The　frequent　occurrence　of　system　failures　in　hydrogen　energy　is　a　significant　impediment　to　its　development　and　widespread　adoption.　Currently,　the　field　of　hydrogen　energy　safety　monitoring　is　rapidly　advancing,　yet　few　researchers　have　proposed　appropriate　solutions　to　address　system　failures.　This　challenge　arises　from　the　complex　and　interconnected　nature　of　hydrogen　energy　systems.　Furthermore,　unlike　other　energy　sources,　hydrogen　is　highly　combustible　and　explosive,　and　susceptible　to　leaks,　necessitating　the　consideration　of　multiple　factors.　In　this　context,　reinforcement　learning,　a　machine　learning　control　technology　renowned　for　its　robust　decision-making　abilities,　has　garnered　considerable　interest　among　researchers.　To　date,　however,　reinforcement　learning　has　not　been　applied　in　the　field　of　hydrogen　safety.　This　paper　provides　a　concise　introduction　to　several　commonly　used　reinforcement　learning　algorithms,　analyzes　the　various　fault　types　and　fault　diagnosis　modes　of　hydrogen　energy,　and　highlights　the　adequacy　and　necessity　of　reinforcement　learning　in　the　field　of　hydrogen　energy.　Furthermore,　this　paper　outlines　a　new　research　direction　that　can　pave　the　way　for　future　developments　in　hydrogen　safety.　©　2023　IEEE.