Step Wise Violation

Step-wise violation research focuses on developing algorithms that guarantee constraints are met at each step of a sequential decision-making process, particularly in challenging scenarios lacking readily available safe actions. Current research emphasizes online convex optimization and reinforcement learning frameworks, employing novel algorithms designed to minimize both cumulative constraint violations and overall performance loss (e.g., regret). This work is crucial for safety-critical applications like robotics and autonomous driving, where ensuring safety at every step is paramount, and existing methods may be insufficient.