Model-based Reinforcement Learning with Provable Safety Guarantees via Control Barrier Functions

Safety is a critical property in applications including robotics, transportation, and energy. Safety is especially challenging in reinforcement learning (RL) settings, in which uncertainty of the system dynamics may cause safety violations during exploration. Control Barrier Functions (CBFs), which enforce safety by constraining the control actions at each time step, are a promising approach for safety-critical control. This technique has been applied to ensure the safety of model-free RL, however, it has not been integrated into model-based RL. In this paper, we propose Uncertainty-Tolerant Control Barrier Functions (UTCBFs), a new class of CBFs to incorporate model uncertainty and provide provable safety guarantees with desired probability. Furthermore, we introduce an algorithm for model-based RL to guarantee safety by integrating CBFs with gradient-based policy search. Our approach is verified through a numerical study of a cart-pole system and an inverted pendulum system with comparison to state-of-the-art RL algorithms.