Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing

This research introduces a learned model predictive game framework for multi-agent drone racing, enabling drones to develop complex strategies and execute them at high speeds. This advancement holds significant implications for autonomous drone competitions, advanced air mobility systems, and cooperative robotics, where dynamic and strategic interactions are paramount.