Learning to Coordinate via Multiple Graph Neural Networks
arXiv:2104.03503
Abstract
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.
arXiv comment12 pages, 4 figures, 2 tables
References in corpus (5)
- Representation Learning on Graphs with Jumping Knowledge Networks
- Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
- Counterfactual Multi-Agent Policy Gradients
- Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
- QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning