Average cost optimal policies for Markov control processes with Borel state space and unbounded costs

We show the existence of average cost optimal stationary policies for Markov control processes with Borel state space and unbounded costs per stage, under a set of assumptions recently introduced by L.I. Sennott (1989) for control processes with countable state space and finite control sets.     

Markovianity of a subset of components of a Markov process

We consider the necessary and sufficient conditions for a group of the components of a stationary vector Gaussian Markov process to possess Markov property. The representation by a linear Itô stochastic differential equation is also given.     

带Markov跳的离散时间随机控制系统的最大值原理

本文研究一类同时含有Markov跳过程和乘性噪声的离散时间非线性随机系统的最优控制问题, 给出并证明了相应的最大值原理. 首先, 利用条件期望的平滑性, 通过引入具有适应解的倒向随机差分方程, 给出了带有线性差分方程约束的线性泛函的表示形式, 并利用Riesz定理证明其唯一性. 其次, 对带Markov跳的非线性随机控制系统, 利用针状变分法, 对状态方程进行一阶变分, 获得其变分所满足的线性差分方程. 然后, 在引入Hamilton函数的基础上, 通过一对由倒向随机差分方程刻画的伴随方程, 给出并证明了带有Markov跳的离散时间非线性随机最优控制问题的最大值原理, 并给出该最优控制问题的一个充分条件和相应的Hamilton-Jacobi-Bellman方程. 最后, 通过 一个实际例子说明了所提理论的实用性和可行性.     

Backward Stochastic H2/H∞ Control with Random Jumps

This paper is concerned with H₂/H_∞ control of a new class of stochastic systems. The most distinguishing feature, compared with the existing literature, is that the systems are described by backward stochastic differential equations (BSDEs) with Brownian motion and random jumps. It is shown that the backward stochastic H₂/H_∞ control under consideration is associated with the of the corresponding uncontrolled backward stochastic perturbed system. A necessary and sufficient condition for the existence of a unique solution to the control problem under consideration is derived. The resulting solution is characterized by the solution of an uncontrolled forward backward stochastic differential equation (FBSDE) with Brownian motion and random jumps. When the coefficients are all deterministic, the equivalent linear feedback solution involves a pair of Riccati‐type equations and an uncontrolled BSDE. In addition an uncontrolled forward stochastic differential equation (SDE) is given.     

An improved method for bounding stationary measures of finite Markov processes

A new method to compute bounds on stationary results of finite Markov processes in discrete or continuous time is introduced. The method extends previously published approaches using polyhedra of eigenvectors for stochastic matrices with a known lower and upper bound of their elements. Known techniques compute bounds for the elements of the stationary vector with respect to the lower bounds of the matrix elements and another set of bounds with respect to the upper bounds of matrix elements. The resulting bounds are usually not sharp, if lower and upper bounds for the elements are known. The new approach combines lower and upper bounds resulting in sharp bounds which are often much tighter than bounds computed using only one bounding value for the matrix elements.     

Probabilistic opacity for Markov decision processes

Opacity is a generic security property, that has been defined on (non-probabilistic) transition systems and later on Markov chains with labels. For a secret predicate, given as a subset of runs, and a function describing the view of an external observer, the value of interest for opacity is a measure of the set of runs disclosing the secret. We extend this definition to the richer framework of Markov decision processes, where non-deterministic choice is combined with probabilistic transitions, and we study related decidability problems with partial or complete observation hypotheses for the schedulers. We prove that all questions are decidable with complete observation and ω-regular secrets. With partial observation, we prove that all quantitative questions are undecidable but the question whether a system is almost surely non-opaque becomes decidable for a restricted class of ω-regular secrets, as well as for all ω-regular secrets under finite-memory schedulers.     

Stability of solutions of dynamic systems with randomly structured aftereffect

The relation is established between the asymptotic stochastic stability of a linear functional differential equation and exponential stability of the trivial solution to this equation. The direct and inverse Lyapunov theorems on the stability of linear differential equations are proved. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 31–38, March–April 2006.     

Linear‐Quadratic Optimal Control Problem for Partially Observed Forward‐Backward Stochastic Differential Equations of Mean‐Field Type

This paper is concerned with the linear‐quadratic optimal control problem for partially observed forward‐backward stochastic differential equations (FBSDEs) of mean‐field type. Based on the classical spike variational method, backward separation approach as well as filtering technique, we first derive the necessary and sufficient conditions of the optimal control problem with the non‐convex domain. Nextly, by means of the decoupling technique, we obtain two Riccati equations, which are uniquely solvable under certain conditions. Also, the optimal cost functional is represented by the solutions of the Riccati equations for the special case.     

