We propose the first 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：我们提出首个适用于无偏蒙特卡洛估计的正规博弈近似纳什均衡损失函数。该构造允许我们部署标准非凸随机优化技术来逼近纳什均衡，从而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，证明随机梯度下降法能够超越先前的最优方法。