This paper investigates the problem of angular momentum control and planning for control moment gyroscope(CMG) arrays in rigid spacecraft attitude control systems using deep reinforcement learning (DRL). Specifically, a DRL-based angular momentum control strategy is proposed for spacecraft attitude control systems employing multiple CMGs as actuators. The twin-delayed deep deterministic policy gradient (TD3) algorithm is used to perform online learning and policy updates based on environmental feedback. This approach eliminates the need for precise mathematical models and iterative parameter tuning. This enables the CMG system to perform angular momentum planning and facilitates rapid and high-precision spacecraft attitude maneuvers and control through angular momentum exchange. Simulations were performed to analyze spacecraft attitude maneuvers and stabilization under various scenarios, focusing on the angular momentum control process of a pyramidal single gimbal CMG (SGCMG) array. The results demonstrate that the proposed method effectively achieves large-angle attitude maneuvers and stable attitude maintenance, both in ideal conditions and in the presence of nonlinear disturbances. During large-angle maneuvers, the spacecraft’s attitude estimation using MRPs converges in less than 1 min, and the convergence accuracy during attitude-holding reaches the order of 10⁻³. Moreover, the approach fully leverages the output characteristics of the CMG system and achieves robust performance and accuracy, even under conditions with significant noise and disturbances.