£115.51

Springer - Reinforcement Learning for Optimal Feedback Control

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Description

Reinforcement Learning for Optimal Feedback Control provides a deep look into model-based and data-driven reinforcement learning methods. This text is designed to help readers solve optimal control problems within nonlinear deterministic dynamical systems. By focusing on deterministic systems, the book allows for a detailed Lyapunov-based analysis of performance. To address the challenges of learning under uncertainty, the content introduces data-driven methods for identifying system models in real-time. Readers can see the practical advantages of using existing models and previous experience through recorded data. The book supports these concepts with various simulations and experiments, making it a valuable resource for those studying communications and control engineering.

Key Features

Develops model-based and data-driven reinforcement learning methods specifically for nonlinear deterministic dynamical systems.

Provides data-driven methods for real-time system model identification to help achieve learning under uncertainty.

Offers an in-depth Lyapunov-based analysis by focusing on the characteristics of deterministic systems.

Demonstrates the practical advantages of using recorded data and previous experience through detailed simulations.

Includes experimental data to illustrate the effectiveness of the proposed control methods.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
26 December 2018
Listed Since
30 December 2018

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