# Canopy: Property-Driven Learning for Congestion Control

arXiv: https://arxiv.org/abs/2412.10915 · HTML: https://mlsanity.com/paper/2412.10915/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models](https://mlsanity.com/paper/1810.12715/) (2018)
- [Batch Policy Learning under Constraints](https://mlsanity.com/paper/1903.08738/) (2019)
- [Towards Stable and Efficient Training of Verifiably Robust Neural Networks](https://mlsanity.com/paper/1906.06316/) (2019)
- [A Composable Specification Language for Reinforcement Learning Tasks](https://mlsanity.com/paper/2008.09293/) (2020)
- [Learning Memory Access Patterns](https://mlsanity.com/paper/1803.02329/) (2018)
- [Safe Neurosymbolic Learning with Differentiable Symbolic Execution](https://mlsanity.com/paper/2203.07671/) (2022)
- [Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation](https://mlsanity.com/paper/2301.11374/) (2023)

## Built on by (most cited first, 2 of 2)

- [TuxBot: Semantic-Aware Online OS Tuning with Large Language Models](https://mlsanity.com/paper/2605.15026/) (2026)
- [CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers](https://mlsanity.com/paper/2605.21915/) (2026)

## Abstract (arXiv)

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.