Reinforcement Learning with Deep Neural Networks: A Practical Guide to Building Intelligent System

By Roshan Shetty

"Reinforcement Learning with Deep Neural Networks" is a comprehensive guide to the field of deep reinforcement learning (DRL). The book covers the fundamentals of DRL, including the mathematical foundations and key algorithms, as well as advanced techniques and state-of-the-art algorithms. The book begins with an introduction to the basics of reinforcement learning, including the Markov Decision Process (MDP) and Q-learning. It then covers the use of deep neural networks in DRL, including the use of convolutional and recurrent neural networks for learning from high-dimensional state spaces. The book also covers key DRL algorithms such as Q-learning, SARSA, and actor-critic methods, as well as more advanced techniques such as deep deterministic policy gradient and trust region policy optimization. The book also covers the use of DRL in a variety of applications, such as game playing, robotics, autonomous vehicles, finance, and natural language processing. Throughout the book, the focus is on providing a practical and hands-on approach to learning DRL. Each chapter includes detailed explanations of the key concepts, along with code examples and exercises to help readers gain a deep understanding of the techniques and applications of DRL. The book is aimed at researchers and practitioners in the field of AI and machine learning, as well as graduate students and advanced undergraduates studying computer science, engineering, and related fields. It serves as a valuable resource for anyone interested in mastering the techniques and applications of DRL.

• Format: Paperback
• Language: English
• ISBN: B0BS51F7Q5
• ISBN13: 9798373492249
• Release Date: January 2023
• Publisher: Independently Published
• Length: 390 Pages
• Weight: 0.99 lbs.
• Dimensions: 0.8" x 5.5" x 8.5"