  This paper proposes a computational technique based on ``deep unfolding'' to
solving the finite-time maximum hands-off control problem for discrete-time
nonlinear stochastic systems. In particular, we seek a sparse control input
sequence that stabilizes the system such that the expected value of the square
of the final states is small by training a deep neural network. The proposed
technique is demonstrated by a numerical experiment.
