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Learning to generate Reliable Broadcast Algorithms

arXiv 2208.00525 ↗First posted 31 Jul 2022cs.DC cs.DS cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 105 references that are in the map. First posted on arXiv 31 Jul 2022.

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15 of 105 references.

References · 15

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 342
  4. 2017A Survey of Machine Learning for Big Code and Naturalnesscited by 220
  5. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  6. 2016Playing FPS Games with Deep Reinforcement Learningcited by 95
  7. 2020Deep Learning for Source Code Modeling and Generation: Models, Applications and Challengescited by 55
  8. 2021Generating GPU Compiler Heuristics using Reinforcement Learningcited by 3
  9. 2019Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specificationscited by 2
  10. 2021On Symmetry and Quantification: A New Approach to Verify Distributed Protocolscited by 2
  11. 2014Model Checking Paxos in Spincited by 1
  12. 2015Trading off t-Resilience for Efficiency in Asynchronous Byzantine Reliable Broadcastcited by 1
3 more
  1. 2021Practical Byzantine Reliable Broadcast on Partially Connected Networks (Extended version)cited by 1
  2. 2021Towards an Automatic Proof of Lamport's Paxoscited by 1
  3. 2022Reaching Consensus in the Byzantine Empire: A Comprehensive Review of BFT Consensus Algorithmscited by 1

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