Model-based Reinforcement Learning with Provable Safety Guarantees via Control Barrier Functions
Hongchao Zhang,Zhouchi Li,Andrew Clark,Hongchao Zhang,Zhouchi Li,Andrew Clark
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 s...
