Shiyu Zhao – författare

This book explores the Ming dynasty (1368-1644) military, its impact on local society, and its many legacies for Chinese society. It is based on extensive original research by scholars using the methodology of historical anthropology, an approach that has transformed the study of Chinese history by approaching the subject from the bottom up. Its nine chapters, each based on a different region of China, examine the nature of Ming military institutions and their interaction with local social life over time. Several chapters consider the distinctive role of imperial institutions in frontier areas and how they interacted with and affected non-Han ethnic groups and ethnic identity. Others discuss the long-term legacy of Ming military institutions, especially across the dynastic divide from Ming to Qing (1644-1912) and the implications of this for understanding more fully the nature of the Qing rule.

This book explores the Ming dynasty (1368-1644) military, its impact on local society, and its many legacies for Chinese society. It is based on extensive original research by scholars using the methodology of historical anthropology, an approach that has transformed the study of Chinese history by approaching the subject from the bottom up. Its nine chapters, each based on a different region of China, examine the nature of Ming military institutions and their interaction with local social life over time. Several chapters consider the distinctive role of imperial institutions in frontier areas and how they interacted with and affected non-Han ethnic groups and ethnic identity. Others discuss the long-term legacy of Ming military institutions, especially across the dynastic divide from Ming to Qing (1644-1912) and the implications of this for understanding more fully the nature of the Qing rule.

This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability.The book is divided into two parts. The first part is on the mathematical foundations of reinforcement learning, covering topics such as the Bellman equation, Bellman optimality equation, and stochastic approximation. The second part explicates reinforcement learning algorithms, including value iteration and policy iteration, Monte Carlo methods, temporal-difference methods, value function methods, policy gradient methods, and actor-critic methods.With its comprehensive scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning.