CHARLOTTESVILLE, VA—Engineers at the University of Virginia have developed an AI-driven system that could transform how factories operate. Using Multi-Agent Reinforcement Learning (MARL), they created a more efficient way to optimize manufacturing systems, improving both speed and quality while reducing waste.

The system integrates AI agents that work together to optimize production processes. By coordinating multiple agents to manage tasks in real time, it adjusts automatically, learning and improving performance over time. The research was conducted in collaboration with General Motors, which provided real-world applications for the AI system.

“We are addressing the complexity of modern manufacturing,” says Qing “Cindy” Chang, Ph.D., a professor of mechanical and aerospace engineering at the University of Virginia who is heading up the project. “Instead of optimizing individual processes in isolation, our system looks at the big picture, coordinating everything at once.