Markov Decision Process: Fundamentals and Applications

A discrete-time stochastic control process is referred to as a Markov decision process (MDP) in the field of mathematics. It offers a mathematical framework for modeling decision making in scenarios in which the outcomes are partially controlled by a decision maker and partly determined by random chance. The study of optimization issues that can be handled by dynamic programming lends itself well to the use of MDPs. At the very least, MDPs were recognized to exist in the 1950s. Ronald Howard's book, published in 1960 and titled Dynamic Programming and Markov Processes, is credited for initiating a core body of study on Markov decision processes. They have applications in a wide variety of fields, including as robotics, automatic control, economics, and manufacturing, among others. Because Markov decision processes are an extension of Markov chains, the Russian mathematician Andrey Markov is where the term "Markov decision processes" (MDPs) originated.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Markov decision process

Chapter 2: Markov chain

Chapter 3: Reinforcement learning

Chapter 4: Bellman equation

Chapter 5: Admissible decision rule

Chapter 6: Partially observable Markov decision process

Chapter 7: Temporal difference learning

Chapter 8: Multi-armed bandit

Chapter 9: Optimal stopping

Chapter 10: Metropolis-Hastings algorithm

(II) Answering the public top questions about markov decision process.

(III) Real world examples for the usage of markov decision process in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of markov decision process' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of markov decision process.

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