Learning to Coordinate via Multiple Graph Neural Networks
This addresses coordination challenges in multi-agent reinforcement learning, but appears incremental as it builds on existing graph network and value-decomposition techniques.
The paper tackles the credit assignment problem in multi-agent systems by introducing MGAN, a new algorithm that combines graph convolutional networks and value-decomposition methods, achieving improved interpretability through visualization of agent representations.
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.