E-Books →Practical Simulations for Machine Learning Using Synthetic Data for AI

With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating 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.
You'll learn how to
* Design an approach for solving ML and AI problems using simulations with the Unity engine
* Use a game engine to synthesize images for use as training data
* Create simulation environments designed for training deep reinforcement learning and imitation learning models
* Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization
* Train a variety of ML models using different approaches
* Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits