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

Training a visual agent

Unity develops a 2D and 3D gaming engine/platform that has become the most popular platform for building games. Most of these games are the 3D variety, hence the specialized interest by Unity in mastering the task of agents that can tackle more 3D natural worlds. It naturally follows then that Unity has invested substantially into this problem and has/is working with DeepMind to develop this further. How this collaboration turns out remains to be seen, but one thing is for certain is that Unity will be our go-to platform for exploring 3D agent training.

In the next exercise, we are going to jump back into Unity and look at how we can train an agent in a visual 3D environment. Unity is arguably the best place to set up and build these type of environments as we have seen in the earlier chapters. Open the Unity editor and follow these steps:

  1. Open the VisualHallway...