Model-free Safe Control for Zero-Violation Reinforcement Learning
Proceedings of the 5th Conference on Robot Learning, PMLR 164:784-793, 2022.
While deep reinforcement learning (DRL) has impressive performance in a variety of continuous control tasks, one critical hurdle that limits the application of DRL to physical world is the lack of safety guarantees. It is challenging for DRL agents to persistently satisfy a hard state constraint (known as the safety specification) during training. On the other hand, safe control methods with safety guarantees have been extensively studied. However, to synthesize safe control, these methods require explicit analytical models of the dynamic system; but these models are usually not available in DRL. This paper presents a model-free safe control strategy to synthesize safeguards for DRL agents, which will ensure zero safety violation during training. In particular, we present an implicit safe set algorithm, which synthesizes the safety index (also called the barrier certificate) and the subsequent safe control law only by querying a black-box dynamic function (e.g., a digital twin simulator). The theoretical results indicate the implicit safe set algorithm guarantees forward invariance and finite-time convergence to the safe set. We validate the proposed method on the state-of-the-art safety benchmark Safety Gym. Results show that the proposed method achieves zero safety violation and gains $ 95% \pm 9%$ cumulative reward compared to state-of-the-art safe DRL methods. Moreover, it can easily scale to high-dimensional systems.