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Read instantly in your browser. by Onesimo Hernandez-Lerma (Author), Jean Bernard Lasserre (Author). ISBN-13: 978-0387945798. Much of the material appears for the first time in book form.

Basic Optimality Criteria. This book presents the first part of a planned two-volume series devoted to a systematic exposition of some recent developments in the theory of discrete-time Markov control processes (MCPs). Interest is mainly confined to MCPs with Borel state and control (or action) spaces, and possibly unbounded costs and noncompact control constraint sets.

A Markov decision process (MDP) is a discrete time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker

A Markov decision process (MDP) is a discrete time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning.

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cle{cretetimeMC, title {Discrete-time Markov control processes with . The basic control problem we are concerned with is to minimize the infinite-horizon, expected total discounted cost.

cle{cretetimeMC, title {Discrete-time Markov control processes with discounted unbounded costs: Optimality criteria}, author {On{'e}simo Hern{'a}ndez-Lerma and Myriam Mu{~n}oz de Ozak}, journal {Kybernetika}, year {1990}, volume {28}, pages {191-212} }. Onésimo Hernández-Lerma, Myriam Muñoz de Ozak. Published in Kybernetika 1990.

Onesimo Hernandez-Lerma, Jean B. Lasserre. This book presents the second part of a two-volume series devoted to a sys tematic exposition of some recent developments in the theory of discrete time Markov control processes (MCPs). As in the first part, hereafter re ferred to as "Volume I" (see Hernandez-Lerma and Lasserre ), interest is mainly confined to MCPs with Borel state and control spaces, and possibly unbounded costs. This book presents the first part of a planned two-volume series devoted to a systematic exposition of some recent developments in the theory of discrete-time Markov control processes (MCPs)

Onesimo Hernandez-Lerma, Jean B.

O. Hernandez-Lerma and . 134 MARKOV PROCESSES AND CONTROLLED MARKOV CHAINS P. Mandl, Estimation and control in Markov chains, Adv. Appl. Lasserre, Discrete-Time Markov Control Processes: Basic Optimality Criteria, Springer-Verlag, New York, 1996. O. Lasserre, Further Topics on Discrete- Time Markov Control Processes, Springer-Verlag, New York, 1999. Kartashov, Inequalities in theorems of ergodicity and stability for Markov chains with common phase space II, Theory Probab.

Discrete-Time Markov Control Processes: Basic Optimality Criteria. Jean-Bernard Lasserre

Discrete-Time Markov Control Processes: Basic Optimality Criteria. This paper studies several average-cost criteria for Markov control processes on Borel spaces with possibly unbounded costs. Jean-Bernard Lasserre. The authors consider a Markov control process with Borel state and actions spaces, unbounded costs, and under the long-run sample-path average cost criterion. They prove that under very weak assumptions on the transition law and a moment assumption for the one-step cost, there exists a stationary policy with invariant probability distribution v, that is sample-path average cost optimal for v-almost all initial states.

Onesimo Hernandez-Lerma (author), Jean B. Lasserre (author).