Abstract
This article deals with a risk probability minimization problem for finite horizon continuous-time Markov decision processes with unbounded transition rates and history-dependent policies. Only using the assumption of nonexplosion of the controlled state process as well as the finiteness of actions available at each state, we not only establish the existence and uniqueness of a solution to the corresponding optimality equation, but also prove the existence of a risk probability optimal policy. Finally, we give two examples to illustrate our results: one example shows that the value iteration algorithm is provided for computing both the value function and an optimal risk probability policy, and the other shows the differences between the conditions in this article and those in the previous literature.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
