Large Markov decision processes and combinatorial optimisation

马尔可夫决策过程在对从供应链和排队网络的分析到认知科学和自动车辆控制的广泛应用的建模方面继续受到欢迎。尽管如此，随着模型规模的快速增长，它们往往在数字上变得难以处理。最近的工作使用机器学习技术来克服这一关键问题，但没有收敛保证。该项目旨在为解决大型马尔可夫决策过程提供理论上可靠的框架，并利用它们来解决重要的组合优化问题。这一及时的项目可以促进澳大利亚在为许多科学和工业应用开发这种新型框架方面的地位。

Markov decision processes continue to gain in popularity for modelling a wide range of applications ranging from analysis of supply chains and queueing 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 project aims to provide theoretically sound frameworks for solving large Markov decision processes, and exploit them to solve important combinatorial optimisation problems. This timely project can promote Australia's position in the development of such novel frameworks for many scientific and industrial applications.