[Objective] In the space vector modulation direct torque control (SVM-DTC) of brushless doubly-fed machine (BDFM), inner-loop torque and flux proportional-integral (PI) controllers are commonly used. However, fixed PI parameters are difficult to adapt to the system’s nonlinearity and frequently varying operating conditions, making it challenging to maintain optimal performance across all operating points. To address this problem, a deep reinforcement learning (DRL)-based SVM-DTC strategy is proposed to eliminate the dependence on manual parameter tuning and improve the system’s dynamic performance and robustness. [Methods] The twin delayed deep deterministic policy gradient (TD3) algorithm was adopted to construct and train an Actor-Critic neural network, which replaced the inner-loop torque and flux PI controllers. The state space consisted of estimated torque, estimated flux, torque error, flux error, integral of torque error, integral of flux error, speed reference, and speed feedback. The action space was defined as the d-q axis components of the control winding reference voltage vector. Meanwhile, a reward function was designed to integrate torque ripple suppression, and flux deviation penalty, guiding the TD3-agent to learn optimal voltage decisions in a continuous action space. [Results] Simulations were carried out under typical operating conditions such as speed step change and sudden load variation to compare the performance of the proposed DRL-based SVM-DTC strategy and the traditional SVM-DTC strategy. The simulation results showed that the proposed method achieved basically consistent speed control performance with the traditional SVM-DTC strategy. [Conclusion] The well-trained Actor network can directly generate the control winding reference voltages according to the system’s real-time states, obviating the parameter tuning process of the inner-loop torque and flux PI controllers. The TD3-based SVM-DTC strategy can achieve high-quality control of torque and flux for BDFM, exhibiting excellent dynamic response and robustness.