The stability of dual active bridge converter (DAB) is threatened when feeding the constant power loads (CPLs). This article proposes a deep reinforcement learning-based backstepping control strategy to solve this problem. First, a nonlinear disturbance observer is adopted to estimate the large-signal nonlinear disturbance. Then, a backstepping controller is used to stabilize the voltage response of the DAB under the large-signal disturbance. Finally, a compensation method based on deep reinforcement learning is developed to intelligently minimize output voltage tracking error and improve the operating efficiency of the system. The proposed controller can guarantee system stability under the large-signal disturbance of the CPL and achieve a fast dynamic response with accurate voltage tracking; it is more adaptive by using the deep reinforcement learning technique through the learning of its neural networks. The effectiveness of the proposed controller is verified by experiments.