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

All about Rainbow DQN

Throughout this book, we have learned how the various threads in Reinforcement Learning (RL) combined to form modern RL and then advanced to Deep Reinforcement Learning (DRL) with the inclusion of Deep Learning (DL). Like most other specialized fields from this convergence, we now see a divergence back to specialized methods for specific classes of environments. We started to see this in the chapters where we covered Policy Gradient (PG) methods and the environments it specialized on are continuous control. The flip side of this is the more typical episodic game environment, which is episodic with some form of discrete control mechanism. These environments typically perform better with DQN but the problem then becomes about DQN. Well, in this chapter, we will look at how smart people solved that by introducing Rainbow DQN.

In this chapter, we will introduce...