English | 2021 | ISBN: 9781492089926| 116 Pages | EPUB | 10.8 MB
Simulation and synthesis are core parts of the future of AI and machine learning.
Consider: programmers, data scientists, and machine learning eeers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train traditional machine learning models. That's just the bning.
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 ee 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
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