  We propose a framework based on Recurrent Neural Networks (RNNs) to determine
an optimal control strategy for a discrete-time system that is required to
satisfy specifications given as Signal Temporal Logic (STL) formulae. RNNs can
store information of a system over time, thus, enable us to determine
satisfaction of the dynamic temporal requirements specified in STL formulae.
Given a STL formula, a dataset of satisfying system executions and
corresponding control policies, we can use RNNs to predict a control policy at
each time based on the current and previous states of system. We use Control
Barrier Functions (CBFs) to guarantee the safety of the predicted control
policy. We validate our theoretical formulation and demonstrate its performance
in an optimal control problem subject to partially unknown safety constraints
through simulations.
