Book Image

Hands-On Deep Learning for Games

By : Micheal Lanham
Book Image

Hands-On Deep Learning for Games

By: Micheal Lanham

Overview of this book

The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: The Basics
6
Section 2: Deep Reinforcement Learning
14
Section 3: Building Games

Online training

Online Imitation Learning is where you teach the agent to learn the observations of a player or another agent in real time. It also is one of the most fun and engaging ways to train agents or bots. Let's jump in and set up the tennis environment for online Imitation Learning in the next exercise:

  1. Select the TennisArea | AgentA object and set Tennis Agent | Brain to TennisPlayer. In this IL scenario, we have one brain acting as a teacher, the player, and a second brain acting as the student, the learner.
  2. Select the AgentB object and make sure Tennis Agent | Brain is set to TennisLearning. This will be the student brain.
  3. Open the online_bc_config.yaml file from the ML-Agents/ml-agents/config folder. IL does not use the same configuration as PPO so the parameters will have similar names but may not respond to what you have become used to.
  4. Scroll down in the...