The design and development of unmanned aerial vehicle (UAV) controllers are complex system engineering. The traditional development method based on code programming has the disadvantages of difficult development, long cycle, and high error rate. Although the intelligent flight control algorithm based on reinforcement learning has achieved good performance in simulation, it still lacks a complete development system in practice. This study presents a model-based development system for intelligent flight control, applying the reinforcement learning algorithm to the embedded development and deployment for flight control with modular programming and automatic code generation technologies. The system is equipped for the training simulation, testing, and hardware deployment of the reinforcement learning algorithm, and it is expected to improve the deployment speed of intelligent control algorithms represented by reinforcement learning and to reduce the development difficulty of intelligent flight control systems.