Artificial Intelligence And Simulation

Practical Simulations for Machine Learning

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.That's just the beginning. 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

Artificial Intelligence, Simulation, and Modeling

This interdisciplinary approach to computer modeling addresses both traditional simulationists seeking the greater representational flexibility and ease of use that AI techniques offer, and computer scientists seeking the greater power and realism that rigorous simulation techniques can provide. First section reveals the theoretical underpinnings of AI and simulation. Second section describes application of simulation techniques to current problems in AI research. Third section discusses application of AI methods to simulation.

Artificial Intelligence and Simulation