In this paper, Weighted reward Perturbed Markov Decision Processes with finite state and countable action spaces (semi-infinite WMDP for short) are considered. The ”weighted reward” refers to appropriately normalized convex combination of the discounted and the long-run average reward...

In this paper weighted singularly perturbed hybrid stochastic systems are discussed. Under some reasonable assumptions, it is shown that there exists a uniformly δ-optimal policy when the perturbation is sufficiently small. Copyright Springer-Verlag Berlin Heidelberg 2005

We consider the problem of the perturbation of a class of linear-quadratic differential games with piecewise deterministic dynamics, where the changes from one structure (for the dynamics) to another are governed by a finite-state Markov process. Player 1 controls the continuous dynamics,...

In this paper, Discounted Markov Decision Processes with finite state and countable action set (semi-infinite DMDP for short) are considered. A policy improvement finite algorithm which finds a nearly optimal deterministic strategy is presented. The steps of the algorithm are based on the...

This work concerns controlled Markov chains with denumerable state space and discrete time parameter. The reward function is assumed to be≤0 and the performance of a control policy is measured by the expected total-reward criterion. Within this context, sufficient conditions are given so that...

Both the static and the dynamic single-leg revenue management problem are studied from the perspective of a risk-averse decision maker. Structural results well-known from the risk-neutral case are extended to the risk-averse case on the basis of an exponential utility function. In particular,...

For sequential decision processes with countable state spaces, we prove compactness of the set of strategic measures corresponding to nonrandomized policies. For the Borel state case, this set may not be compact (Piunovskiy, Optimal control of random sequences in problems with constraints....

We present a novel simulation-based algorithm, as an extension of the well-known policy iteration algorithm, by combining multi-policy improvement with a distributed simulation-based voting policy evaluation, for approximately solving Markov Decision Processes (MDPs) with infinite horizon...

We consider a problem of dynamic replenishment of parts in the supply chain consisting of single class of customers, company, and supplier. Customers request a service via the WEB-based ordering system and the company supports service using parts which are procured from the supplier. The...

This paper studies the discrete time Markov decision processes (MDP) with expected discounted total reward, where the state space is countable, the action space is measurable, the reward function is extended real-valued, and the discount rate may be any real number. Two conditions (GC) and (C)...