| Summary: | Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.
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