EurekAlert  August 10, 2020 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 resulting in increased computational complexity and communication requirements. To address this problem a team of researchers in the US (Oklahoma State University, Army Research Laboratory, North Carolina State University) is developing 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. It decomposes the global control objective into multiple hierarchies and a broad […]