Deep Reinforcement Learning Hands-On - Second Edition

Deep Reinforcement Learning Hands-On, Second Edition, guides you through the latest advancements and applications in reinforcement learning (RL). With practical examples and six brand-new chapters, this book offers a comprehensive dive into RL, teaching you how to code intelligent agents to solve real-world problems effectively.

What this Book will help me do

• Apply advanced reinforcement learning techniques to solve practical problems, such as playing complex games and optimizing real-world tasks.
• Understand key RL concepts like deep Q-networks, policy gradients, and actor-critic methods, and implement them with confidence.
• Explore tools like Microsoft's TextWorld and use them to train RL agents in specialized environments.
• Build cost-efficient robotics solutions trained with RL algorithms, bringing automation and robotics into your projects.
• Master discrete optimization problems using RL, like solving a Rubik's Cube or implementing strategies like AlphaGo Zero.

Author(s)

Maxim Lapan is a seasoned AI and deep learning practitioner, with years of hands-on experience developing machine learning solutions. He is passionate about sharing technical knowledge in an accessible and engaging way. His expertise includes building scalable AI systems and transforming theoretical concepts into practicality.

Who is it for?

This book is ideal for developers and software engineers with a basic understanding of Python and deep learning, looking to delve into reinforcement learning. It opens doors to those interested in AI research, real-world robotics, and scalable optimization problems. Whether you're a beginner in RL or seeking a comprehensive update, this book will meet your needs.