Endo-atmospheric maneuver penetration strategy based on generative adversarial reinforcement learning

An intelligent endo-atmospheric penetration strategy based on generative adversarial reinforcement learning is proposed in this manuscript. Firstly, attack and defense adversarial models are established, and missile maneuver penetration problem is transformed into an optimal control problem, considering penetration, handover position and mid-terminal guidance velocity constraints. Then, Radau Pseudospectral method is adopted to generate data samples considering random perturbations. Furthermore, Generative Adversarial Imitation Learning Combined with Deep Deterministic Policy Gradient method (GAIL-DDPG) is designed, with internal process reward signals constructed to tackle long-term sparse reward in missile manuver penetration problem. Finally, penetration strategy is trained and verified. Simulation shows that using generative adversarial reinforcement learning, with sample library to learn expert experience in training early stage, the proposed method can quickly converge. Also, performance is further optimized with reinforcement learning exploration strategy in the later stage of training. Simulation shows that the proposed method has better engineering application ability compared with traditional reinforcement learning method.