MODEL-BASED REINFORCEMENT LEARNING: FROM DATA TO ACTIONS WITH A PYTHON-BASED TOOLBOX

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DESCRIPTION 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.

Linear Algebra and Learning from Data

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Linear Algebra and Learning from Data ISBN13：9780692196380 出版社：Cambridge Univ Pr 作者：Gilbert Strang 裝訂：精裝 出版日：2019/02/28 內容簡介 ●The first textbook designed to teach linear algebra as a tool for deep learning ●From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra ●Includes the necessary background from statistics and optimization ●Explains stochastic gradient descent, the key algorithim of deep learning, in detail 目錄 Deep learning and neural nets Preface and acknowledgements Part I. Highlights of Linear Algebra Part II. Computations with Large Matrices Part III. Low Rank and Compressed Sensing Part IV. Special Matrices Part V. Probability and Statistics Part VI. Optimization Part VII. Learning from Data Books on machine learning Eigenvalues and singular values: Rank One Codes and algorithms for numerical linear algebra Counting parameters in the basic factorizations