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Mean-Field Control (MFC) has recently been proven to be a scalable tool to approximately solve large-scale multi-agent reinforcement learning (MARL) problems.
Q-learning
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Ming Tan · 1993
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On-line Q-learning using connectionist systems , volume 37
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Stationary deterministic policies for constrained mdps with multiple rewards, costs, and discount factors
Dmitri A Dolgov and Edmund H Durfee · 2005
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Chiara Buratti, Andrea Conti, Davide Dardari, and Roberto Verdone · 2009
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An online actor–critic algorithm with function approximation for constrained markov decision processes
Shalabh Bhatnagar and K Lakshmanan · 2012
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Joint latency and cost optimization for erasure coded data center storage
Yu Xiang, Tian Lan, Vaneet Aggarwal, and Yih Farn R Chen · 2014
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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 Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Optimal resource allocation for competitive spreading processes on bilayer networks
Nicholas J Watkins, Cameron Nowzari, Victor M Preciado, and George J Pappas · 2016
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Risk-constrained reinforcement learning with percentile risk criteria
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Probabilistic Theory of Mean Field Games with Applications I-II
René Carmona, François Delarue, et al · 2018
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Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
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Controlling propagation of epidemics via mean-field control
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René Carmona, Mathieu Laurière, and Zongjun Tan · 2019
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Constrained reinforcement learning has zero duality gap
Santiago Paternain, Luiz Chamon, Miguel Calvo-Fullana, and Alejandro Ribeiro · 2019
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Last-iterate convergence of general parameterized policies in constrained mdps
Washim Uddin Mondal and Vaneet Aggarwal
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Unified reinforcement q-learning for mean field game and control problems
Andrea Angiuli, Jean-Pierre Fouque, and Mathieu Laurière · 2022
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