论文标题
大型马尔可夫决策过程和组合优化
Large Markov Decision Processes and Combinatorial Optimization
论文作者
论文摘要
马尔可夫的决策过程在建模广泛的应用程序中，从供应链和排队网络到认知科学以及对自动驾驶汽车的控制等广泛应用程序，继续受到人们的欢迎。但是，随着模型的大小增长，它们往往会在数值上棘手。最近的工作使用机器学习技术来克服这个关键问题，但没有融合保证。本注释简要概述了有关解决大型马尔可夫决策过程的文献，并利用它们来解决重要的组合优化问题。
Markov decision processes continue to gain in popularity for modeling a wide range of applications ranging from analysis of supply chains and queuing networks to cognitive science and control of autonomous vehicles. Nonetheless, they tend to become numerically intractable as the size of the model grows fast. Recent works use machine learning techniques to overcome this crucial issue, but with no convergence guarantee. This note provides a brief overview of literature on solving large Markov decision processes, and exploiting them to solve important combinatorial optimization problems.
