We propose the first, to our knowledge, loss function for approximate Nash equilibria of normal-form games that is amenable to unbiased Monte Carlo estimation. This construction allows us to deploy standard non-convex stochastic optimization techniques for approximating Nash equilibria, resulting in novel algorithms with provable guarantees. We complement our theoretical analysis with experiments demonstrating that stochastic gradient descent can outperform previous state-of-the-art approaches.

翻译：我们首次提出一种适用于无偏蒙特卡洛估计的标准式博弈近似纳什均衡损失函数。该构造使我们能够部署标准的非凸随机优化技术来逼近纳什均衡，并由此得到具有可证明保证的新算法。我们通过实验补充理论分析，展示随机梯度下降法能够超越先前最先进的方法。