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An improved deep reinforcement learning algorithm for cruise missile penetration path planning
Ma Zijie，Gao Jie，Wu Peiyu，Xie Yongjun
School of Electronics and Information Engineering，Beihang University，Beijing 100191，China
Abstract： Aiming at the problem of cruise missile penetration trajectory planning under the threat of dynamic early of warning aircraft radar, an improved deep reinforcement learning intelligent trajectory planning method is proposed. Firstly, aiming at the penetration mission of cruise missiles facing early warning threats, a typical combat scenario is constructed, and a prediction formula of radar detection probability of early warning aircraft is given. On this basis, a reward function that introduces dynamic early warning threats is designed, and the deep deterministic policy gradient algorithm(DDPG) is used to explore the intelligent penetration of cruise missiles. And then, in response to the poor exploration ability of the traditional DDPG algorithm that explores the uncorrelated timing of noise, Ornstein-Uhlenbeck noise is introduced to improve the training efficiency of the algorithm. The simulation results show that the improved DDPG algorithm training convergence time is shorter.
