6 papers
Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning
Astrid Horn Brorholt, Maris F. L. Galesloot, Nils Jansen +2
Probabilistic shielding is a technique for safe reinforcement learning (RL). Typically, a static observer -- called the shield -- constrains the learning agent's actions to those f…
Robust Zonotopic Control
Fouzi Tabouri, Kim Guldstrand Larsen, Christian Schilling
We propose a zonotopic framework for synthesizing a single robust state feedback controller that is certified to stabilize every plant inside a matrix zonotope, describing linearly…
Uppaal Coshy: Automatic Synthesis of Compact Shields for Hybrid Systems
Asger Horn Brorholt, Andreas Holck Høeg-Petersen, Peter Gjøl Jensen +4
We present Uppaal Coshy, a tool for automatic synthesis of a safety strategy -- or shield -- for Markov decision processes over continuous state spaces and complex hybrid dynamics.…
Compositional Shielding and Reinforcement Learning for Multi-Agent Systems
Asger Horn Brorholt, Kim Guldstrand Larsen, Christian Schilling
Deep reinforcement learning has emerged as a powerful tool for obtaining high-performance policies. However, the safety of these policies has been a long-standing issue. One promis…
CommonUppRoad: A Framework of Formal Modelling, Verifying, Learning, and Visualisation of Autonomous Vehicles
Rong Gu, Kaige Tan, Andreas Holck Høeg-Petersen +2
Combining machine learning and formal methods (FMs) provides a possible solution to overcome the safety issue of autonomous driving (AD) vehicles. However, there are gaps to be bri…
Efficient Shield Synthesis via State-Space Transformation
Asger Horn Brorholt, Andreas Holck Høeg-Petersen, Kim Guldstrand Larsen +1
We consider the problem of synthesizing safety strategies for control systems, also known as shields. Since the state space is infinite, shields are typically computed over a finit…