Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2111.07695v1
• Date: Mon, 15 Nov 2021 12:05:44 GMT
• Title: Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
• Authors: Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen
• Abstract summary: A valid safety certificate is an energy function indicating that safe states are with low energy. Existing learning-based studies treat the safety certificate and the safe control policy as prior knowledge to learn the other. This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
• Score: 7.658716383823426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety is the major consideration in controlling complex dynamical systems using reinforcement learning (RL), where the safety certificate can provide provable safety guarantee. A valid safety certificate is an energy function indicating that safe states are with low energy, and there exists a corresponding safe control policy that allows the energy function to always dissipate. The safety certificate and the safe control policy are closely related to each other and both challenging to synthesize. Therefore, existing learning-based studies treat either of them as prior knowledge to learn the other, which limits their applicability with general unknown dynamics. This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL. We do not rely on prior knowledge about either an available model-based controller or a perfect safety certificate. In particular, we formulate a loss function to optimize the safety certificate parameters by minimizing the occurrence of energy increases. By adding this optimization procedure as an outer loop to the Lagrangian-based constrained reinforcement learning (CRL), we jointly update the policy and safety certificate parameters and prove that they will converge to their respective local optima, the optimal safe policy and a valid safety certificate. We evaluate our algorithms on multiple safety-critical benchmark environments. The results show that the proposed algorithm learns provably safe policies with no constraint violation. The validity or feasibility of synthesized safety certificate is also verified numerically.

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