A new shielding framework for robust Markov decision processes (RMDPs) enhances the safety of reinforcement learning agents by ensuring compliance with safety standards even in unknown environments. This approach combines robust transition probability modeling with existing sampling methods, promising high-confidence safety guarantees while maintaining performance efficiency.