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Birkhauser Output Feedback Reinforcement Learning Control

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Description

This monograph by Birkhauser provides an in-depth exploration of model-free optimal control systems through the lens of reinforcement learning (RL) theory. It introduces new methods designed to address modern challenges in the field, focusing on the development of sensor data efficient RL algorithms. By utilizing output feedback, these methods reduce sensor requirements while providing necessary optimality and stability guarantees. The text covers several practical engineering challenges, including control constraints, disturbance rejection, and communication delays. Additionally, the book incorporates ideas from game theory to solve complex output problems. This work serves as a significant resource for those studying advanced control engineering and reinforcement learning applications in linear systems.

Key Features

This Birkhauser monograph explores the analysis and design of model-free optimal control systems based on reinforcement learning theory.

New methods are presented to develop sensor data efficient RL algorithms that reduce the requirement of sensors via output feedback.

The research ensures both optimality and stability guarantees for control systems within the reinforcement learning framework.

Practical engineering challenges such as disturbance rejection, control constraints, and communication delays are addressed.

The text incorporates game theory concepts to help solve complex output problems in linear systems.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 November 2022
Listed Since
27 July 2022

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