Shielded Reinforcement Learning for Hybrid Systems

Safe and optimal controller synthesis for switched-controlled hybrid systems, which combine differential equations and discrete changes of the system's state, is known to be intricately hard. Reinforcement learning has been leveraged to construct near-optimal controllers, but their behavior is not guaranteed to be safe, even when it is encouraged by reward engineering. One way of imposing safety to a learned controller is to use a shield, which is correct by design. However, obtaining a shield for non-linear and hybrid environments is itself intractable. In this paper, we propose the construction of a shield using the so-called barbaric method, where an approximate finite representation of an underlying partition-based two-player safety game is extracted via systematically picked samples of the true transition function. While hard safety guarantees are out of reach, we experimentally demonstrate strong statistical safety guarantees with a prototype implementation and UPPAAL STRATEGO. Furthermore, we study the impact of the synthesized shield when applied as either a pre-shield (applied before learning a controller) or a post-shield (only applied after learning a controller). We experimentally demonstrate superiority of the pre-shielding approach. We apply our technique on a range of case studies, including two industrial examples, and further study post-optimization of the post-shielding approach.

It's Time to Play Safe: Shield Synthesis for Timed Systems

Erroneous behaviour in safety critical real-time systems may inflict serious consequences. In this paper, we show how to synthesize timed shields from timed safety properties given as timed automata. A timed shield enforces the safety of a running system while interfering with the system as little as possible. We present timed post-shields and timed pre-shields. A timed pre-shield is placed before the system and provides a set of safe outputs. This set restricts the choices of the system. A timed post-shield is implemented after the system. It monitors the system and corrects the system's output only if necessary. We further extend the timed post-shield construction to provide a guarantee on the recovery phase, i.e., the time between a specification violation and the point at which full control can be handed back to the system. In our experimental results, we use timed post-shields to ensure the safety in a reinforcement learning setting for controlling a platoon of cars, during the learning and execution phase, and study the effect.