This project seeks to develop a new framework and class of algorithms that allow unmanned aerial systems to learn complex multi-agent behavior in simulator environments, then seamlessly transfer their knowledge from simulation to real-world field environments. The team envisions a first responder system where a swarm of autonomous aircraft are virtually trained on how to navigate and cooperate in a simulation of a novel disaster area. The system then transfers the learning gained in the simulation to the real autonomous aircraft swarm. An aircraft deploys a large “mothership” ground station which releases these trained autonomous aircraft to automatically perform time-critical, labor-intensive tasks like surveying disaster areas and locating and identifying survivors.