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.
This new edition introduces more problem-solving strategies and new conceptual and challenge problems. Also, each Chapter Review has been enhanced with Learning Goals to reinforce the mastery of concepts for students.
This laboratory manual contains 42 experiments for the standard sequence of topics in general, organic, and biological chemistry.
The book guides students through basic chemistry problem solving with engaging visuals and a focus on developing the math skills necessary to be successful in the course.
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Basic Chemistry
Basic Chemistry, Books a la Carte Edition
Essential Laboratory Manual for General, Organic and Biological Chemistry
The main objective in writing this text is to make the study of chemistry an engaging and a positive experience for students by relating the structure and behaviour of matter to real life.
The eText pages look exactly like the printed text, and include powerful interactive and customization functions. This is the product access code card for MasteringChemistry with Pearson eText and does not include the actual bound book.
Health, Environmental, and Green Chemistry Notes throughout the text relate chemistry chapters to real-life topics in health, the environment, and medicine that are interesting and motivating to students.