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中图分类法:
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O224 版次： |
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著者:
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Meyn, S. P. |
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题名:
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Control systems and reinforcement learning / / , |
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出版发行:
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出版地： Cambridge, United Kingdom ; 出版社： Cambridge University Press, 出版日期： 2022. |
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载体形态:
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xv, 435 pages : illustrations ; 26 cm |
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内容提要:
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"A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of "deep" or "Q", or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning."--Provided by publisher. |
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主题词:
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Reinforcement learning. |
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主题词:
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Mathematical optimization. |
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主题词:
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Control theory. |