Optimal decisions for continuous time Markov decision processes over finite planning horizons

The computation of ϵ-optimal policies for continuous time Markov decision processes (CTMDPs) over finite time intervals is a sophisticated problem because the optimal policy may change at arbitrary times. Numerical algorithms based on time discretization or uniformization have been proposed for the computation of optimal policies. The uniformization based algorithm has shown to be more reliable and often also more efficient but is currently only available for processes where the gain or reward does not depend on the decision taken in a state. In this paper, we present two new uniformization based algorithms for computing ϵ-optimal policies for CTMDPs with decision dependent rewards over a finite time horizon. Due to a new and tighter upper bound the newly proposed algorithms cannot only be applied for decision dependent rewards, they also outperform the available approach for rewards that do not depend on the decision. In particular for models where the policy only rarely changes, optimal policies can be computed much faster.     

Robustness inequality for Markov control processes with unbounded costs

The paper discusses the robustness of discrete-time Markov control processes whose transition probabilities are known up to certain degree of accuracy. Upper bounds of increase of a discounted cost are derived when using an optimal control policy of the approximating process in order to control the original one. Bounds are given in terms of weighted total variation distance between transition probabilities. They hold for processes on Borel spaces with unbounded one-stage costs functions.     

Optimal control of mean-field backward doubly stochastic systems driven by Itô-Lévy processes

ABSTRACT

In this paper, we introduce a new class of backward doubly stochastic differential equations (in short BDSDE) called mean-field backward doubly stochastic differential equations (in short MFBDSDE) driven by Itô-Lévy processes and study the partial information optimal control problems for backward doubly stochastic systems driven by Itô-Lévy processes of mean-field type, in which the coefficients depend on not only the solution processes but also their expected values. First, using the method of contraction mapping, we prove the existence and uniqueness of the solutions to this kind of MFBDSDE. Then, by the method of convex variation and duality technique, we establish a sufficient and necessary stochastic maximum principle for the stochastic system. Finally, we illustrate our theoretical results by an application to a stochastic linear quadratic optimal control problem of a mean-field backward doubly stochastic system driven by Itô-Lévy processes.     

The Kalman-Bucy Filter for Linear Stochastic Dynamic Systems with Discontinuous Trajectories

An optimal linear filtration problem is considered in the paper based on Kalman-Bucy results. The sequential linear regression method being a modification of fundamental Wiener results is used.     

Quasi-Monte Carlo methods for Markov chains with continuous multi-dimensional state space

We describe a quasi-Monte Carlo method for the simulation of discrete time Markov chains with continuous multi-dimensional state space. The method simulates copies of the chain in parallel. At each step the copies are reordered according to their successive coordinates. We prove the convergence of the method when the number of copies increases. We illustrate the method with numerical examples where the simulation accuracy is improved by large factors compared with Monte Carlo simulation.     

Optimal Control of Forward‐Backward Stochastic Jump‐Diffusion Differential Systems with Observation Noises: Stochastic Maximum Principle

This paper is concerned with a partially observed optimal control problem for a controlled forward‐backward stochastic system with correlated noises between the system and the observation, which generalizes the result of a previous work to a jump‐diffusion system. Under some convexity assumptions, necessary and sufficient optimality conditions for such an optimal control are established in the form of Pontryagin type maximum principle in a unified way by means of duality analysis and convex variational techniques     

Stochastic consensus control with finite frequency specification for Markov jump networks

A stochastic finite frequency consensus protocol for directed networks with Markov jump topologies and external disturbances is proposed in this paper. By introducing the frequency band information into consensus control design, the disagreement dynamics of interconnected networks asymptotically converge to zero with an improved level of disturbance attenuation in the specific frequency band. In addition, the new model transformation approach is presented by exploring certain features of Laplacian matrix in real Jordan form, which leads to more generality of the designed protocol. A numerical example validates the potential of the developed results. Copyright © 2015 John Wiley & Sons, Ltd.     

Markov 控制过程在紧致行动集上的迭代优化算法

研究一类连续时间Markov控制过程(CTMCP)在紧致行动集上关于平均代价性能准则的优化算法。根据CTMCP的性能势公式和平均代价最优性方程，导出了求解最优或次最优平稳控制策略的策略迭代算法和数值迭代算法，在无需假设迭代算子是sp—压缩的条件下，给出了这两种算法的收敛性证明。最后通过分析一个受控排队网络的例子说明了这种方法的优越性。