Input-to-State Safety for Reinforcement Learning

Summary

This paper presents an off-policy safe reinforcement learning approach for nonlinear dynamical systems under input saturation. It formulates safe exploration as a robust control problem using input-to-state safe control barrier functions, enforced through a quadratic programming optimization, with an adaptive epsilon-tuning law that tightens constraints near safety boundaries. Simulations are reported to demonstrate the framework's efficacy.