Multi-platform collaborative firepower allocation method based on task decomposition and reinforcement learning
In order to effectively solve the multi-platform collaborative fire allocation problem,this paper decomposes complex decision-making tasks according to the divide conquer frame and task decomposition technology.The paper proposes a novel combination approach of a heuristic algorithm and reinforcement learning(HARL),and carries out simulation experiments on the background of multi-fire platform joint attack.The sub-target platform allocation layer will select the platforms that are most suitable for attacking the current sub-target based on the reinforcement learning model,and the sub-platform fire allocation layer plans the optimal fire allocation plan for the platform executing the attack task based on the heuristic algorithm.Experimental results of simulation examples show that the reinforcement learning algorithm that combines heuristic operators outperforms traditional reinforcement learning algorithms by less than 15％,and improves the solving time by 20％compared with the classical heuristic algorithm.The research results may provide powerful technology support to solve more complex decision problems in the future.
multi-platform collaborative fire allocationreinforcement learningtask decompositioniterative optimization
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