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

Playing with Keras-RL

Keras is a very popular deep learning framework on its own and it is heavily used by newcomers looking to learn about the basics of constructing networks. The framework is considered very high-level and abstracts most of the inner details of constructing networks. It only goes to assume that an RL framework built with Keras would attempt to do the same thing.

This example is dependent on the version of Keras and TensorFlow and may not work correctly unless the two can work together. If you encounter trouble, try installing a different version of TensorFlow and try again.

To run this example, we will start by doing the installation and all of the setup in this exercise:

  1. To install Keras, you should create a new virtual environment using Python 3.6 and use pip to install it along with the keras-rl framework. The commands to do all of this on Anaconda are...