In this paper, we consider constrained denumerable state non-stationary Markov decision processes (MDPs, for short) with expected total reward criterion. By the mechanics of intro- ducing Lagrange multiplier and using...In this paper, we consider constrained denumerable state non-stationary Markov decision processes (MDPs, for short) with expected total reward criterion. By the mechanics of intro- ducing Lagrange multiplier and using the methods of probability and analytics, we prove the existence of constrained optimal policies. Moreover, we prove that a constrained optimal policy may be a Markov policy, or be a randomized Markov policy that randomizes between two Markov policies, that differ in only one state.展开更多
In this work, for a control consumption-investment process with the discounted reward optimization criteria, a numerical estimate of the stability index is made. Using explicit formulas for the optimal stationary poli...In this work, for a control consumption-investment process with the discounted reward optimization criteria, a numerical estimate of the stability index is made. Using explicit formulas for the optimal stationary policies and for the value functions, the stability index is explicitly calculated and through statistical techniques its asymptotic behavior is investigated (using numerical experiments) when the discount coefficient approaches 1. The results obtained define the conditions under which an approximate optimal stationary policy can be used to control the original process.展开更多
基金the National Natural Science Foundation of China !19901038by Natural Science Foundation of Guangdong Province and by Found
文摘In this paper, we consider constrained denumerable state non-stationary Markov decision processes (MDPs, for short) with expected total reward criterion. By the mechanics of intro- ducing Lagrange multiplier and using the methods of probability and analytics, we prove the existence of constrained optimal policies. Moreover, we prove that a constrained optimal policy may be a Markov policy, or be a randomized Markov policy that randomizes between two Markov policies, that differ in only one state.
文摘In this work, for a control consumption-investment process with the discounted reward optimization criteria, a numerical estimate of the stability index is made. Using explicit formulas for the optimal stationary policies and for the value functions, the stability index is explicitly calculated and through statistical techniques its asymptotic behavior is investigated (using numerical experiments) when the discount coefficient approaches 1. The results obtained define the conditions under which an approximate optimal stationary policy can be used to control the original process.
