The collaboration between agents has gradually become an important topic in multi-agent systems. The key is how to efficiently solve the credit assignment problems. This paper introduces MGAN for collaborative multi-agent reinforcement learning, a new algorithm that combines graph convolutional networks and value-decomposition methods. MGAN learns the representation of agents from different perspectives through multiple graph networks, and realizes the proper allocation of attention between all agents. We show the amazing ability of the graph network in representation learning by visualizing the output of the graph network, and therefore improve interpretability for the actions of each agent in the multi-agent system.
翻译:代理商之间的合作逐渐成为多试剂系统的一个重要议题。关键在于如何有效解决信用分配问题。本文件介绍MGAN, 用于合作性多剂强化学习,这是一种将图形变幻网络和价值分解方法相结合的新算法。 MGAN通过多个图形网络从不同角度学习代理商的表述,并实现所有代理商之间的适当关注分配。我们通过直观图图网络的产出来显示图形网络在代表性学习方面的惊人能力,从而改进多剂系统中每个代理商行动的可解释性。