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Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent.
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Lianmin Zheng, Jiacheng Yang, Han Cai, Ming Zhou, Weinan Zhang, Jun Wang, and Yong Yu. 2018 · 2018
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A survey and critique of multiagent deep reinforcement learning
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor. 2019 · 2019
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