Hands-On Deep Learning for Games by Lanham, Micheal -- Read -- Imperial Library of Trantor

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Title Page Copyright and Credits

Hands-On Deep Learning for Games

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Preface

Who this book is for What this book covers To get the most out of this book

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Section 1: The Basics Deep Learning for Games

The past, present, and future of DL

The past The present The future

Neural networks – the foundation

Training a perceptron in Python

Multilayer perceptron in TF TensorFlow Basics Training neural networks with backpropagation

The Cost function Partial differentiation and the chain rule

Building an autoencoder with Keras

Training the model Examining the output

Exercises Summary

Convolutional and Recurrent Networks

Convolutional neural networks

Monitoring training with TensorBoard

Understanding convolution Building a self-driving CNN

Spatial convolution and pooling The need for Dropout

Memory and recurrent networks

Vanishing and exploding gradients rescued by LSTM

Playing Rock, Paper, Scissors with LSTMs Exercises Summary

GAN for Games

Introducing GANs Coding a GAN in Keras

Training a GAN Optimizers

Wasserstein GAN Generating textures with a GAN

Batch normalization Leaky and other ReLUs

A GAN for creating music

Training the music GAN Generating music via an alternative GAN

Exercises Summary

Building a Deep Learning Gaming Chatbot

Neural conversational agents

General conversational models

Sequence-to-sequence learning

Breaking down the code Thought vectors

DeepPavlov Building the chatbot server

Message hubs (RabbitMQ) Managing RabbitMQ Sending and receiving to/from the MQ Writing the message queue chatbot

Running the chatbot in Unity

Installing AMQP for Unity

Exercises Summary

Section 2: Deep Reinforcement Learning Introducing DRL

Reinforcement learning

The multi-armed bandit Contextual bandits

RL with the OpenAI Gym A Q-Learning model

Markov decision process and the Bellman equation Q-learning Q-learning and exploration

First DRL with Deep Q-learning RL experiments

Keras RL

Exercises Summary

Unity ML-Agents

Installing ML-Agents Training an agent What's in a brain? Monitoring training with TensorBoard Running an agent

Loading a trained brain

Exercises Summary

Agent and the Environment

Exploring the training environment

Training the agent visually Reverting to the basics

Understanding state Understanding visual state Convolution and visual state

To pool or not to pool

Recurrent networks for remembering series

Tuning recurrent hyperparameters

Exercises Summary

Understanding PPO

Marathon RL The partially observable Markov decision process Actor-Critic and continuous action spaces

Expanding network architecture

Understanding TRPO and PPO

Generalized advantage estimate

Learning to tune PPO

Coding changes required for control projects Multiple agent policy

Exercises Summary

Rewards and Reinforcement Learning

Rewards and reward functions

Building reward functions

Sparsity of rewards Curriculum Learning Understanding Backplay

Implementing Backplay through Curriculum Learning

Curiosity Learning

The Curiosity Intrinsic module in action Trying ICM on Hallway/VisualHallway

Exercises Summary

Imitation and Transfer Learning

IL, or behavioral cloning Online training Offline training

Setting up for training Feeding the agent

Transfer learning

Transferring a brain Exploring TensorFlow checkpoints

Imitation Transfer Learning

Training multiple agents with one demonstration

Exercises Summary

Building Multi-Agent Environments

Adversarial and cooperative self-play

Training self-play environments

Adversarial self-play Multi-brain play Adding individuality with intrinsic rewards Extrinsic rewards for individuality

Creating uniqueness with customized reward functions Configuring the agents' personalities

Exercises Summary

Section 3: Building Games Debugging/Testing a Game with DRL

Introducing the game Setting up ML-Agents

Introducing rewards to the game Setting up TestingAcademy Scripting the TestingAgent Setting up the TestingAgent

Overriding the Unity input system

Building the TestingInput Adding TestingInput to the scene Overriding the game input Configuring the required brains Time for training

Testing through imitation

Configuring the agent to use IL

Analyzing the testing process

Sending custom analytics

Exercises Summary

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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