Simulation and synthesis are core parts of the future of AI and machine
learning. Consider: programmers, data scientists, and machine learning
engineers can create the brain of a self-driving car without the car.
Rather than use information from the real world, you can synthesize
artificial data using simulations to train traditional machine learning
models. Thatâ s just the beginning.
With this practical book, youâ 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
