Smoothing-Enabled Randomized Stochastic Gradient Schemes for Solving Nonconvex Nonsmooth Potential Games under Uncertainty
This paper proposes randomized stochastic gradient schemes with smoothing techniques to solve nonconvex nonsmooth stochastic potential games under uncertainty, achieving optimal sample complexity and asymptotic convergence without relying on stringent growth conditions or local convexity.