Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
This paper introduces a safe reinforcement learning framework that focuses on 'when' an agent should act rather than 'what' it should do. By combining a run-time assurance (RTA) layer with Lyapunov stability theory, the agent learns adaptive timing decisions that maintain safety while drastically reducing communication frequency. Experiments on an inverted pendulum, cart-pole, and planar quadrotor show mean inter-sample interval (MSI) improvements of 1.45× to 3.51× over a Lyapunov-triggered baseline. A fixed LQR controller at the same average rate is unstable, highlighting that adaptive timing, not a lower rate, enables safe sparsity. A preference-conditioned extension recovers the full tradeoff frontier at 2/11 of training cost, and the method shows robustness to mass variations and disturbances.
[2605.12561] Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
[Submitted on 11 May 2026]
Title:Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
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Abstract: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 Lyapunov safety shield. We focus on stabilization around a known equilibrium, where CARE-based LQR backups, Lyapunov certificates, and classical Lyapunov-STC are well defined, enabling clean comparison against analytical baselines. A run-time assurance (RTA) layer overrides the policy via a one-step-ahead Lyapunov prediction and a precomputed LQR backup, providing a strictly stronger guarantee than constrained MDP methods that enforce safety only in expectation. On an inverted pendulum, cart--pole, and planar quadrotor, the learned policy achieves $1.91\times$, $1.45\times$, and $3.51\times$ higher mean inter-sample interval (MSI) than a Lyapunov-triggered baseline; a fixed LQR controller at the same average rate is unstable on all three plants, showing that adaptive timing, not a lower average rate, makes sparsity safe. A CARE-derived Lyapunov reward transfers across environments without redesign, with a single weight $w_c$ controlling the stability--communication tradeoff; ablations confirm the RTA shield is essential, with its removal reducing MSI by $1.27$--$1.84\times$ and degrading state norms. A preference-conditioned extension recovers the full tradeoff frontier from one model at $\tfrac{2}{11}$ of training compute, and SAC experiments show the results are algorithm-agnostic across discrete and continuous domains. A 12-state 3D quadrotor case study extends the framework to higher-dimensional systems where classical STC is intractable, and robustness to $\pm30\%$ mass variation and disturbances shows graceful degradation, with the RTA absorbing what the learned policy cannot.
Comments: 27 pages, 6 figures
Subjects:
Machine Learning (cs.LG); Robotics (cs.RO)
Cite as: arXiv:2605.12561 [cs.LG]
(or arXiv:2605.12561v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.12561
arXiv-issued DOI via DataCite
Submission history
From: Adam Haroon [view email] [v1] Mon, 11 May 2026 23:55:15 UTC (6,405 KB)
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