About this book
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.
This monograph provides academic researchers with backgrounds in diverse disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.
- DOI https://doi.org/10.1007/978-3-319-78384-0
- Copyright Information Springer International Publishing AG 2018
- Publisher Name Springer, Cham
- eBook Packages Engineering
- Print ISBN 978-3-319-78383-3
- Online ISBN 978-3-319-78384-0
- Series Print ISSN 0178-5354
- Series Online ISSN 2197-7119
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