Distributed　generation　(DG)　represented　by　wind　turbines　and　photovoltaic　systems　has　been　extensively　connected　to　the　power　distribution　network　(DN).　However,　the　random　fluctuations　of　DG　pose　new　challenges　to　the　safety,　stability,　and　economic　performance　of　DN,　while　distribution　network　reconfiguration　(DNR)　technology　can　alleviate　this　problem　to　some　extent.　Traditional　heuristic　algorithms　are　difficult　to　deal　with　uncertainties　in　the　source-load　and　the　increasing　complexity　of　DN.　Therefore,　this　paper　proposes　an　active　DNR　method　based　on　a　model-free　multi-agent　deep　deterministic　policy　gradient　reinforcement　learning　framework　(MADDPG).　Firstly,　the　number　of　fundamental　loops　in　the　distribution　network　are　determined　and　agent　for　each　fundamental　loop　are　deployed.　Each　agent　has　an　actor　and　a　critic　network,　which　can　control　operations　of　the　branch　switches　in　the　loop.　Next,　a　mathematical　model　of　DNR　will　be　constructed.　Then,　a　MADDPG　training　framework　for　distribution　network　reconfiguration　is　built,　which　adopts　centralized　training　and　distributed　execution.　Finally,　the　simulation　cases　are　performed　on　an　improved　IEEE　33-bus　power　system　to　prove　the　effectiveness　of　MADDPG　algorithm.　The　results　illustrate　that　MADDPG　algorithm　can　improve　the　economic　and　stability　performance　of　the　distribution　network　to　some　extent,　demonstrating　the　effectiveness　of　the　proposed　approach.　　©　2023　IEEE.