  Guided policy search algorithms have been proven to work with incredible
accuracy for not only controlling a complicated dynamical system, but also
learning optimal policies from various unseen instances. One assumes true
nature of the states in almost all of the well known policy search and learning
algorithms. This paper deals with a trajectory optimization procedure for an
unknown dynamical system subject to measurement noise using expectation
maximization and extends it to learning (optimal) policies which have less
noise because of lower variance in the optimal trajectories. Theoretical and
empirical evidence of learnt optimal policies of the new approach is depicted
in comparison to some well known baselines which are evaluated on an autonomous
system with widely used performance metrics.
