  We propose a policy search approach to learn controllers from specifications
given as Signal Temporal Logic (STL) formulae. The system model is unknown, and
it is learned together with the control policy. The model is implemented as a
feedforward neural network (FNN). To capture the history dependency of the STL
specification, we use a recurrent neural network (RNN) to implement the control
policy. In contrast to prevalent model-free methods, the learning approach
proposed here takes advantage of the learned model and is more efficient. We
use control barrier functions (CBFs) with the learned model to improve the
safety of the system. We validate our algorithm via simulations. The results
show that our approach can satisfy the given specification within very few
system runs, and therefore it has the potential to be used for on-line control.
