Reinforcement Learning for Optimal Feedback Control A Lyapunov-Based Approach /

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...

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Bibliographic Details
Main Authors: Kamalapurkar, Rushikesh (Author), Walters, Patrick (Author), Rosenfeld, Joel (Author), Dixon, Warren (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : 2018.
Imprint: Springer,
Edition:1st ed. 2018.
Series:Communications and Control Engineering,
Subjects:
Control engineering.
Mathematical optimization.
Calculus of variations.
System theory.
Control theory.
Telecommunication.
Control and Systems Theory.
Calculus of Variations and Optimization.
Systems Theory, Control.
Communications Engineering, Networks.
Online Access:Full-text access
Table of 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.

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