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We propose a policy search approach to learn controllers from specificationsgiven as Signal Temporal Logic (STL) formulae. The system model, which isunknown but assumed to be an affine control system, is learned together withthe control policy. The model is implemented as two feedforward neural networks(FNNs) - one for the drift, and one for the control directions. To capture thehistory dependency of STL specifications, we use a recurrent neural network(RNN) to implement the control policy. In contrast to prevalent model-freemethods, the learning approach proposed here takes advantage of the learnedmodel and is more efficient. We use control barrier functions (CBFs) with thelearned model to improve the safety of the system. We validate our algorithmvia simulations and experiments. The results show that our approach can satisfythe given specification within very few system runs, and can be used foron-line control.

Safe Model-based Control from Signal Temporal Logic Specifications Using Recurrent Neural Networks