Summary
As autonomous systems like self-driving cars and delivery robots become more common, making sure that they operate safely is critical. These systems often learn how to act using reinforcement learning (RL). While powerful, RL methods typically do not guarantee safety, which limits their use in the real world. A common approach towards capturing safety in RL is ensuring the satisfaction of safety constraints. However, verifying that a learned RL controller never leads to any constraint violation is in general a nontrivial problem due to the black-box nature of such controllers. In this project,
