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

• Main Authors: Kamalapurkar, Rushikesh (Author, http://id.loc.gov/vocabulary/relators/aut), Walters, Patrick (http://id.loc.gov/vocabulary/relators/aut), Rosenfeld, Joel (http://id.loc.gov/vocabulary/relators/aut), Dixon, Warren (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Communications and Control Engineering,
• Subjects: Control engineering.; Calculus of variations.; System theory.; Electrical engineering.; Control and Systems Theory.; Calculus of Variations and Optimal Control; Optimization.; Systems Theory, Control.; Communications Engineering, Networks.

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...

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.