With　the　rapid　development　of　dual-carbon　targets,　many　distributed　power　sources,　represented　by　wind　power　and　photovoltaics,　are　being　connected　to　distribution　networks.　This　will　further　exacerbate　the　intermittency　and　volatility　of　power　output.　Dynamic　reconfiguration　of　active　distribution　networks　constitutes　a　complex,　high-dimensional,　mixed-integer,　nonlinear,　and　stochastic　optimization　problem.　Traditional　algorithms　exhibit　numerous　shortcomings　in　addressing　this　issue.　By　integrating　the　advantages　of　both　deep　learning　and　reinforcement　learning,　the　deep　reinforcement　learning　algorithm　is　highly　suitable　for　formulating　dynamically　reconfigurable　strategies　for　active　distribution　networks,　which　are　currently　of　great　concern.　This　paper　first　summarizes　the　characteristics　of　the　active　distribution　network　of　the　new　generation　power　system,　and　analyzes　the　progress　and　challenges　of　the　current　research　on　the　dynamic　reconfiguration　of　the　active　distribution　network　in　mathematical　models.　Secondly,　the　coding　method　of　the　distribution　network　dynamic　reconfiguration　is　discussed,　and　the　deep　reinforcement　learning　algorithm　is　systematically　reviewed.　Furthermore,　the　shortcomings　of　the　existing　algorithms　in　dealing　with　the　dynamic　reconfiguration　of　the　active　distribution　network　are　analyzed,　and　the　research　status　and　advantages　of　the　deep　reinforcement　learning　algorithm　in　the　dynamic　reconfiguration　of　the　active　distribution　network　are　summarized.　Finally,　the　future　research　directions　for　the　dynamic　reconfiguration　of　active　distribution　networks　are　presented.　©　2025　Science　Press.　All　rights　reserved.