Cooperative Strike Target Assignment Algorithm Based on Deep Reinforcement Learning

Due to the complexity of the environment and the dynamic change of the target, the relationship between the target and the missile is diversified, with randomness, fuzziness and uncertainty. In order to improve the timeliness of command decisions and the accuracy of interception strategies, a deep reinforcement learning model is constructed to optimize the decision loss function, obtain the optimal target allocation results, and achieve effective coordination of strike firepower. The simulation results show that it is feasible to apply the deep reinforcement learning method to cooperative strike target allocation decision.