  We consider a class of stochastic optimal control problems for discrete-time
stochastic linear systems which seek for control policies that will steer the
probability distribution of the terminal state of the system close to a desired
Gaussian distribution. In our problem formulation, the closeness between the
terminal state distribution and the desired (goal) distribution is measured in
terms of the squared Wasserstein distance which is associated with a
corresponding terminal cost term. We recast the stochastic optimal control
problem as a finite-dimensional nonlinear program and we show that its
performance index can be expressed as the difference of two convex functions.
This representation of the performance index allows us to find local minimizers
of the original nonlinear program via the so-called convex-concave procedure
[1]. Subsequently, we consider a similar problem but this time we use a
terminal cost that corresponds to the KL divergence. Finally, we present
non-trivial numerical simulations to demonstrate the proposed techniques and
compare them in terms of computation time.
