Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics

Understand

This paper proposes a framework for safe reinforcement learning that can handle stochastic nonlinear dynamical systems.

• We focus on the setting where the nominal dynamics are known, and are subject to additive stochastic disturbances with known distribution.
• Our goal is to ensure the safety of a control policy trained using reinforcement learning, e.g., in a simulated environment.
• We build on the idea of model predictive shielding (MPS), where a backup controller is used to override the learned policy as needed to ensure safety.