Certifiable Reachability Learning Using a New Lipschitz Continuous Value Function

Understand

We propose a new reachability learning framework for high-dimensional nonlinear systems, focusing on reach-avoid problems.

• These problems require computing the reach-avoid set, which ensures that all its elements can safely reach a target set despite disturbances within pre-specified bounds.
• Our framework has two main parts: offline learning of a newly designed reachavoid value function, and post-learning certification.
• Compared to prior work, our new value function is Lipschitz continuous and its associated Bellman operator is a contraction mapping, both of which improve the learning performance.