This paper proposes a reinforcement learning (RL) enhanced control strategy for carrier landing of carrier-based aircraft under challenging conditions. A stabilization trajectory using direct side force is developed using nonlinear dynamic inverse (NDI) control methods. To address the systematic uncertainties associated with the landing process, an RL algorithm is employed to optimize the loop gains, thereby enhancing the robustness of the landing controller. Numerical simulation results demonstrate that this approach effectively maintains the aircraft’s attitude and position stability throughout the landing process.