End-to-end　driving　behavior　decision-making　is　a　research　hotspot　in　the　field　of　driverless　driving.　This　paper　studies　the　end-to-end　lane　change　decision　control　based　on　the　PPO　(Proximal　Policy　Optimization)　deep　reinforcement　learning　algorithm.　First,　an　end-to-end　decision　control　model　based　on　PPO　algorithm　is　established.　The　model　uses　the　information　perceived　from　the　environment　as　the　input　state　and　outputs　the　control　quantity　(acceleration,　braking,　steering).　Training　and　verification　in　the　driving　environment　under　the　TORCS　(The　Open　Racing　Car　Simulator)　platform　show　that　the　model　can　achieve　end-to-end　lane　change　driving　behavior　decision-making.　Finally,　compared　with　the　DDPG　(Deep　Deterministic　Policy　Gradient)　model,　which　is　also　a　deep　reinforcement　learning　method,　the　experimental　results　show　that　the　PPO　lane　change　decision　control　model　has　a　faster　convergence　rate.　©　2023　SPIE.