Web: http://arxiv.org/abs/2201.12243

Jan. 31, 2022, 2:11 a.m. | Yixuan Wang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu

cs.LG updates on arXiv.org arxiv.org

In model-based reinforcement learning for safety-critical control systems, it
is important to formally certify system properties (e.g., safety, stability)
under the learned controller. However, as existing methods typically apply
formal verification \emph{after} the controller has been learned, it is
sometimes difficult to obtain any certificate, even after many iterations
between learning and verification. To address this challenge, we propose a
framework that jointly conducts reinforcement learning and formal verification
by formulating and solving a novel bilevel optimization problem, which is …

arxiv learning optimization reinforcement learning verification

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