In this paper, a cooperative search method for multiple UAVs is proposed to solve the problem of low efficiency of multi-UAV task execution by using a cooperative game with incomplete information. To improve search efficiency, CBBA (Consensus-Based Bundle Algorithm) is applied to designate the tasks area for each UAV. Then, Independent Deep Reinforcement Learning (IDRL) is used to solve Nash equilibrium to improve UAVs' collaborations. The proposed reward function is smartly developed to guide UAVs to fly along the path with higher reward value while avoiding the collisions between UAVs during flights. Finally, extensive experiments are carried out to compare our proposed method with other algorithms. Simulation results show that the proposed method can obtain more rewards in the same period of time as other algorithms.
Keywords: deep reinforcement learning; multi-UAV; task assignment.