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

Exercises

The further we progress in this book, the more valuable and expensive each of these exercises will become. By expensive, we mean the amount of time you need to invest in each will increase. That may mean you are inclined to do fewer exercises, but please continue to try and do two or three exercises on your own:

  1. Revisit TensorSpace Playground and see if you can understand the difference pooling makes in those models. Remember that we avoid the use of pooling in order to avoid losing spatial integrity.
  2. Open Chapter_7_DQN_CNN.py and alter some of the convolutional layer inputs such as the kernel or stride size. See what effect this has on training.
  3. Tune the hyperparameters or create new ones for Chapter_7_DoubleDQN.py.
  4. Tune the hyperparameters or create new ones for Chapter_7_DDQN.py.
  5. Tune the hyperparameters or create new ones for Chapter_7_DoubleDQN_wprority.py.
  6. Convert...