Practical Simulations for Machine Learning (Second Early Release)

With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, with a focus on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential.
With this deeply practical book, you'll learn how to:

Design an approach for solving ML and AI problems using simulations
Use a game engine to synthesize images for use as training data
Create simulation environments designed for training deep reinforcement learning and imitation learning
Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization (PPO) and soft actor-critic (SAO)
Train ML models locally, concurrently, and in the cloud
Use PyTorch, TensorFlow, the Unity ML-Agents and Perception Toolkits to enable ML tools to work with industry-standard game development tools