The　widespread　integration　of　distributed　renewable　energy　sources　has　brought　a　series　of　problems　to　the　operation　of　distribution　networks,　including　voltage　violations　and　increase　in　network　losses.　This　paper　proposes　a　model-free　voltage　control　strategy　based　on　multi-agent　reinforcement　learning.　By　coordinating　photovoltaic　inverters,　distributed　energy　storages,　and　soft　open　points,　the　strategy　aims　to　reduce　network　losses　and　eliminate　voltage　violations.　To　tackle　the　problem　that　traditional　voltage　control　strategies　have　strong　dependence　on　accurate　distribution　network　model　parameters,　a　power　flow　surrogate　model　based　on　Gaussian　process　regression　is　proposed.　The　model　enables　offline　training　and　online　application　through　interactions　between　multi-agents　and　the　power　flow　surrogate　model.　Additionally,　a　multi-agent　deep　reinforcement　learning　algorithm　based　on　random　weighted　triple　Q-learning　is　proposed　to　further　reduce　the　overestimation　and　underestimation　errors　of　the　soft　actor-critic　algorithm.　The　proposed　method　improves　the　algorithm　exploration　capability　and　results　quality.　Finally,　simulation　results　on　the　IEEE　33-node　system　verify　the　effectiveness　of　the　proposed　method　in　solving　the　distributed　voltage　control　problem　of　distribution　networks.　©　2024　Science　Press.　All　rights　reserved.