Average optimality for continuous-time Markov decision processes with a policy iteration approach

This paper deals with the average expected reward criterion for continuous-time Markov decision processes in general state and action spaces. The transition rates of underlying continuous-time jump Markov processes are allowed to be unbounded, and the reward rates may have neither upper nor lower bounds. We give conditions on the system's primitive data and under which we prove the existence of the average reward optimality equation and an average optimal stationary policy. Also, under our conditions we ensure the existence of ?-average optimal stationary policies. Moreover, we study some properties of average optimal stationary policies. We not only establish another average optimality equation on an average optimal stationary policy, but also present an interesting “martingale characterization” of such a policy. The approach provided in this paper is based on the policy iteration algorithm. It should be noted that our way is rather different from both the usually “vanishing discounting factor approach” and the “optimality inequality approach” widely used in the previous literature.     

Another Set of Conditions for Strong n (n = −1, 0) Discount Optimality in Markov Decision Processes

Abstract

In this paper we study discrete-time Markov decision processes with average expected costs (AEC) and discount-sensitive criteria in Borel state and action spaces. The costs may have neither upper nor lower bounds. We propose another set of conditions on the system's primitive data, and under which we prove (1) AEC optimality and strong ? 1-discount optimality are equivalent; (2) a condition equivalent to strong 0-discount optimal stationary policies; and (3) the existence of strong n (n = ?1, 0)-discount optimal stationary policies. Our conditions are weaker than those in the previous literature. In particular, the “stochastic monotonicity condition” in this paper has been first used to study strong n (n = ?1, 0)-discount optimality. Moreover, we provide a new approach to prove the existence of strong 0-discount optimal stationary policies. It should be noted that our way is slightly different from those in the previous literature. Finally, we apply our results to an inventory system and a controlled queueing system.     

Average sample-path optimality for continuous-time Markov decision processes in Polish spaces

In this paper we study the average sample-path cost(ASPC) problem for continuous-time Markov decision processes in Polish spaces.To the best of our knowledge,this paper is a first attempt to study the ASPC criterion on continuous-time MDPs with Polish state and action spaces.The corresponding transition rates are allowed to be unbounded,and the cost rates may have neither upper nor lower bounds.Under some mild hypotheses,we prove the existence of ε(ε≥ 0)-ASPC optimal stationary policies based on two differe...     

Markov Decision Processes with Variance Minimization: A New Condition and Approach

Abstract

This article deals with the limiting average variance criterion for discrete-time Markov decision processes in Borel spaces. The costs may have neither upper nor lower bounds. We propose another set of conditions under which we prove the existence of a variance minimal policy in the class of average expected cost optimal stationary policies. Our conditions are weaker than those in the previous literature. Moreover, some sufficient conditions for the existence of a variance minimal policy are imposed on the primitive data of the model. In particular, the stochastic monotonicity condition in this paper has been first used to study the limiting average variance criterion. Also, the optimality inequality approach provided here is different from the “optimality equation approach” widely used in the previous literature. Finally, we use a controlled queueing system to illustrate our results.     

A New Condition and Approach for Zero-Sum Stochastic Games with Average Payoffs

Abstract

This article deals with discrete-time two-person zero-sum stochastic games with Borel state and action spaces. The optimality criterion to be studied is the long-run expected average payoff criterion, and the (immediate) payoff function may have neither upper nor lower bounds. We first replace the optimality equation widely used in the previous literature with two so-called optimality inequalities, and give a new set of conditions for the existence of solutions to the optimality inequalities. Then, from the optimality inequalities we ensure the existence of a pair of average optimal stationary strategies. Our new condition is slightly weaker than those in the previous literature, and as a byproduct some interesting results such as the convergence of a value iteration scheme to the value of the discounted payoff game is obtained. Finally, we first apply the main results in this article to generalized inventory systems, and then further provide an example of controlled population processes for which all of our conditions are satisfied, while some of conditions in some of previous literature fail to hold.     

Average optimality inequality for continuous-time Markov decision processes in Polish spaces

In this paper, we study the average optimality for continuous-time controlled jump Markov processes in general state and action spaces. The criterion to be minimized is the average expected costs. Both the transition rates and the cost rates are allowed to be unbounded. We propose another set of conditions under which we first establish one average optimality inequality by using the well-known “vanishing discounting factor approach”. Then, when the cost (or reward) rates are nonnegative (or nonpositive), from the average optimality inequality we prove the existence of an average optimal stationary policy in all randomized history dependent policies by using the Dynkin formula and the Tauberian theorem. Finally, when the cost (or reward) rates have neither upper nor lower bounds, we also prove the existence of an average optimal policy in all (deterministic) stationary policies by constructing a “new” cost (or reward) rate. Research partially supported by the Natural Science Foundation of China (Grant No: 10626021) and the Natural Science Foundation of Guangdong Province (Grant No: 06300957).     

