更新时间：2021-06-24 15:48:33
coverpage
Title Page
Copyright and Credits
Hands-On Deep Learning for Games
Dedication
About Packt
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Contributors
About the author
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Preface
Who this book is for
What this book covers
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Download the example code files
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Conventions used
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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
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
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
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
Unity ML-Agents
Installing ML-Agents
Training an agent
What's in a brain?
