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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, Walters, Patrick (Author), Rosenfeld, Joel (Author), Dixon, Warren (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2018, 2018
Edition:1st ed. 2018
Series:Communications and Control Engineering
Subjects:
Control And Systems Theory
Calculus Of Variations And Optimization
Control Theory
Systems Theory, Control
System Theory
Control Engineering
Telecommunication
Communications Engineering, Networks
Mathematical Optimization
Calculus Of Variations
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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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