书籍详情
格式
精装书
页数
552
语言
英语
已发布
Nov 13, 2018
出版商
Bradford Books
版本
second edition
ISBN-10
0262039249
ISBN-13
9780262039246
描述
In this comprehensive exploration of reinforcement learning, two leading experts in the field, Richard S. Sutton and Andrew G. Barto, guide readers through the foundational concepts and advanced applications of this dynamic area of artificial intelligence. The authors take a structured approach, beginning with core principles and progressively delving into complex methodologies that define reinforcement learning. With a focus on both theoretical understanding and practical implementation, this work serves as an essential resource for students and professionals alike.
The second edition reflects the latest developments in the field, incorporating new topics and insights that have emerged since the previous release. Sutton and Barto strengthen their discussions with real-world examples, algorithms, and programming frameworks, enabling readers to grasp the implications and potential of reinforcement learning in various industries. They emphasize the adaptability and dynamic decision-making capabilities essential for developing intelligent systems.
Overall, this book not only enhances comprehension of reinforcement learning but also encourages critical thinking and innovation among readers. Users are equipped with a comprehensive toolkit for tackling complex problems through the lens of reinforcement learning, paving the way for future advancements in artificial intelligence.
The second edition reflects the latest developments in the field, incorporating new topics and insights that have emerged since the previous release. Sutton and Barto strengthen their discussions with real-world examples, algorithms, and programming frameworks, enabling readers to grasp the implications and potential of reinforcement learning in various industries. They emphasize the adaptability and dynamic decision-making capabilities essential for developing intelligent systems.
Overall, this book not only enhances comprehension of reinforcement learning but also encourages critical thinking and innovation among readers. Users are equipped with a comprehensive toolkit for tackling complex problems through the lens of reinforcement learning, paving the way for future advancements in artificial intelligence.
类型
科学与技术
历史
心理学