Decomposing shared networks for separate cooperation with multi-agent reinforcement learning

Sharing network parameters between agents is an essential and typical operation to improve the scalability of multi-agent reinforcement learning algorithms. However, agents with different tasks sharing the same network parameters are not conducive to distinguishing the agents' skills. In addition, the importance of communication between agents undertaking the same task is much higher than that with external agents. Therefore, we propose Dual Cooperation Networks (DCN). In order to distinguish whether agents undertake the same task, all agents are grouped according to their status through the graph neural network instead of the traditional proximity. The agent communicates within the group to achieve strong cooperation. After that, the global value function is decomposed by groups to facilitate cooperation between groups. Finally, we have verified it in simulation and physical hardware, and the algorithm has achieved excellent performance.