Micheal Lanham - Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques

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Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.

Tony V. Le works in experience design (XD) and specializes in game and web design/development. Tony graduated from DePaul University with a master of arts in experience design and from Columbia College, Chicago, with a bachelor of arts in game design. Tony currently runs and operates a small development studio known as tvledesign LLC, whose core focus is to create unique interactive experiences and help clients create better experiences for their customers.

Micael DaGraa is a professional game designer and interactive creator who works with independent video game studios and creates interactive apps focused on the health and pharmaceutical industries. He studied digital arts at the University of IESA Multimedia, Paris, and ESAD Matosinhos. He started his career as a project manager in a small studio and then gradually started working as a game developer by helping other studios to develop their games. More recently, he has been creating interactive content for the pharmaceutical industry.

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This book is your one-stop shop for learning how various reinforcement learning (RL) techniques and algorithms play an important role in game development using Python.

The book will start with the basics to provide you with the necessary foundation to understand how RL is playing a major role in game development. Each chapter will help you implement various RL techniques, such as Markov decision processes, Q-learning, the actor-critic method, state-action-reward-state-action (SARSA), and the deterministic policy gradients algorithm, to build logical self-learning agents. You will use these techniques to enhance your game development skills and add various features to improve your overall productivity. Later in the book, you will learn how deep RL techniques can be used to devise strategies that enable agents to learn from their own actions so that you can build fun and engaging games.

By the end of the book, you will be able to use RL techniques to build various projects and contribute to open source applications.

This book is for game developers who are looking to add to their knowledge by implementing RL techniques to build games from scratch. This book will also appeal to machine learning and deep learning practitioners, and RL researchers who want to understand how self-learning agents can be used in the game domain. Prior knowledge of game development and a working knowledge of Python programming are expected.

, Understanding Rewards-Based Learning, explores the basics of learning, what it is to learn, and how RL differs from other, more classic learning methods. From there, we explore how the Markov decision process works in code and how it relates to learning. This leads us to the classic multi-armed and contextual bandit problems. Finally, we will learn about Q-learning and quality-based model learning.