"Deep Reinforcement Learning Hands-On" is a comprehensive technical book that explores modern methods of Reinforcement Learning (RL). Written by Maxim Lapan, the book offers a practical approach to learning and applying concepts such as Deep Q-Networks, value iteration, policy gradients, TRPO, and AlphaGo Zero. With 546 pages, it serves as a valuable resource for anyone looking to delve into the complex world of machine learning. The book is written in English and is aimed at readers with a basic understanding of programming and machine learning. The clearly structured content and practical examples enable readers to translate theoretical foundations into practice. With dimensions of 19.7 cm in width and 23.7 cm in height, it is convenient for both study and professional development. This book is a valuable addition to the library of professionals and students interested in the latest developments in the field of Reinforcement Learning.