Risk-sensitive average continuous-time Markov decision processes with unbounded rates

Abstract

In this paper we study the risk-sensitive average cost criterion for continuous-time Markov decision processes in the class of all randomized Markov policies. The state space is a denumerable set, and the cost and transition rates are allowed to be unbounded. Under the suitable conditions, we establish the optimality equation of the auxiliary risk-sensitive first passage optimization problem and obtain the properties of the corresponding optimal value function. Then by a technique of constructing the appropriate approximating sequences of the cost and transition rates and employing the results on the auxiliary optimization problem, we show the existence of a solution to the risk-sensitive average optimality inequality and develop a new approach called the risk-sensitive average optimality inequality approach to prove the existence of an optimal deterministic stationary policy. Furthermore, we give some sufficient conditions for the verification of the simultaneous Doeblin condition, use a controlled birth and death system to illustrate our conditions and provide an example for which the risk-sensitive average optimality strict inequality occurs.     

New Average Optimality Conditions for Semi-Markov Decision Processes in Borel Spaces

This paper deals with semi-Markov decision processes under the average expected criterion. The state and action spaces are Borel spaces, and the cost/reward function is allowed to be unbounded from above and from below. We give another set of conditions, under which the existence of an optimal (deterministic) stationary policy is proven by a new technique of two average optimality inequalities. Our conditions are slightly weaker than those in the existing literature, and some new sufficient conditions for the verifications of our assumptions are imposed on the primitive data of the model. Finally, we illustrate our results with three examples.     

Recent results on conditions for the existence of average optimal stationary policies

This paper concerns countable state space Markov decision processes endowed with a (long-run expected)average reward criterion. For these models we summarize and, in some cases,extend some recent results on sufficient conditions to establish the existence of optimal stationary policies. The topics considered are the following: (i) the new assumptions introduced by Sennott in [20–23], (ii)necessary and sufficient conditions for the existence of a bounded solution to the optimality equation, and (iii) equivalence of average optimality criteria. Some problems are posed.This research was partially supported by the Third World Academy of Sciences (TWAS) under Grant No. TWAS RG MP 898-152.     

Sample-path optimality and variance-maximization for Markov decision processes

This paper studies both the average sample-path reward (ASPR) criterion and the limiting average variance criterion for denumerable discrete-time Markov decision processes. The rewards may have neither upper nor lower bounds. We give sufficient conditions on the system’s primitive data and under which we prove the existence of ASPR-optimal stationary policies and variance optimal policies. Our conditions are weaker than those in the previous literature. Moreover, our results are illustrated by a controlled queueing system. Research partially supported by the Natural Science Foundation of Guangdong Province (Grant No: 06025063) and the Natural Science Foundation of China (Grant No: 10626021).     

Continuous-Time Markov Decision Processes with Unbounded Transition and Discounted-Reward Rates

Abstract

In this article, we study continuous-time Markov decision processes in Polish spaces. The optimality criterion to be maximized is the expected discounted criterion. The transition rates may be unbounded, and the reward rates may have neither upper nor lower bounds. We provide conditions on the controlled system's primitive data under which we prove that the transition functions of possibly non-homogeneous continuous-time Markov processes are regular by using Feller's construction approach to such transition functions. Then, under continuity and compactness conditions we prove the existence of optimal stationary policies by using the technique of extended infinitesimal operators associated with the transition functions of possibly non-homogeneous continuous-time Markov processes, and also provide a recursive way to compute (or at least to approximate) the optimal reward values. The conditions provided in this paper are different from those used in the previous literature, and they are illustrated with an example.     

Blackwell Optimality in the Class of Markov Policies for Continuous-Time Controlled Markov Chains

This paper deals with Blackwell optimality for continuous-time controlled Markov chains with compact Borel action space, and possibly unbounded reward (or cost) rates and unbounded transition rates. We prove the existence of a deterministic stationary policy which is Blackwell optimal in the class of all admissible (nonstationary) Markov policies, thus extending previous results that analyzed Blackwell optimality in the class of stationary policies. We compare our assumptions to the corresponding ones for discrete-time Markov controlled processes.     

Zero-sum stochastic games with average payoffs: New optimality conditions

In this paper we study zero-sum stochastic games. The optimality criterion is the long-run expected average criterion, and the payoff function may have neither upper nor lower bounds. We give a new set of conditions for the existence of a value and a pair of optimal stationary strategies. Our conditions are slightly weaker than those in the previous literature, and some new sufficient conditions for the existence of a pair of optimal stationary strategies are imposed on the primitive data of the model. Our results are illustrated with a queueing system, for which our conditions are satisfied but some of the conditions in some previous literatures fail to hold.     

Continuous-Time Markov Decision Processes with State-Dependent Discount Factors

We consider continuous-time Markov decision processes in Polish spaces. The performance of a control policy is measured by the expected discounted reward criterion associated with state-dependent discount factors. All underlying Markov processes are determined by the given transition rates which are allowed to be unbounded, and the reward rates may have neither upper nor lower bounds. By using the dynamic programming approach, we establish the discounted reward optimality equation (DROE) and the existence and uniqueness of its solutions. Under suitable conditions, we also obtain a discounted optimal stationary policy which is optimal in the class of all randomized stationary policies. Moreover, when the transition rates are uniformly bounded, we provide an algorithm to compute (or?at least to approximate) the discounted reward optimal value function as well as a discounted optimal stationary policy. Finally, we use an example to illustrate our results. Specially, we first derive an explicit and exact solution to the DROE and an explicit expression of a discounted optimal stationary policy for such an example.     

Denumerable state semi-Markov decision processes with unbounded costs,average cost criterion

This paper establishes a rather complete optimality theory for the average cost semi-Markov decision model with a denumerable state space, compact metric action sets and unbounded one-step costs for the case where the underlying Markov chains have a single ergotic set. Under a condition which, roughly speaking, requires the existence of a finite set such that the supremum over all stationary policies of the expected time and the total expected absolute cost incurred until the first return to this set are finite for any starting state, we shall verify the existence of a finite solution to the average costs optimality equation and the existence of an average cost optimal stationary policy.     

Denumerable controlled Markov chains with strong average optimality criterion: Bounded & unbounded costs

This paper studies discrete-time nonlinear controlled stochastic systems, modeled by controlled Markov chains (CMC) with denumerable state space and compact action space, and with an infinite planning horizon. Recently, there has been a renewed interest in CMC with a long-run, expected average cost (AC) optimality criterion. A classical approach to study average optimality consists in formulating the AC case as a limit of the discounted cost (DC) case, as the discount factor increases to 1, i.e., as the discounting effectvanishes. This approach has been rekindled in recent years, with the introduction by Sennott and others of conditions under which AC optimal stationary policies are shown to exist. However, AC optimality is a rather underselective criterion, which completely neglects the finite-time evolution of the controlled process. Our main interest in this paper is to study the relation between the notions of AC optimality andstrong average cost (SAC) optimality. The latter criterion is introduced to asses the performance of a policy over long but finite horizons, as well as in the long-run average sense. We show that for bounded one-stage cost functions, Sennott's conditions are sufficient to guarantee thatevery AC optimal policy is also SAC optimal. On the other hand, a detailed counterexample is given that shows that the latter result does not extend to the case of unbounded cost functions. In this counterexample, Sennott's conditions are verified and a policy is exhibited that is both average and Blackwell optimal and satisfies the average cost inequality.     

Dynamic admission and service rate control of a queue

This paper investigates a queueing system in which the controller can perform admission and service rate control. In particular, we examine a single-server queueing system with Poisson arrivals and exponentially distributed services with adjustable rates. At each decision epoch the controller may adjust the service rate. Also, the controller can reject incoming customers as they arrive. The objective is to minimize long-run average costs which include: a holding cost, which is a non-decreasing function of the number of jobs in the system; a service rate cost c(x), representing the cost per unit time for servicing jobs at rate x; and a rejection cost κ for rejecting a single job. From basic principles, we derive a simple, efficient algorithm for computing the optimal policy. Our algorithm also provides an easily computable bound on the optimality gap at every step. Finally, we demonstrate that, in the class of stationary policies, deterministic stationary policies are optimal for this problem.     

A new strong optimality criterion for nonstationary Markov decision processes

This paper deals with a new optimality criterion consisting of the usual three average criteria and the canonical triplet (totally so-called strong average-canonical optimality criterion) and introduces the concept of a strong average-canonical policy for nonstationary Markov decision processes, which is an extension of the canonical policies of Herna′ndez-Lerma and Lasserre [16] (pages: 77) for the stationary Markov controlled processes. For the case of possibly non-uniformly bounded rewards and denumerable state space, we first construct, under some conditions, a solution to the optimality equations (OEs), and then prove that the Markov policies obtained from the OEs are not only optimal for the three average criteria but also optimal for all finite horizon criteria with a sequence of additional functions as their terminal rewards (i.e. strong average-canonical optimal). Also, some properties of optimal policies and optimal average value convergence are discussed. Moreover, the error bound in average reward between a rolling horizon policy and a strong average-canonical optimal policy is provided, and then a rolling horizon algorithm for computing strong average ε(>0)-optimal Markov policies is given.     

连续时间马尔可夫决策过程的折扣模型

本文考虑的是转移速率族任意且费用率函数可能无界的连续时间马尔可夫决策过程的折扣模型．放弃了传统的要求相应于每个策略的　Ｑ　－过程唯一等条件,而首次考虑相应每个策略的　Ｑ　－过程不一定唯一,　转移速率族也不一定保守,　费用率函数可能无界,　且允许行动空间非空任意的情形．　本文首次用"α－折扣费用最优不等式"更新了传统的α－折扣费用最优方程,并用"最优不等式"和新的方法,不仅证明了传统的主要结果即最优平稳策略的存在性,　而且还进一步探讨了（　∈＞０　　）－最优平稳策略,具有单调性质的最优平稳策略,　以及（∈≥０）　－最优决策过程的存在性,　得到了一些有意义的新结果．　最后,　提供了一个迁移率受控的生灭系统例子,　它满足本文的所有条件,　而传统的假设（见文献［１－１４］）均不成立．