Grokking Deep Reinforcement Learning

Grokking Deep Reinforcement Learning is a book for developers with some understanding of deep learning algorithms, that introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. It explains how AI agents learn optimal behavior on their own from raw sensory input, using trial and error and embracing and building on the things that give them reward and success.

Format: Paperback / softback
Length: 472 pages
Publication date: 25 December 2020
Publisher: Manning Publications

Introduction to Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is a revolutionary machine learning approach that empowers developers with a solid understanding of deep learning algorithms, even without prior experience in reinforcement learning. This comprehensive guide, written with developers in mind, offers a hands-on exploration of DRL, utilizing examples, illustrations, exercises, and a crystal-clear teaching style. With its well-paced curriculum and engaging writing style, readers will delve into the fundamentals of reinforcement learning, discover effective deep learning techniques, and gain practical insights into the emerging field of DRL.

Learning is a continuous process that involves trial and error. We instinctively avoid actions that lead to pain and failure, while embracing and reinforcing those that bring us reward and success. This fundamental pattern serves as the foundation of deep reinforcement learning. It involves the development of machine learning systems that actively explore and learn through their interactions with the environment.

In deep reinforcement learning, AI agents acquire optimal behavior through self-learning from raw sensory input. These agents perceive their environment, interpret the outcomes of their past decisions, and utilize this information to optimize their behavior for maximum long-term returns. By leveraging the power of DRL, developers can create intelligent systems that can adapt and learn in dynamic and complex environments, paving the way for groundbreaking advancements in various industries.

This guide aims to provide a solid foundation for developers who are eager to embrace the world of deep reinforcement learning. Whether you are a seasoned programmer or a newcomer to the field, this book will guide you through the complexities of DRL, helping you build robust and effective AI systems that can navigate the challenges of real-world scenarios. So, let's embark on this exciting journey of learning and exploration, and unlock the full potential of deep reinforcement learning.

Key Features:

Comprehensive Coverage: This guide provides a thorough introduction to DRL, covering foundational concepts, methods, and popular agents used in high-dimensional environments.

Practical Examples: Each chapter includes practical examples and exercises to reinforce the learning process, allowing readers to apply the knowledge gained in a hands-on manner.

Engaging Writing Style: The writing style is engaging, informative, and easy to understand, making it accessible to developers with varying levels of experience.

Well-paced Teaching: The curriculum is carefully designed to provide a balanced blend of theory and practice, ensuring a solid understanding of DRL principles while encouraging practical application.

Cutting-Edge Techniques: Explore the latest advancements in DRL, including agents that emulate human-like behavior and techniques for artificial general intelligence.

Real-World Applications: Learn how DRL is being applied in various industries, such as robotics, autonomous vehicles, and game development, to solve complex problems and achieve remarkable results.

By investing in this guide, developers will gain the skills and knowledge necessary to thrive in the rapidly evolving field of deep reinforcement learning. Whether you are looking to enhance your existing AI capabilities or embark on a new career in this exciting field, this book will be your invaluable companion. So, why wait? Start your journey toward mastery of deep reinforcement learning today!

Weight: 862g
Dimension: 187 x 235 x 30 (mm)
ISBN-13: 9781617295454