Forward-Invariant Policy Classes for Safe Reinforcement Learning in Multicopter Control
The framework embeds safety directly into reinforcement learning by creating a finite library of controllers sharing one common mathematical certificate. This guarantees that no matter which controller is used, the system remains within a safe operating region during arbitrary switching. It achieves this by designing the action space so every possible move keeps the system within a predefined safe set.