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Mirror play (MP) is a well-accepted primal-dual multi-agent learning algorithm where all agents simultaneously implement mirror descent in a distributed fashion.
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Y. Pan, T. Li, and Q. Zhu, “Is stochastic mirror descent vulnerable to adversarial delay attacks? a traffic assignment resilience study,” in 2023 62nd IEEE Conference on Decision and Control (CDC)
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P. Mertikopoulos and Z. Zhou, “Learning in games with continuous action sets and unknown payoff functions,” Mathematical Programming
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B. Gao and L. Pavel, “Continuous-time discounted mirror descent dynamics in monotone concave games,” IEEE Transactions on Automatic Control
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B. Tzen, A. Raj, M. Raginsky, and F. Bach, “Variational principles for mirror descent and mirror langevin dynamics,” IEEE Control Systems Letters
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S. Liu, T. Li, and Q. Zhu, “Game-Theoretic Distributed Empirical Risk Minimization With Strategic Network Design,” IEEE Transactions on Signal and Information Processing over Networks
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