ReGuard Framework Protects RL Network Controllers from Worst-Case Failures
Researchers have introduced ReGuard, a framework designed to identify worst-case scenarios for network controllers that utilize reinforcement learning (RL) and to safeguard them during inference without the need for retraining. While RL controllers generally perform well in tasks such as adaptive bitrate streaming and congestion control, they can experience severe failures under specific circumstances. ReGuard addresses this by framing the discovery process as a bilevel regret-maximization challenge, ensuring a certified lower limit on the worst-case performance gap. It generates lightweight logic rules from counterfactual trajectories that activate only when a hazardous state is recognized, preserving the controller's standard operations. This method circumvents the challenges of enumeration and the difficulties associated with formal verification in sequential, closed-loop RL systems.
Key facts
- RL-based controllers achieve strong average-case performance in networking tasks
- Performance can degrade severely under certain network conditions
- Identifying worst-case conditions by enumeration is intractable
- Formal verification methods are impractical for sequential, closed-loop RL controllers
- ReGuard discovers worst-case scenarios for a given RL controller
- Discovery is formulated as a bilevel regret-maximization problem
- ReGuard yields a certified lower bound on the worst-case performance gap
- Discovered trajectories are compiled into lightweight logic rules for intervention
Entities
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