Deep reinforcement learning has received a significant amount of research attention recently as it enables autonomous systems, such as autonomous cars or drones, to make decisions in real time. However, researchers are still grappling with the computational demands of learning by such controllers as well as the need to guarantee their safety and transferability into novel environments.
Yiannis Kantaros, an assistant professor of electrical and systems engineering at the McKelvey School of Engineering at Washington University in St. Louis, has received a $413,694 grant from the National Science Foundation to address these challenges. Kantaros aims to develop data-efficient machine learning methods that can create safe and verified controllers that allow autonomous robots to satisfy complex mission and safety requirements that may be different from the ones used during the robot’s training phase.
Read more on the McKelvey School of Engineering website.
