Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Discrete-Time Markov Control Processes: Basic Optimality Criteria

Discrete-Time Markov Control Processes: Basic Optimality Criteria

Hernandez-Lerma, Onesimo
Lasserre, Jean B.

Hardcover

Series: Stochastic Modelling and Applied Probability, Book 30

Technology & EngineeringGeneral MathematicsProbability & Statistics

ISBN10: 0387945792
ISBN13: 9780387945798
Publisher: Springer Nature
Published: Dec 1 1995
Pages: 216
Weight: 1.11
Height: 0.74 Width: 6.37 Depth: 9.59
Language: English
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. MCPs are a class of stochastic control problems, also known as Markov decision processes, controlled Markov processes, or stochastic dynamic pro- grams; sometimes, particularly when the state space is a countable set, they are also called Markov decision (or controlled Markov) chains. Regardless of the name used, MCPs appear in many fields, for example, engineering, economics, operations research, statistics, renewable and nonrenewable re- source management, (control of) epidemics, etc. However, most of the lit- erature (say, at least 90%) is concentrated on MCPs for which (a) the state space is a countable set, and/or (b) the costs-per-stage are bounded, and/or (c) the control constraint sets are compact. But curiously enough, the most widely used control model in engineering and economics--namely the LQ (Linear system/Quadratic cost) model-satisfies none of these conditions. Moreover, when dealing with partially observable systems) a standard approach is to transform them into equivalent completely observable sys- tems in a larger state space (in fact, a space of probability measures), which is uncountable even if the original state process is finite-valued.

1 different editions

Also available

Also in

Probability & Statistics

View All