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Springer Integral and Inverse Reinforcement Learning Book

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Description

Explore advanced learning techniques for optimal control systems and games with this Springer publication. Part of the Advances in Industrial Control series, this book develops integral reinforcement learning (RL) to achieve model-free control without the need for system estimation. This approach avoids the estimation errors common in traditional system identification methods. Designed with applications in autonomous driving and microgrid systems in mind, the text also introduces novel inverse RL methods. These methods provide data-driven, model-free solutions for inverse optimization, optimal control, imitation learning, and autonomous systems. It serves as a comprehensive resource for readers seeking to bridge the gap between theory and practical application in modern control engineering.

Key Features

This Springer book covers integral reinforcement learning for model-free control without requiring system estimation.

The text provides novel inverse RL methods to support data-driven solutions for inverse optimization and optimal control.

Content is motivated by real-world applications such as autonomous driving and microgrid systems.

The book explores imitation learning and autonomous systems within the Advances in Industrial Control series.

It offers an alternative to system identification methods to help avoid inevitable estimation errors.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
06 March 2024
Listed Since
29 August 2023

Barcode

No barcode data available

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