基于多智能体深度强化学习的无人平台箔条干扰末端防御动态决策方法

Chaff centroid jamming of unmanned platform is an important means of missile terminal defense. The intelligent decision-making ability in platform maneuvering and chaff launching is an important factor to determine whether the strategic assets can be protected successfully. The current decision-making methods,such as computational analysis based on mechanism model and space exploration based on heuristic algorithm,have the problems of low degree of intelligence,poor adaptability and slow decision-making speed. A dynamic decision-making method of chaff jamming for terminal defense based on multi-agent deep reinforcement learning is proposed. The problem of cooperative chaff jamming of multi-platform for terminal defense is defined,and a simulation environment is constructed. The missile guidance and fuze model,unmanned jamming platform maneuvering model,chaff diffusion model and centroid jamming model are established. The centroid jamming decision problem is transformed into a Markov decision problem,a decision-making agent is constructed,the state and action spaces are defined,and a reward function is set. The decision-making agent is trained by using the multi-agent proximal policy optimization (MAPPO) algorithm. The simulated results show that the proposed method reduces the training time by 85. 5% and increases the success rate of asset protection by 3. 84 compared with the multi-agent deep deterministic policy gradient (MADDPG) algorithm. Compared with the GA,it reduces the deciding time by 99. 96 % and increases the success rate of asset protection 1. 12.