AI RESEARCH
Fairness in two-player zero-sum games with bandit feedback
arXiv CS.LG
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ArXi:2606.01159v1 Announce Type: new We study two-player zero-sum games (TPZSGs) with bandit feedback under fairness constraints requiring every action to be played with probability at least $\alpha/m$. Existing instance-dependent results target $\textit{pure}$ Nash equilibria, while fairness generically produces $\textit{mixed}$ equilibria, a harder learning target.