Name: Model–Based Reinforcement Learning: From Data to Actions With Python–Based Toolbox (Wiley–Ieee Press Book Series on Control Systems Theory and Applications)
SKU: 53330353
Price: 125.40 EUR
Availability: InStock
Author: Farsi, Milad ; Liu, Jun ; Di Benedetto, Maria Domenica
ISBN: 9781119808572

portada Model–Based Reinforcement Learning: From Data to Actions With Python–Based Toolbox (Wiley–Ieee Press Book Series on Control Systems Theory and Applications)

Model–Based Reinforcement Learning: From Data to Actions With Python–Based Toolbox (Wiley–Ieee Press Book Series on Control Systems Theory and Applications)

Type

Physical Book

Author

Jun Liu

Milad Farsi

Illustrated by

Maria Domenica Di Benedetto

Publisher

Wiley-IEEE Press

Year

2022

Language

English

Pages

320

Format

Hardcover

Dimensions

22.9x15.2x1.6 cm

Weight

0.52 kg.

ISBN13

9781119808572

Synopsis "Model–Based Reinforcement Learning: From Data to Actions With Python–Based Toolbox (Wiley–Ieee Press Book Series on Control Systems Theory and Applications) "

Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory--optimal control and dynamic programming - or on algorithms--most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework--from design to application--of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.

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Model–Based Reinforcement Learning: From Data to Actions With Python–Based Toolbox (Wiley–Ieee Press Book Series on Control Systems Theory and Applications) - Farsi, Milad ; Liu, Jun ; Di Benedetto, Maria Domenica

Synopsis "Model–Based Reinforcement Learning: From Data to Actions With Python–Based Toolbox (Wiley–Ieee Press Book Series on Control Systems Theory and Applications) "

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