Book Image

Hands-On Reinforcement Learning for Games

By : Micheal Lanham
Book Image

Hands-On Reinforcement Learning for Games

By: Micheal Lanham

Overview of this book

With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.
Table of Contents (19 chapters)
1
Section 1: Exploring the Environment
7
Section 2: Exploiting the Knowledge
15
Section 3: Reward Yourself

Creating a new environment

The great thing about the ML-Agents toolkit is the ability it provides for creating new agent environments quickly and simply. You can even transform existing games or game projects into training environments for a range of purposes, from building full robotic simulations to simple game agents or even game agents that play as non-player characters. There is even potential to use DRL agents for game quality assurance testing. Imagine building an army of game testers that learn to play your game with just trial and error. The possibilities are endless and Unity is even building a full cloud-based simulation environment for running or training these agents in the future.

In this section, we will walk through using a game project as a new training environment. Any environment you create in Unity would be best tested with the ML-Agents toolkit before you...