强化学习
计算机科学
趋同(经济学)
编码(集合论)
马尔可夫决策过程
马尔可夫链
过程(计算)
光学(聚焦)
人工智能
控制(管理)
马尔可夫过程
理论计算机科学
机器学习
数学
程序设计语言
集合(抽象数据类型)
经济
物理
光学
统计
经济增长
标识
DOI:10.1017/9781009051873
摘要
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.
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