  Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated
promising results on various challenging non-linear control tasks. While a
model-free DRL algorithm can solve unknown dynamics and high-dimensional
problems, it lacks safety assurance. Although safety constraints can be encoded
as part of a reward function, there still exists a large gap between an RL
controller trained with this modified reward and a safe controller. In
contrast, instead of implicitly encoding safety constraints with rewards, we
explicitly co-learn a Twin Neural Lyapunov Function (TNLF) with the control
policy in the DRL training loop and use the learned TNLF to build a runtime
monitor. Combined with the path generated from a planner, the monitor chooses
appropriate waypoints that guide the learned controller to provide
collision-free control trajectories. Our approach inherits the scalability
advantages from DRL while enhancing safety guarantees. Our experimental
evaluation demonstrates the effectiveness of our approach compared to DRL with
augmented rewards and constrained DRL methods over a range of high-dimensional
safety-sensitive navigation tasks.
