  In this note we consider the problem of synthesizing optimal control policies
for a system from noisy datasets. We present a novel algorithm that takes as
input the available dataset and, based on these inputs, computes an optimal
policy for possibly stochastic and nonlinear systems that also satisfies
actuation constraints. The algorithm relies on solid theoretical foundations,
which have their key roots into a probabilistic interpretation of dynamical
systems. The effectiveness of our approach is illustrated by considering an
autonomous car use case. For such use case, we make use of our algorithm to
synthesize a control policy from noisy data allowing the car to merge onto an
intersection, while satisfying additional constraints on the variance of the
car speed
