  We consider a stochastic generalized Nash equilibrium problem (GNEP) with
expected-value cost functions. Inspired by Yi and Pavel (Automatica, 2019), we
propose a distributed GNE seeking algorithm by exploiting the forward-backward
operator splitting and a suitable preconditioning matrix. Specifically, we
apply this method to the stochastic GNEP, where, at each iteration, the
expected value of the pseudo-gradient is approximated via a number of random
samples. Our main contribution is to show almost sure convergence of our
proposed algorithm if the sample size grows large enough.
