As　microservices　architecture　has　steadily　emerged　as　the　prevailing　direction　in　software　system　design,　the　assurance　of　services　within　microservices　systems　has　garnered　increasing　attention.　The　concept　of　intelligent　service　assurance　within　microservices　systems　offers　a　novel　approach　to　addressing　adaptation　challenges　in　complex,　risk-laden　environments.　This　paper　introduces　a　groundbreaking　approach　known　as　the　Reinforcement　Learning　(RL)　Based　Service　Assurance　Method　for　Microservice　Systems　(RL-SAMS),　which　incorporates　the　fundamental　RL　principle　of　＇improving　performance　through　experience＇　into　service　assurance　activities.　Through　the　implementation　of　an　intelligent　service　degradation　mechanism,　the　continuity　of　services　is　ensured.　Within　the　framework　of　our　designed　microservices　system,　two　essential　components　are　introduced:　the　Adapter　Component　(AC)　and　the　RL　Decision-making　Component　(RLDC).　Each　microservice　is　treated　as　an　independent　RL　agent,　resulting　in　the　construction　of　a　multi-agent　RL　decision-making　architecture　that　balances　＇centralized　learning　and　decentralized　decision-making.＇　This　intelligent　decision-making　model　undergoes　training　and　learning,　accumulating　positive　experiences　through　continuous　trial　and　error.　Experimental　cases　demonstrate　that　RL-SAMS　outperforms　the　widely　adopted　Hystrix　across　various　service　risk　scenarios,　particularly　excelling　in　intelligently　critical　service　assurance.　©　2023　Copyright　for　this　paper　by　its　authors.