AI RESEARCH
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
arXiv CS.LG
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ArXi:2605.12561v1 Announce Type: new Safe reinforcement learning (RL) typically asks $\textit{what}$ an agent should do. We ask $\textit{when}$ it needs to act, and show that a single policy can jointly learn control inputs and communication-efficient timing decisions under a pointwise Lyapuno safety shield. We focus on stabilization around a known equilibrium, where CARE-based LQR backups, Lyapuno certificates, and classical Lyapuno-STC are well defined, enabling clean comparison against analytical baselines.