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

Summary

In this chapter, we extended our exploration of RL and looked again at trial-and-error methods. In particular, we focused on how the Monte Carlo method could be used as a way of learning from experimenting. We first looked at an example experiment of the Monte Carlo method for calculating π. From there, we looked at how to visualize the output of this experiment with matplotlib. Then, we looked at a code example that showed how to use the Monte Carlo method to solve a version of the FrozenLake problem. Exploring the code example in detail, we uncovered how the agent played the game and, through that exploration, learned to improve a policy. Finally, we finished this chapter by understanding how the agent improves this policy using an incremental sample mean.

The Monte Carlo method is powerful but, as we learned, it requires episodic gameplay while, in the real world...