Task Allocation of Multiple Unmanned Aerial Vehicles Based on Deep Transfer Reinforcement Learning

Abstract

With the development of UAV technology, the task allocation problem of multiple UAVs is remarkable, but most of these existing heuristic methods are easy to fall into the problem of local optimization. In view of this limitation, deep transfer reinforcement learning is applied to the task allocation problem of multiple unmanned aerial vehicles, which provides a new idea about solving this kind of problem. The deep migration reinforcement learning algorithm based on QMIX is designed. The algorithm first compares the target task with the source task in the strategy base to find the task with the highest similarity, and then migrates the network parameters obtained from the source task after training, stored in the strategy base, so as to accelerate the convergence of the QMIX algorithm. Simulation results show that the proposed algorithm is significantly better than the traditional heuristic method of allocation in terms of efficiency and has the same running time.