基于深度强化学习的舰船导弹目标分配方法OA北大核心CSTPCD

To effectively solve the missile-target allocation problem of the naval ship in the case of confrontation,this study proposes a deep reinforcement learning algorithm combining attention mechanism.First,we construct a mathematical model for multi-type missiles of the naval ship and design the Markov decision-making process considering the situation of multi-times target interception.After that,the policy network is constructed based on the encoder-decoder architecture,in which targets are encoded combined with the multi-head attention mechanism and the reasonable missile-target allocation scheme is generated in the decoder according to integrated global and local embedding information.Finally,we conduct simulation experiments are carried out on the profit of missile-target allocation schemes,computation time,and the training process of the policy network.The experimental results show that our algorithm can engender missile-target allocation schemes with higher profit compared to baselines,the computation time in large-scale problems is reduced by 10％～94％,and it converges fast and stably.