Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance

This paper introduces a communication-efficient reinforcement learning approach where a single policy learns both control inputs and timing decisions, secured by a pointwise Lyapunov safety shield. A run-time assurance layer overrides the policy to provide strictly stronger safety guarantees and achieve significantly higher mean inter-sample intervals on various systems.