The Resource Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach, by Rushikesh Kamalapurkar, Patrick Walters, Joel Rosenfeld, Warren Dixon, (electronic resource)

Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach, by Rushikesh Kamalapurkar, Patrick Walters, Joel Rosenfeld, Warren Dixon, (electronic resource)

Label
Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach
Title
Reinforcement Learning for Optimal Feedback Control
Title remainder
A Lyapunov-Based Approach
Statement of responsibility
by Rushikesh Kamalapurkar, Patrick Walters, Joel Rosenfeld, Warren Dixon
Creator
Contributor
Author
Author
Subject
Language
eng
Summary
Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book's focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor-critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements. This monograph provides academic researchers with backgrounds in diverse disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry
Member of
Is part of
http://library.link/vocab/creatorName
Kamalapurkar, Rushikesh
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
  • s3TI4a9o8iE
  • 08Rq3Cz2H5U
  • mQDoQ7lAagc
  • diZidlpb8iI
Image bit depth
0
LC call number
TJ212-225
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
  • Walters, Patrick.
  • Rosenfeld, Joel.
  • Dixon, Warren.
Series statement
Communications and Control Engineering,
http://library.link/vocab/subjectName
  • Automatic control
  • Calculus of variations
  • System theory
  • Electrical engineering
  • Control and Systems Theory
  • Calculus of Variations and Optimal Control; Optimization
  • Systems Theory, Control
  • Communications Engineering, Networks
Label
Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach, by Rushikesh Kamalapurkar, Patrick Walters, Joel Rosenfeld, Warren Dixon, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Chapter 1. Optimal control -- Chapter 2. Approximate dynamic programming -- Chapter 3. Excitation-based online approximate optimal control -- Chapter 4. Model-based reinforcement learning for approximate optimal control -- Chapter 5. Differential Graphical Games -- Chapter 6. Applications -- Chapter 7. Computational considerations -- Reference -- Index
Dimensions
unknown
Edition
1st ed. 2018.
Extent
XVI, 293 p.
File format
multiple file formats
Form of item
electronic
Isbn
9783319783840
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-78384-0
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-78384-0
Label
Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach, by Rushikesh Kamalapurkar, Patrick Walters, Joel Rosenfeld, Warren Dixon, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Chapter 1. Optimal control -- Chapter 2. Approximate dynamic programming -- Chapter 3. Excitation-based online approximate optimal control -- Chapter 4. Model-based reinforcement learning for approximate optimal control -- Chapter 5. Differential Graphical Games -- Chapter 6. Applications -- Chapter 7. Computational considerations -- Reference -- Index
Dimensions
unknown
Edition
1st ed. 2018.
Extent
XVI, 293 p.
File format
multiple file formats
Form of item
electronic
Isbn
9783319783840
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-78384-0
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-78384-0

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