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Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games /
This book will give you an in-depth view of the potential of deep learning and neural networks in game development. You will also learn to use neural nets combined with reinforcement learning for new types of game AI.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- 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