang, T., and Gong, B · 2018
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Multiagent soft q-learning
Wei, E., Wicke, D., Freelan, D., and Luke, S · 2018
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A study of ai population dynamics with million-agent reinforcement learning
Yang, Y., Yu, L., Bai, Y., Wen, Y., Zhang, W., and Wang, J · 2018
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Gdpp: Learning diverse generations using determinantal point processes
Elfeki, M., Couprie, C., Riviere, M., and Elhoseiny, M · 2019
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Efficient ridesharing order dispatching with mean field multi-agent reinforcement learning
Li, M., Qin, Z., Jiao, Y., Yang, Y., Wang, J., Wang, C., Wu, G., and Ye, J · 2019
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Maven: Multi-agent variational exploration
Mahajan, A., Rashid, T., Samvelyan, M., and Whiteson, S · 2019
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Peng, P., Wen, Y., Yang, Y., Yuan, Q., Tang, Z., Long, H., and Wang, J · 2017
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Value-decomposition networks for cooperative multi-agent learning
Sunehag, P., Lever, G., Gruslys, A., Czarnecki, W. M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J. Z., Tuyls, K., et al · 2017
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Fair and diverse dpp-based data summarization
Celis, L. E., Keswani, V., Straszak, D., Deshpande, A., Kathuria, T., and Vishnoi, N. K · 2018
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Fast greedy map inference for determinantal point process to improve recommendation diversity
Chen, L., Zhang, G., and Zhou, E · 2018
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Counterfactual multi-agent policy gradients
Foerster, J. N., Farquhar, G., Afouras, T., Nardelli, N., and Whiteson, S · 2018
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Qmix: monotonic value function factorisation for deep multi-agent reinforcement learning
Rashid, T., Samvelyan, M., De Witt, C. S., Farquhar, G., Foerster, J., and Whiteson, S · 2018
Cited alongside, same era.
The starcraft multi-agent challenge
Samvelyan, M., Rashid, T., Schroeder de Witt, C., Farquhar, G., Nardelli, N., Rudner, T. G., Hung, C.-M., Torr, P. H., Foerster, J., and Whiteson, S
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Determinantal reinforcement learning
Osogami, T. and Raymond, R · 2019
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Improving adversarial robustness via promoting ensemble diversity
Pang, T., Xu, K., Du, C., Chen, N., and Zhu, J · 2019
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Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Son, K., Kim, D., Kang, W. J., Hostallero, D. E., and Yi, Y · 2019
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Alpha-alpha-rank: Scalable multi-agent evaluation through evolution
Yang, Y., Tutunov, R., Sakulwongtana, P., Ammar, H. B., and Wang, J · 2019
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Factorized q-learning for large-scale multi-agent systems
Zhou, M., Chen, Y., Wen, Y., Yang, Y., Su, Y., Zhang, W., Zhang, D., and Wang, J · 2019
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