ModGNN: モジュラーグラフニューラルネットワークによるマルチエージェント系の専門家方策近似
ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture
GCNを一般化した分散型フレームワークModGNNを提案し、マルチエージェントの群れ行動で既存手法より高い性能と未知環境への汎化を示した。
著者: Ryan Kortvelesy, Amanda Prorok
分類: cs.LG, cs.MA, cs.RO
原文アブストラクト
Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn complex coordination strategies. However, most current approaches use minor variants of a Graph Convolutional Network (GCN), which applies a convolution to the communication graph formed by the multi-agent system. In this paper, we investigate whether the performance and generalization of GCNs can be improved upon. We introduce ModGNN, a decentralized framework which serves as a generalization of GCNs, providing more flexibility. To test our hypothesis, we evaluate an implementation of ModGNN against several baselines in the multi-agent flocking problem. We perform an ablation analysis to show that the most important component of our framework is one that does not exist in a GCN. By varying the number of agents, we also demonstrate that an application-agnostic implementation of ModGNN possesses an improved ability to generalize to new environments.
