论文标题
学会生成可靠的广播算法
Learning to generate Reliable Broadcast Algorithms
论文作者
论文摘要
现代分布式系统由易于故障的算法（如可靠的广播和共识）提供支持，即使系统的某些节点失败，也可以确保系统的正确操作。但是，分布式算法的开发是一个手动且复杂的过程，导致科学论文通常呈现出单一算法或现有算法的变化。为了自动化开发此类算法的过程，这项工作提出了一种使用强化学习来生成正确且高效耐受耐受性的分布式算法的智能代理。我们表明，我们的方法能够在仅12,000个学习情节中生成正确的可靠的可靠广播算法，而文献中其他可用的其他性能则与其他人相同。
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.
