让自学习拥塞控制既智能又可靠，通过形式化验证优化训练过程。
Canopy: Property-Driven Learning for Congestion Control
- 用量化认证和抽象解释引导学习，实现可验证的性能优化。
- 在多种网络条件下，训练出的控制器兼具适应性与最坏情况可靠性。
- 适合关注智能算法安全性的网络系统研究者和工程师。
基于学习的拥塞控制相比传统启发式方法具有更强的适应性。然而，学习技术的不可靠性可能导致学习型控制器表现不佳，因此需要形式化保证。尽管已有方法可用于形式化验证学习型拥塞控制器，但这些方法仅提供二元反馈，无法指导控制器向更优行为优化。我们提出 Canopy，一种将学习与形式化推理融合的新框架，通过新颖的定量认证与抽象解释器，在学习循环中引导训练过程，奖励表现稳健且安全的模型，并评估其在最坏输入下的性能。实验表明，与现有最优学习型控制器不同，Canopy 训练的控制器在多种网络条件下同时具备适应性与最坏情况可靠性。
原文摘要 · Abstract (English)
Learning-based congestion controllers offer better adaptability compared to traditional heuristics. However, the unreliability of learning techniques can cause learning-based controllers to behave poorly, creating a need for formal guarantees. While methods for formally verifying learned congestion controllers exist, these methods offer binary feedback that cannot optimize the controller toward better behavior. We improve this state-of-the-art via Canopy, a new property-driven framework that integrates learning with formal reasoning in the learning loop. Canopy uses novel quantitative certification with an abstract interpreter to guide the training process, rewarding models, and evaluating robust and safe model performance on worst-case inputs. Our evaluation demonstrates that unlike state-of-the-art learned controllers, Canopy-trained controllers provide both adaptability and worst-case reliability across a range of network conditions.
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