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
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