Safety and Stability Preserving Reinforcement Learning

While reinforcement algorithms have achieved notable successes recently, the use of such approaches in controlling real physical systems is not really prevalent. The primary reason for this is the lack guarantees regarding safety and stability of the physical system in classical RL algorithms. In this project, we will explore ideas from classical control theory as well as more recent advances in deep learning to develop more robust algorithms deployable on physical systems.