# Learning to generate Reliable Broadcast Algorithms

arXiv: https://arxiv.org/abs/2208.00525 · HTML: https://mlsanity.com/paper/2208.00525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 105 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://mlsanity.com/paper/2207.01780/) (2022)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [Playing FPS Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05521/) (2016)
- [Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges](https://mlsanity.com/paper/2002.05442/) (2020)
- [Generating GPU Compiler Heuristics using Reinforcement Learning](https://mlsanity.com/paper/2111.12055/) (2021)
- [Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications](https://mlsanity.com/paper/1911.05403/) (2019)
- [On Symmetry and Quantification: A New Approach to Verify Distributed Protocols](https://mlsanity.com/paper/2103.14831/) (2021)
- [Model Checking Paxos in Spin](https://mlsanity.com/paper/1408.5962/) (2014)
- [Trading off t-Resilience for Efficiency in Asynchronous Byzantine Reliable Broadcast](https://mlsanity.com/paper/1510.06882/) (2015)
- [Practical Byzantine Reliable Broadcast on Partially Connected Networks (Extended version)](https://mlsanity.com/paper/2104.03673/) (2021)
- [Towards an Automatic Proof of Lamport's Paxos](https://mlsanity.com/paper/2108.08796/) (2021)
- [Reaching Consensus in the Byzantine Empire: A Comprehensive Review of BFT Consensus Algorithms](https://mlsanity.com/paper/2204.03181/) (2022)

## Abstract (arXiv)

Modern distributed systems are supported by fault-tolerant algorithms, like Reliable Broadcast and Consensus, that assure the correct operation of the system even when some of the nodes of the system fail. However, the development of distributed algorithms is a manual and complex process, resulting in scientific papers that usually present a single algorithm or variations of existing ones. To automate the process of developing such algorithms, this work presents an intelligent agent that uses Reinforcement Learning to generate correct and efficient fault-tolerant distributed algorithms. We show that our approach is able to generate correct fault-tolerant Reliable Broadcast algorithms with the same performance of others available in the literature, in only 12,000 learning episodes.