Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-based Toolbox

[LeeAndro]

English | 2023 | ISBN: ‎ 978-1119808572 | 275 pages | True PDF | 11.52 MB

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.
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Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control eeers, and roboticists.



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