ADELPHI, Md. — Army researchers developed a reinforcement learning approach that will allow swarms of unmanned aerial and ground vehicles to optimally accomplish various missions while minimizing performance uncertainty.
Swarming is a method of operations where multiple autonomous systems act as a cohesive unit by actively coordinating their actions.
Army researchers said future multi-domain battles will require swarms of dynamically coupled, coordinated heterogeneous mobile platforms to overmatch enemy capabilities and threats targeting U.S. forces.
The Army is looking to swarming technology to be able to execute time-consuming or dangerous tasks, said Dr. Jemin George of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory.
“Finding optimal guidance policies for these swarming vehicles in real-time is a key requirement for enhancing warfighters’ tactical situational awareness, allowing the U.S. Army to dominate in a contested environment,” George said.
Reinforcement learning provides a way to optimally control uncertain agents to achieve multi-objective goals when the precise model for the agent is unavailable; however, the existing reinforcement learning schemes can only be applied in a centralized manner, which requires pooling the state information of the entire swarm at a central learner. This drastically increases the computational complexity and communication requirements, resulting in unreasonable learning time, George said.
In order to solve this issue, in collaboration with Prof. Aranya Chakrabortty from North Carolina State University and Prof. He Bai from Oklahoma State University, George created a research effort to tackle the large-scale, multi-agent reinforcement learning problem. The Army funded this effort through the Director’s Research Award for External Collaborative Initiative, a laboratory program to stimulate and support new and innovative research in collaboration with external partners.
The main goal of this effort is to develop a theoretical foundation for data-driven optimal control for large-scale swarm networks, where control actions will be taken based on low-dimensional measurement data instead of dynamic models.
The current approach is called Hierarchical Reinforcement Learning, or HRL, and it decomposes the global control objective into multiple hierarchies – namely, multiple small group-level microscopic control, and a broad swarm-level macroscopic control.
“Each hierarchy has its own learning loop with respective local and global reward functions,” George said. “We were able to significantly reduce the learning time by running these learning loops in parallel.”
