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From shortest paths to reinforcement learning : a MATLAB-based tutorial on dynamic programming / Paolo Brandimarte.

Brandimarte, Paolo, author.
2021
T57.83 .B73 2021
Available Online
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Title
From shortest paths to reinforcement learning : a MATLAB-based tutorial on dynamic programming / Paolo Brandimarte.
Author
Brandimarte, Paolo, author.
ISBN
9783030618674 (electronic bk.)
3030618676 (electronic bk.)
3030618668
9783030618667
DOI
https://doi.org/10.1007/978-3-030-61867-4
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-030-61867-4 doi
Call Number
T57.83 .B73 2021
Dewey Decimal Classification
519.703
658.40301
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from digital title page (viewed on March 02, 2021).
Series
EURO advanced tutorials on operational research.
Available in Other Form
Print version: 9783030618667
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All Resources
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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