Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers
RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong performance is still achievable. Identifying such conditions and quantifying the resulting performance gap is intractable by enumeration, while the sequential and closed-loop nature of RL controllers makes formal verification methods impractical. We present ReGuard, a framework that discovers worst-case scenarios for a given RL controller and protects it against them at inference time without retraining. Discovery is formulated as a bilevel regret-maximization problem, wh...
| Authors | Hongyu Hè, Minhao Jin, Maria Apostolaki |
|---|---|
| Primary category | cs.NI |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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