Network　failures,　especially　link　failures,　happen　frequently　in　Internet　Service　Provider　(ISP)　networks.　When　link　failures　occur,　the　routing　policies　need　to　be　re-computed　and　failure　recovery　usually　takes　a　few　minutes,　which　degrades　the　network　performance　to　a　great　extent.　Therefore,　a　proper　failure　recovery　scheme　that　can　realize　a　fast　and　timely　routing　policy　computation　needs　to　be　designed.　In　this　paper,　we　propose　FRRL,　a　Reinforcement　Learning　(RL)　approach　to　intelligently　perceive　network　failures　and　timely　compute　the　routing　policy　for　improving　the　network　performance　when　link　failure　happens.　Specifically,　to　perceive　the　link　failures,　we　design　a　Topology　Difference　Vector　(TDV)　encoder　module　in　FRRL　for　encoding　the　topology　structure　with　link　failures.　To　efficiently　compute　the　routing　policy　when　link　failures　happen,　we　integrate　the　TDV　in　the　agent　training　for　learning　the　map　between　the　encoded　failure　topology　structure　and　routing　policies.　To　evaluate　the　performance　of　our　proposed　method,　we　conduct　experiments　on　three　network　topologies　and　the　experimental　results　demonstrate　that　our　proposed　method　has　superior　performance　when　link　failures　happen　compared　to　other　methods.