Abstract: To improve the precision of coordinated control between reactor power and steam generator water levels in nuclear power plants, a multi-agent reinforcement learning coordination control framework based on TD3 (Twin Delayed Deep Deterministic Policy Gradient) is proposed in this study, in which various subtasks are assigned to the corresponding agents, and these agents cooperate with each other to accurately coordinate the reactor power and steam generator water levels. Through a series of simulation experiments, the effectiveness and superiority of the framework were verified. The experimental results demonstrated that the multi-agent control framework significantly improves the control speed and stability under various power switching conditions, with overshoot and control time outperforming traditional PID controllers. Additionally, the framework exhibits outstanding generalization ability in new and untrained conditions. This research indicates that the multi-agent reinforcement learning control framework can effectively improve the coordination control accuracy of nuclear reactor systems.