Self-Learning Control of Finite Markov Chains

by A.S. Poznyak, Kaddour Najim, E. Gomez-Ramirez

Overview

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.

Product Details

ISBN-13:	9780367398996
Publisher:	Taylor & Francis
Publication date:	10/10/2019
Pages:	314
Product dimensions:	6.88(w) x 9.69(h) x (d)

About the Author

Poznyak, A.S.; Najim, Kaddour; Gomez-Ramirez, E.

Table of Contents

Controlled Markov chains. Unconstrained Markov chains: Lagrange multipliers approach; penalty function approach; projection gradient method. Constrained Markov chains: Lagrange multipliers approach; penalty function approach; nonregular Markov chains; practical aspects.