Schedule　maintenance　activities　for　hydrogen　fuel　cell　vehicles　are　aimed　to　keep　them　in　better　condition　in　long-term　operation.　In　this　paper,　we　develop　a　reinforcement　learning　based　schedule　maintenance　strategy,　which　is　used　to　comprehensively　consider　the　balance　between　safety　and　maintenance　cost,　so　as　to　find　the　optimal　maintenance　strategy.　A　multi-level　framework　is　established　with　remaining　useful　life　of　key　components,　fault　tree　analysis,　and　logistics　cost　model　to　generate　an　exploration　environment　that　can　express　the　operational　stability,　the　failure　rate　of　components,　and　the　cost　of　repair　and　storage.　The　trained　agent　combines　a　deep　neural　network　to　explore　the　optimal　strategy　under　the　dynamic　reliability　of　key　components.　Finite　steps　are　the　constraints.　Safety　rates,　maintenance　cost,　and　episode　of　operation　are　incorporated　into　a　multi-objective　reward　function.　The　hydrogen　supply　circuit　of　fuel　cell　vehicle　is　simulated　via　Python.　The　trained　agent　is　compared　with　traditional　time-based　schedule　maintenance　strategy　and　corrective　maintenance　strategy.　The　results　show　that　the　reinforcement　learning　based　schedule　maintenance　agent　has　optimized　the　total　reward,　cost　control　and　accident　rate　by　77%,　59%　and　5%,　respectively,　compared　with　the　traditional　time-based　schedule　maintenance　strategy.　In　the　safe　operation　management　of　fuel　cell　vehicles,　efficient　and　stable　decision-making　ability　has　been　achieved.　©　2024