Abstract: In the intelligent era,the battle for land battlefield expands from planar land control to vertical land control.UAV anti-tank operation plays a crucial role in the battle for land control in future intelligent war.Deep reinforcement learning method in complex problem solving are faced with problems such as decision space explosion and sparse reward,this paper puts forward a dynamic multi-agent curriculum learning method based on VDN,the curriculum learning method is added into the training process of multi-agent deep reinforcement learning in this method,and combined with Stein variational gradient descent algorithm to improve the curriculum learning process.The problems of poor initial training effect,long training time and difficult convergence of reinforcement learning in complex tasks are solved.In addition,the curriculum learning model is constructed in the multi-agent particle environment and UAV anti-tank combat scene respectively,and the transfer of the model and training prior knowledge from easy to difficult is realized.Experimental results show that the curriculum learning DyMA-CL mechanism can improve the reinforcement learning training process,and the reinforcement learning agent can obtain better initial training effect,model convergence speed and final effect when conducting difficult task learning.

Key words: Deep reinforcement learning, Curriculum learning, Stein variational gradient descent, Unmanned aerial vehicle, Anti-tank