Safety-Critical Scenario Generation Using Reinforcement Learning Editing

The author proposes a reinforcement learning approach to generate safety-critical scenarios by sequential editing, addressing challenges in traditional optimization techniques. The core thesis revolves around employing deep reinforcement learning to efficiently explore and generate diverse safety-critical scenarios.