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cs.GT2025
Efficient Last-Iterate Convergence in Regret Minimization via Adaptive Reward Transformation
Hang Ren, Yulin Wu, Shuhan Qi +4
Regret minimization is a powerful method for finding Nash equilibria in Normal-Form Games (NFGs) and Extensive-Form Games (EFGs), but it typically guarantees convergence only for t…
cs.GT2020★ 8 cited
Solving imperfect-information games via exponential counterfactual regret minimization
Huale Li, Xuan Wang, Shuhan Qi +4
In general, two-agent decision-making problems can be modeled as a two-player game, and a typical solution is to find a Nash equilibrium in such game. Counterfactual regret minimiz…