Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games /: leverage the power of neural networks and reinforcement learning to build intelligent games. (2019)

• Record Type: Book
• Further Information: Note: Michael Lanham.
• Authors: Lanham, Michael
• Contents: Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: The Basics; Chapter 1: 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 Chapter 2: Convolutional and Recurrent NetworksConvolutional 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; Chapter 3: 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 GANGenerating music via an alternative GAN; Exercises; Summary ; Chapter 4: 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; Chapter 5: Introducing DRL; Reinforcement learning The multi-armed banditContextual 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; Chapter 6: 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; Chapter 7: Agent and the Environment; Exploring the training environment; Training the agent visually; Reverting to the basics; Understanding state Understanding visual stateConvolution and visual state; To pool or not to pool; Recurrent networks for remembering series; Tuning recurrent hyperparameters; Exercises; Summary; Chapter 8: 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; Chapter 9: Rewards and Reinforcement Learning; Rewards and reward functions
• Publisher Details: Birmingham, UK : Packt Publishing
• Publication Date: 2019
• Extent: 1 online resource, illustrations
• Subjects: 006.31; Reinforcement learning; Machine learning; Computer games -- Programming; Neural networks (Computer science); Application software -- Development; Electronic books
• Languages: English
• ISBNs: 9781788998765; 1788998766
• Related ISBNs: 9781788994071
• Notes: Note: Description based on online resource; title from title page (Safari, viewed May 14, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.