报告题目：Finite Horizon Partially Observable Semi-Markov Decision Processes under Risk Probability Criteria
报告人： 温馨 博士（中山大学）
时间：2024年10月17日14:30
地点：管理学院 A北307
【报告内容摘要】
This paper deals with a risk probability minimization problem for finite horizon partially observable semi-Markov decision processes, which are the fairly most general models for stochastic dynamic systems. In contrast to the expected discounted and average criteria, the optimality investigated in this paper is to minimize the probability that the accumulated rewards do not reach a prescribed profit level at the finite terminal stage. First, the state space is augmented as the joint conditional distribution of the current unobserved state and the remaining profit goal. We introduce a class of policies depending on observable histories and a class of Markov policies including observable process with the joint conditional distribution. Then under mild assumptions, we prove that the value function is the unique solution to the optimality equation for the probability criterion by using iteration techniques. The existence of (ε-)optimal Markov policy for this problem is established. Finally, we use a bandit problem with the probability criterion to demonstrate our main results in which an effective algorithm and the corresponding numerical calculation are given for the semi-Markov model. Moreover, for the case of reduction to the discrete-time Markov model, we derive a concise solution.
【温馨个人简介】
温馨博士，中山大学管理学院博士后。2017年中南大学数学与应用数学专业学士毕业，2022年中山大学概率论与数理统计专业博士毕业，主要研究方向为马氏决策过程和随机博弈等理论研究，以及在金融保险、收益管理和能源系统管理等领域的应用研究。研究成果被Naval Research Logistics、IEEE Transactions on Automatic Control、Operations Research Letters、Acta Mathematicae Applicatae Sinica(English Series)、《中国科学：数学》、《应用概率统计》等期刊发表或录用。主持国自科青年项目一项，参与国自科重点、专项及面上等项目多项。
欢迎有兴趣的教师，全体博士生、硕士生参加。
管理学院人才办公室
2024-10-16
