Reinforcement learning-based missile terminal guidance of maneuvering targets with decoys
Tianbo Deng, Hao Huang, Yangwang Fang, Jie Yan, Haoyu Cheng
Abstract
In this paper, a missile terminal guidance law based on a new Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to intercept a maneuvering target equipped with an infrared decoy. First, to deal with the issue that the missile cannot accurately distinguish the target from the decoy, the energy center method is employed to obtain the equivalent energy center (called virtual target) of the target and decoy, and the model for the missile and the virtual decoy is established. Then, an improved DDPG algorithm is proposed based on a trusted-search strategy, which significantly increases the train efficiency of the previous DDPG algorithm. Furthermore, combining the established model, the network obtained by the improved DDPG algorithm and the reward function, an intelligent missile terminal guidance scheme is proposed. Specifically, a heuristic reward function is designed for training and learning in combat scenarios. Finally, the effectiveness and robustness of the proposed guidance law are verified by Monte Carlo tests, and the simulation results obtained by the proposed scheme and other methods are compared to further demonstrate its superior performance.