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Hands-On Deep Learning for Games

By : Micheal Lanham

3 (2)

Hands-On Deep Learning for Games

3 (2)

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.

Preface

Preface

Who this book is for

What this book covers

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Section 1: The Basics

Section 1: The Basics

Deep Learning for Games

Deep Learning for Games

The past, present, and future of DL

Neural networks – the foundation

Multilayer perceptron in TF

TensorFlow Basics

Training neural networks with backpropagation

Building an autoencoder with Keras

Exercises

Summary

Convolutional and Recurrent Networks

Convolutional and Recurrent Networks

Convolutional neural networks

Understanding convolution

Building a self-driving CNN

Memory and recurrent networks

Playing Rock, Paper, Scissors with LSTMs

Exercises

Summary

GAN for Games

GAN for Games

Introducing GANs

Coding a GAN in Keras

Wasserstein GAN

Generating textures with a GAN

A GAN for creating music

Exercises

Summary

Building a Deep Learning Gaming Chatbot

Building a Deep Learning Gaming Chatbot

Neural conversational agents

Sequence-to-sequence learning

DeepPavlov

Building the chatbot server

Running the chatbot in Unity

Exercises

Summary

Section 2: Deep Reinforcement Learning

Section 2: Deep Reinforcement Learning

Introducing DRL

Introducing DRL

Reinforcement learning

RL with the OpenAI Gym

A Q-Learning model

First DRL with Deep Q-learning

RL experiments

Exercises

Summary

Unity ML-Agents

Unity ML-Agents

Installing ML-Agents

Training an agent

What's in a brain?

Monitoring training with TensorBoard

Running an agent

Exercises

Summary

Agent and the Environment

Agent and the Environment

Exploring the training environment

Understanding state

Understanding visual state

Convolution and visual state

Recurrent networks for remembering series

Exercises

Summary

Understanding PPO

Understanding PPO

Marathon RL

The partially observable Markov decision process

Actor-Critic and continuous action spaces

Understanding TRPO and PPO

Learning to tune PPO

Exercises

Summary

Rewards and Reinforcement Learning

Rewards and Reinforcement Learning

Rewards and reward functions

Sparsity of rewards

Curriculum Learning

Understanding Backplay

Curiosity Learning

Exercises

Summary

Imitation and Transfer Learning

Imitation and Transfer Learning

IL, or behavioral cloning

Online training

Offline training

Transfer learning

Imitation Transfer Learning

Exercises

Summary

Building Multi-Agent Environments

Building Multi-Agent Environments

Adversarial and cooperative self-play

Adversarial self-play

Multi-brain play

Adding individuality with intrinsic rewards

Extrinsic rewards for individuality

Exercises

Summary

Section 3: Building Games

Section 3: Building Games

Debugging/Testing a Game with DRL

Debugging/Testing a Game with DRL

Introducing the game

Setting up ML-Agents

Overriding the Unity input system

Testing through imitation

Analyzing the testing process

Exercises

Summary

Obstacle Tower Challenge and Beyond

Obstacle Tower Challenge and Beyond

The Unity Obstacle Tower Challenge

Deep Learning for your game?

Building your game

More foundations of learning

Summary

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Monitoring training with TensorBoard

Training an agent with RL, or any DL model for that matter, while enjoyable, is not often a simple task and requires some attention to detail. Fortunately, TensorFlow ships with a set of graph tools called TensorBoard we can use to monitor training progress. Follow these steps to run TensorBoard:

Open an Anaconda or Python window. Activate the ml-agents virtual environment. Don't shut down the window running the trainer; we need to keep that going.
Navigate to the ML-Agents/ml-agents folder and run the following command:

tensorboard --logdir=summaries

This will run TensorBoard with its own built-in web server. You can load the page by using the URL that is shown after you run the previous command.
Enter the URL for TensorBoard as shown in the window, or use localhost:6006 or machinename:6006 in your browser. After an hour or so, you...

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