In　case　of　faults　or　severe　disturbances,　the　power　system　will　enter　an　emergency　operation　state.　After　the　system　instability　is　detected,　oscillation　and　blackout　will　occur　in　the　system　if　effective　control　measures　are　not　taken　in　time.　Generator　tripping　control　(GTC)　is　the　most　effective　emergency　control　measure.　In　view　of　the　mismatch　between　the　traditional　GTC　algorithm　and　the　transient　stability　assessment　method　based　on　machine　learning,　a　new　real-time　GTC　method　is　needed.　In　this　paper,　a　three-part　control　framework　is　designed　for　the　GTC　problem.　The　control　agent　is　endowed　with　decision-making　ability　by　interacting　with　the　simulation　environment　in　the　offline　pre-learning　part.　Then　the　trained　agent　is　transplanted　to　the　online　application　which　can　help　system　operators　make　decisions.　Meanwhile,　the　agent　is　updated　with　real　data　to　be　better　adapted　to　the　actual　system　in　the　online　learning　part.　A　deep　reinforcement　learning　algorithm,　deep　deterministic　policy　gradient　(DDPG)　is　employed　to　train　the　control　agent　in　this　framework.　A　modified　DDPG　algorithm　and　the　corresponding　reward　function　are　designed　for　the　GTC　problem.　Convolution　neural　network　(CNN)　is　added　to　the　DDPG　network,　by　which　the　training　time　of　the　agent　is　shortened　and　the　generalization　ability　of　the　algorithm　is　improved.　Trained　with　simulation　data　and　real　system　experience,　the　control　agent　can　determine　control　strategies　timely　according　to　the　system　operating　conditions.　Simulation　results　on　the　IEEE-39　bus　system　and　the　realistic　regional　power　system　of　Eastern　China　show　the　effectiveness,　generalizability,　and　timeliness　of　the　decision　algorithm.　©　2022　Elsevier　Ltd