Hands-On Reinforcement Learning for Games

"Hands-On Reinforcement Learning for Games" is a comprehensive guide for game developers who want to explore the applications of reinforcement learning (RL) in creating adaptive and intelligent gaming experiences. You will learn about fundamental RL techniques and progress towards advanced deep reinforcement learning algorithms, enabling you to implement self-learning agents in your projects.

What this Book will help me do

• Understand the implementation of reinforcement learning methodologies like Q-learning and SARSA in gaming contexts.
• Master how to design and train deep reinforcement learning agents using state-of-the-art frameworks.
• Build interactive experiments and simulations to test the effectiveness of game agent decision models.
• Apply advanced algorithms to create dynamic map generation, autonomous characters, and other game features.
• Gain insights into transitioning theoretical reinforcement learning knowledge into practical gaming applications.

Author(s)

Micheal Lanham is a seasoned game developer and AI researcher with extensive experience in applying machine learning to gaming. As an author, he prides himself on providing a practical, project-based learning experience to his readers. With his approachable and clear style, Micheal guides developers through the complexities of reinforcement learning.

Who is it for?

This book is ideal for game developers interested in integrating AI techniques into their projects. It is also suited for machine learning practitioners and reinforcement learning researchers looking to apply their expertise to the gaming domain. Readers should have some experience with game development and Python programming, while aiming to design cutting-edge smart game agents.