Abstract: Driven by the rapid progress of artificial intelligence technology,multi-agent systems have shown their potential for cooperative navigation in many practical applications,such as environmental monitoring,disaster relief,and autonomous driving.These tasks can generally be summarized as the multi-agent cooperative navigation problem.However,with the increase of the number of agents involved in the task,the expansion of reinforcement learning in multi-agent systems faces problems such as inefficiency and learning inertia,which seriously restrict the performance of task execution.This paper proposes an innovative multi-agent reinforcement learning framework.The framework speeds up the learning process by building a two-tier strategy network that enables agents to consider their peers’ strategies in a partially observable environment.In addition,a dynamic reward mechanism is introduced to solve the problem of poor cooperative navigation.The experimental results show that this deep reinforcement learning model based on two-layer strategy network can significantly improve the cooperation efficiency in multi-agent cooperative navigation tasks,especially in the case of a large number of agents,its advantages are more obvious.

Key words: Multi-agent, Joint navigation, Deep reinforcement learning