Abstract: The development of advanced autonomous flight control for unmanned aerial vehicles is the goal of military powers, and the intelligent flight control is the foundation for the intelligent air combat of the vehicle. Reinforcement learning provides a novel and general controller design paradigm that is adaptive, optimized, model-free and widely applicable, and it is a promising way for the intelligent control. In contrast to the 3 degree-of-freedom （DOF）flight, the 6-DOF motion better describes the aircraft real flight. However, due to the nonlinearity of the dynamics and complexity of the aerodynamics, the implementation of the 6-DOF flight intelligent control for the fixed-wing aircraft is difficult. Based on the multiple continuous states input and multiple continuous action output deep reinforcement learning, the end-to-end integrated intelligent control for the cruise flight, directly from the vehicle flight states to the aero-surfaces and thrust control, is developed for the full-scaled fixed-wing aircraft. It avoids the artificiall trajectory and attitude loop separation in the classic controller design. By introducing the error of the yaw angle as the input, the stable cruise flight with nearly zero sideslip is achieved and the developed controller is applicable to different cruise tasks, which is useful for the future research on the air combat intelligent decision-making.