Learning Distributed Protocols with Zero Knowledge

NeurIPS 2023 Workshop MLSys Submission9 Authors

Published: 28 Oct 2023, Last Modified: 12 Dec 2023MlSys Workshop NeurIPS 2023 PosterEveryoneRevisionsBibTeX
Keywords: machine learning, system, reinforcement learning, fault tolerance
Abstract: The success of AlphaGo Zero shows that a computer can learn to play a complicated board game without relying on the knowledge from human players. We observe that designing a distributed protocol is similar to playing board games to some extent: when determining the next action to take, they both want to ensure they can win even when a smart opponent tries to drive the game/protocol to the worst case. In this work, we explore whether we can apply similar techniques to learn a distributed protocol with zero knowledge. Towards this goal, we model the process in a distributed protocol as a state machine, and further rely on model checking to validate the correctness of the learned state machine. With this approach, we successfully learned a correct atomic commit protocol with three processes, and upon that, we further discuss future work.
Submission Number: 9