The complex aerodynamic changes of the tilt-rotor UAV (TRUAV) in the transition process show strong nonlinearity, which brings a great impact on the stability of the vehicle attitude. This study aims to design a PPO-based RL controller for attitude control in the transition process. A reinforcement-learning PPO approach is used to learn the control strategy by interacting directly with the environment. And the reward function is designed and improved for the transition process. The performance of the proposed controller is tested and compared by simulation. The results show that the PPO algorithm is more suitable for the tilt-rotor transition process control than the A2C algorithm. Our proposed reward function improves the attitude control performance and the designed RL controller has good adaptability to changes in the takeoff weight, the diagonal wheelbase and the tilt rate. This study highlights the effectiveness and potential of reinforcement learning for tilt-rotor UAV transition process attitude control. These findings contribute to the advancement of autonomous flight systems by providing insights into the application of reinforcement learning algorithms. These results have important implications for the development of intelligent flight control systems and could guide future research in this area.