Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages

Overview

Rule-based safety cages supervise reinforcement-learning longitudinal control by constraining actions and guiding exploration. Comparisons with unconstrained agents and different model parameters evaluate training safety, convergence, and the resulting highway-driving policies.