Context-dependent agent evaluation with orthogonal equilibrium learning
NashEval is proposed, a general framework for robust contextual equilibrium learning that frame evaluation as a contextual game between two players, each selecting a distribution over agents as the strategy to receive greater collective preference than the other.
Hao-Rui Ma, Ze-Hua Zang, Jiang-Meng Li et al.
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