Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems

Understand

In this paper, we develop a method for learning a control policy guaranteed to satisfy an affine state constraint of high relative degree in closed loop with a black-box system.

• Previous reinforcement learning (RL) approaches to satisfy safety constraints either require access to the system model, or assume control affine dynamics, or only discourage violations with reward shaping.
• Only recently have these issues been addressed with POLICEd RL, which guarantees constraint satisfaction for black-box systems.
• However, this previous work can only enforce constraints of relative degree 1.