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We propose the first, to our knowledge, loss function for approximate Nashequilibria of normal-form games that is amenable to unbiased Monte Carloestimation. This construction allows us to deploy standard non-convexstochastic optimization techniques for approximating Nash equilibria, resultingin novel algorithms with provable guarantees. We complement our theoreticalanalysis with experiments demonstrating that stochastic gradient descent canoutperform previous state-of-the-art approaches.

Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization