Value Functions are Control Barrier Functions: Verification of Safe Policies using Control Theory (original) (raw)
Related papers
2021
In this paper, we propose a safe reinforcement learning approach to synthesize deep neural network (DNN) controllers for nonlinear systems subject to safety constraints. The proposed approach employs an iterative scheme where a learner and a verifier interact to synthesize safe DNN controllers. The learner trains a DNN controller via deep reinforcement learning, and the verifier certifies the learned controller through computing a maximal safe initial region and its corresponding barrier certificate, based on polynomial abstraction and bilinear matrix inequalities solving. Compared with the existing verification-in-the-loop synthesis methods, our iterative framework is a sequential synthesis scheme of controllers and barrier certificates, which can learn safe controllers with adaptive barrier certificates rather than user-defined ones. We implement the tool SRLBC and evaluate its performance over a set of benchmark examples. The experimental results demonstrate that our approach eff...
Safe Model-Based Reinforcement Learning Using Robust Control Barrier Functions
ArXiv, 2021
Reinforcement Learning (RL) is effective in many scenarios. However, it typically requires the exploration of a sufficiently large number of state-action pairs, some of which may be unsafe. Consequently, its application to safety-critical systems remains a challenge. Towards this end, an increasingly common approach to address safety involves the addition of a safety layer that projects the RL actions onto a safe set of actions. In turn, a challenge for such frameworks is how to effectively couple RL with the safety layer to improve the learning performance. In the context of leveraging control barrier functions for safe RL training, prior work focuses on a restricted class of barrier functions and utilizes an auxiliary neural net to account for the effects of the safety layer which inherently results in an approximation. In this paper, we frame safety as a differentiable robust-control-barrier-function layer in a model-based RL framework. As such, this approach both ensures safety ...