  We present a self-contained proof of the convergence rate of the Stochastic
Gradient Descent (SGD) when the learning rate follows an inverse time decays
schedule; we next apply the results to the convergence of a modified form of
policy gradient Multi-Armed Bandit (MAB) with $L2$ regularization.
