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We propose a framework based on Recurrent Neural Networks (RNNs) to determinean optimal control strategy for a discrete-time system that is required tosatisfy specifications given as Signal Temporal Logic (STL) formulae. RNNs canstore information of a system over time, thus, enable us to determinesatisfaction of the dynamic temporal requirements specified in STL formulae.Given a STL formula, a dataset of satisfying system executions andcorresponding control policies, we can use RNNs to predict a control policy ateach time based on the current and previous states of system. We use ControlBarrier Functions (CBFs) to guarantee the safety of the predicted controlpolicy. We validate our theoretical formulation and demonstrate its performancein an optimal control problem subject to partially unknown safety constraintsthrough simulations.

Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints