  In this paper, we develop safe reinforcement-learning-based controllers for
systems tasked with accomplishing complex missions that can be expressed as
linear temporal logic specifications, similar to those required by
search-and-rescue missions. We decompose the original mission into a sequence
of tracking sub-problems under safety constraints. We impose the safety
conditions by utilizing barrier functions to map the constrained optimal
tracking problem in the physical space to an unconstrained one in the
transformed space. Furthermore, we develop policies that intermittently update
the control signal to solve the tracking sub-problems with reduced burden in
the communication and computation resources. Subsequently, an actor-critic
algorithm is utilized to solve the underlying Hamilton-Jacobi-Bellman
equations. Finally, we support our proposed framework with stability proofs and
showcase its efficacy via simulation results.
