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Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems
We also devise a simulation-guided approach for training NBCs, aiming to achieve tightness in computing precise certified lower and upper bounds.
Robustness Verification of Deep Reinforcement Learning Based Control Systems using Reward Martingales
In this work, we present the first approach for robustness verification of DRL-based control systems by introducing reward martingales, which offer a rigorous mathematical foundation to characterize the impact of state perturbations on system performance in terms of cumulative rewards.
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training
The experimental results on a wide range of benchmarks show that the DNNs trained by using our approach exhibit comparable performance, while the reachability analysis of the corresponding systems becomes more amenable with significant tightness and efficiency improvement over the state-of-the-art white-box approaches.
