Title Page Copyright and Credits Hands-On Deep Learning for Games Dedication About Packt Why subscribe? Packt.com Contributors About the author Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews 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 Other Books You May Enjoy Leave a review - let other readers know what you think