From Shortest Paths to Reinforcement Learning

From Shortest Paths to Reinforcement Learning: A MATLAB-Based Tutorial on Dynamic Programming

by Brandimarte, Paolo

Type Book
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Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with prob…

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Title Statements

  • Full title :From Shortest Paths to Reinforcement Learning : A MATLAB-Based Tutorial on Dynamic Programming / Paolo Brandimarte [aut]

Summary

Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.

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Edition Statements

  • Publication Date:2021

Publication Statements

The Physical Object

  • Description:1 online resource

Edition Identifiers

  • ISBN:9783030618674, 3030618676, 9783030618681, 3030618684, 9783030618698, 3030618692, 9783030618667, 3030618668
  • DOI:10.1007/978-3-030-61867-4
  • oclc:on1346763133

Table of Contents

The dynamic programming principle -- Implementing dynamic programming -- Modeling for dynamic programming -- Numerical dynamic programming for discrete states -- Approximate dynamic programming and reinforcement learning for discrete states -- Numerical dynamic programming for continuous states -- Approximate dynamic programming and reinforcement learning for continuous states.

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