概率统计系列讲座十五：
报告题目【Risk-sensitive discounted continuous-time Markov decision processes】
时间：2020年11月07日 (星期六)上午 09:00
地点：腾讯会议（会议 ID：511 3889 6273）
报告人：中山大学教授，郭先平
主办：数学与信息学院
参加对象：统计系老师与学生
报告摘要：This talk is on the risk-sensitive discounted continuous-time Markov decision processes with unbounded transition and cost rates. Different from the case of bounded transition/cost rates, the optimality equation (OE) no longer has a solution satisfying the uniform convergent condition introduced in the existing literature. Thus, we first replace the uniform convergent condition of the solution with a suitable weighted-bound. Then, we find mild conditions imposed on the primitive data of the decision processes, which not only ensure the existence of a solution to the OE but also are the generalization of the bounded transition/cost rates conditions. Furthermore, using the characterization of the weighted-bound and a novel technique, from the OE we prove the existence of an optimal policy out of the class of randomized history-dependent policies. Finally, we present two examples with unbounded transition/cost rates to illustrate our results.
报告人简介：郭先平，男，博士，博士生导师，国家杰出青年科学基金获得者， 1996年于中南大学获博士学位，2002于中山大学晋升为教授，2003年入选教育部优秀青年教师资助计划，2004年入选教育部新世纪优秀人才支持计划，2010年被评为珠江学者特聘教授。担（曾）任国际（SCI）杂志 Advances in Applied Probability，Journal of Applied Probability，Science China Mathematics，Journal of Dynamics and Games，及国内期刊《中国科学：数学》、《应用数学学报》、《应用概率统计》、《运筹学学报》等杂志编委。研究兴趣为马氏决策过程、随机博弈、风险控制、排队优化等。
