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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