Abstract
In this paper we consider finite state and action, discrete time parameters normalized Markov decision chains, i.e., Markov decision processes with transition matrices that are nonnegative with spectral radius not exceeding one (but not necessarily substochastic). We show that the periodical reward gained in period N is bounded by a polynom, uniformly over the set of all policies. The degree of this polynom can be obtained by considering only the set of stationary policies. Extending and improving results of Sladky (1974) for the stochastic case, we obtain necessary and sufficient conditions for n discount optimality of arbitrary (not necessarily stationary) policies.
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.
