Computer Science - Computer Science and Game Theory

On Separation Between Best-Iterate, Random-Iterate, and Last-Iterate Convergence of Learning in Games
Yang Cai, Gabriele Farian, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng
Last updated on Jun 6, 2025
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Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
Yang Cai, Gabriele Farian, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng
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On Tractable $\Phi$-Equilibria in Non-Concave Games
Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
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Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms
Yang Cai, Gabriele Farian, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng
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Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games
We study policy optimization algorithms for computing correlated equilibria in multi-player general-sum Markov Games. Previous results …
Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
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Learning Thresholds with Latent Values and Censored Feedback
In this paper, we investigate a problem of actively learning threshold in latent space, where the unknown reward $g(\gamma, v)$ …
(*) Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng, Xiaotie Deng
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Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
We revisit the problem of learning in two-player zero-sum Markov games, focusing on developing an algorithm that is uncoupled, …
Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
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Tight Last-Iterate Convergence of the Extragradient and the Optimistic Gradient Descent-Ascent Algorithm for Constrained Monotone Variational Inequalities
The monotone variational inequality is a central problem in mathematical programming that unifies and generalizes many important …
Yang Cai, Argyris Oikonomou, Weiqiang Zheng
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