The　end-to-end　driving　decision　making　is　a　research　hotspot　in　the　field　of　autonomous　driving.　This　paper　studies　the　end-to-end　driving　decision　of　continuous　action　output　based　on　DDPG　(Deep　Deterministic　Policy　Gradient)　deep　reinforcement　learning　algorithm.　First,　an　end-to-end　decision-making　control　model　based　on　DDPG　algorithm　is　established.　The　model　outputs　the　continuous　control　quantity　of　vehicle　driving　action　(acceleration,　braking,　steering)　according　to　the　continuously　acquired　perception　information　(such　as　vehicle　angle,　vehicle　speed,　road　distance,　etc.　)　as　the　input　state.　Then,　the　model　is　trained　and　verified　in　different　driving　environments　on　the　platform　of　TORCS　(The　Open　Racing　Car　Simulator).　The　results　show　that　the　model　can　realize　the　end-to-end　decision-making　of　autonomous　driving.　At　last,　it　is　compared　with　DQN(Deep　Q-Learning　Network)　model　of　discrete　action　output.　The　experimental　results　show　that　DDPG　model　has　better　decision　control　effect.　©　2020,　Chinese　Institute　of　Electronics.　All　right　reserved.