AINeutralarXiv – CS AI · 6h ago6/10
🧠
Robust Shielding for Safe Reinforcement Learning
Researchers introduce a novel shielding framework for reinforcement learning agents that guarantees safety without requiring prior knowledge of system dynamics. By combining robust MDPs with linear temporal logic specifications and PAC learning guarantees, the approach enables the creation of minimally restrictive safety shields for unknown environments while maintaining strong performance as data accumulates.