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Mean Field Control (MFC) is a powerful approximation tool to solve large-scale Multi-Agent Reinforcement Learning (MARL) problems.
Q-learning
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Learning deep mean field games for modeling large population behavior
Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, and Hongyuan Zha · 2017
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
Abubakr O Al-Abbasi, Arnob Ghosh, and Vaneet Aggarwal · 2019
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Graphon mean field games and the gmfg equations: ε \varepsilon -nash equilibria
Peter E Caines and Minyi Huang · 2019
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Model-free mean-field reinforcement learning: mean-field mdp and mean-field q-learning
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Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
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Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
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Large-scale traffic signal control using a novel multiagent reinforcement learning
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On the theory of policy gradient methods: Optimality, approximation, and distribution shift
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Mean-field controls with Q-learning for cooperative MARL: convergence and complexity analysis
Haotian Gu, Xin Guo, Xiaoli Wei, and Renyuan Xu · 2021
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On the convergence of model free learning in mean field games
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On the approximation of cooperative heterogeneous multi-agent reinforcement learning (marl) using mean field control (mfc)
Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, and Satish V Ukkusuri
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Can mean field control (mfc) approximate cooperative multi agent reinforcement learning (marl) with non-uniform interaction?
Washim Uddin Mondal, Vaneet Aggarwal, and Satish Ukkusuri
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Multi-agent reinforcement learning in stochastic networked systems
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Mean-field markov decision processes with common noise and open-loop controls
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