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Více informací o knize Reinforcement Learning for Optimal Feedback Control

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Anotace knihy

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 in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book's focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor-critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements.

Parametry knihy

Zařazení knihy Knihy v angličtině Technology, engineering, agriculture Electronics & communications engineering Electronics engineering

• Plný název: Reinforcement Learning for Optimal Feedback Control
• Autor: Rushikesh Kamalapurkar, Patrick Walters, Joel Rosenfeld, Warren Dixon
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 293
• EAN: 9783030086893
• ISBN: 9783030086893
• ID: 22485821
• Nakladatelství: Springer Nature Switzerland AG
• Hmotnost: 646 g
• Rozměry: 235 × 155 × 15 mm
• Datum vydání: 26. December 2018

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