报告人: 廖仲威(特聘研究员) 华南师范大学

报告题目: Risk-sensitive optimization of continuous-time Markov decision processes

报告时间: 2020年11月19日，下午2:00-3:00（暂定）

报告地点: A4-216

报告摘要

In this talk we consider 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 convergence condition introduced in the existing literature. Thus, we first replace the uniform convergence condition of the solution with a new boundary condition. 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 boundary condition 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：

廖仲威, 华南师范大学.毕业于北京师范大学, 随后在中山大学工作和澳大利亚墨尔本大学访问, 现为华南师范大学华南数学应用与交叉研究中心特聘研究员. 研究兴趣: 随机过程遍历性与长时间行为; 泛函不等式; 马氏决策过程与最优化理论; Stein方法等. 研究工作发表于《SIAM J. CONTROL OPTIM.》,《J. APPL. PROB.》,《ADV. NONLINEAR STUD.》,《STOCH. ANAL. APPL.》,《STAT. PROB. LETT.》等期刊.

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