Overtaking　decision　control　in　unmanned　driving　is　a　current　research　hotspot.　Overtaking　operations　can　improve　driving　efficiency,　but　blindly　overtaking　may　cause　traffic　accidents.　A　critical　issue　is　how　to　perform　overtaking　decisions　in　a　specific　driverless　scenario　safely.　In　this　paper,　based　on　Deep　Deterministic　Policy　Gradient　(DDPG)　deep　reinforcement　learning　algorithm,　this　paper　establishes　an　end-to-end　overtaking　decision　control　model　with　continuous　action　output.　The　model　uses　low-cost　sensors　to　obtain　input　state　information　(such　as　vehicle　speed,　position,　road　angle,　etc.)　and　directly　output　vehicle　action　information　(steering,　acceleration,　braking).　The　results　show　that　the　model　can　achieve　end-to-end　unmanned　overtaking　by　training　and　testing　the　vehicle　to　complete　the　overtaking　task　in　the　TORCS　(The　Open　Racing　Car　Simulator)　platform　environment.　Finally,　our　work　compared　with　the　Deep　Q-learning　Network　(DQN)　overtaking　model　with　discrete　action　output,　and　the　experimental　results　show　that　the　DDPG　overtaking　model　has　a　better　overtaking　decision　control　effect.　©　2023　IEEE.