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Learnable Model Veriﬁcation through Reinforcement Learning

Formal Veriﬁcation of Cyber-Physical Systems provides safety justiﬁcation for the modeling of the environment. However, an accurate model is usually hard to acquire due to the exquisite nature of physics in the real world. With the aid of model-based reinforcement learning, this paper proposes an approach that updates model parameters and provides formal veriﬁcation via differential dynamic logic (d L ). We present the learning process for a series of common reinforcement learning tasks in Cyber-Physical Systems and show that the safety of the model is preserved by proving the parameterized model.