Under　the　carbon　peak　and　neutrality　targets,　the　large-scale　grid　connection　of　renewable　energy　and　the　operation　of　high　proportion　of　power　electronic　equipment　have　reduced　the　inertia　of　the　system　and　impacted　the　stable　operation　of　the　power　system.　Traditional　transient　stability　analysis　has　some　shortcomings,　such　as　difficulty　in　modeling,　low　calculation　efficiency,　and　being　easy　to　be　disturbed　by　uncertain　factors.　In　recent　years,　reinforcement　learning　has　developed　rapidly.　Deep　reinforcement　learning　combines　the　advantages　of　deep　learning　and　reinforcement　learning,　and　it　can　learn　a　large　number　of　high-dimensional　and　uncertain　data　to　solve　decision-making　problems　in　large-scale　scenes　with　limited　information.　This　paper　first　summarizes　deep　reinforcement　learning.　Next,　the　existing　research　results　of　reinforcement　learning　in　power　system　transient　stability　control　decision-making　are　summarized.　Then,　this　paper　analyzes　the　research　status　and　advantages　of　deep　reinforcement　learning　algorithm　in　power　system　transient　stability　control　decision-making　from　three　aspects　of　system　preventive　control,　system　emergency　control,　and　system　recovery　control,　and　the　existing　problems　in　this　research　direction　are　discussed　in　depth.　Finally,　the　prospects　in　future　technical　developments　and　practical　applications　of　deep　reinforcement　learning　are　put　forward.　©　2023　Science　Press.　All　rights　reserved.