Reinforcement Learning

Reinforcement Learning enables an agent to learn a control policy through interaction with the environment, without requiring an explicit model of the system. Our research investigates how reinforcement learning can be made effective and reliable for real control applications, focusing on sample efficiency, safety and stability. Applications include autonomous robot navigation and pose regulation, as well as automated insulin delivery, where learned policies must satisfy stringent safety requirements.