Deep Reinforcement Learning with Guaranteed Performance

This book PDF is perfect for those who love Technology & Engineering genre, written by Yinyan Zhang and published by Springer Nature which was released on 09 November 2019 with total hardcover pages 225. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Deep Reinforcement Learning with Guaranteed Performance books below.

Deep Reinforcement Learning with Guaranteed Performance
Deep Reinforcement Learning with Guaranteed Performance
Author : Yinyan Zhang
File Size : 46,7 Mb
Publisher : Springer Nature
Language : English
Release Date : 09 November 2019
ISBN : 9783030333843
Pages : 225 pages
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Deep Reinforcement Learning with Guaranteed Performance by Yinyan Zhang Book PDF Summary

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

Deep Reinforcement Learning with Guaranteed Performance

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This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with

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