Imitation-Reinforcement Learning Penetration Strategy for Hypersonic Vehicle in Gliding Phase

To enhance the penetration capability of hypersonic vehicles in the gliding phase, an intelligent maneuvering penetration strategy combining imitation learning and reinforcement learning is proposed. Firstly, a reinforcement learning penetration model for hypersonic vehicles is established based on the Markov Decision Process (MDP), with the design of state, action spaces, and composite reward function based on Zero-Effort Miss (ZEM). Furthermore, to overcome the difficulties in training reinforcement learning models, a truncated horizon method is employed to integrate reinforcement learning with imitation learning at the level of the optimization target. This results in the construction of a Truncated Horizon Imitation Learning Soft Actor–Critic (THIL-SAC) intelligent penetration strategy learning model, enabling a smooth transition from imitation to exploration. Finally, reward shaping and expert policies are introduced to enhance the training process. Simulation results demonstrate that the THIL-SAC strategy achieves faster convergence compared to the standard SAC method and outperforms expert strategies. Additionally, the THIL-SAC strategy meets real-time requirements for high-speed penetration scenarios, offering improved adaptability and penetration performance.